Indonesian Journal of Data and Science
type: channel
description: Indonesian Journal of Data and Science (IJODAS) is an electronic journal published by Yocto Brain. The journal publishes peer-reviewed original research articles in Data Science, Data Mining, Data Representation, and Applied Statistics. IJODAS is issued three times a year (March, July, and December). All manuscripts must be written in English and follow the journal’s guidelines and template requirements. IJODAS is indexed in SINTA 3 and reputable indexing databases. The journal is registered with the National Library of Indonesia and holds the ISSN 2715-9930. truk tangki https://isnujombang.org/ https://sushiteigroup.org/ https://kinleybistro.org/ https://ojs.ejournalunigoro.org/ https://nanyangroastery.com/ https://bistronomix.org/ https://palmbeachseafood.org/ https://ktbfuso.org/ https://tbpnickel.org/ https://chandra-asri.org/ https://akashainternational.org/ https://www.astra-daihatsu.org/ https://fksfs.com/ situs gacor https://pmb.sttw.ac.id/ https://procournal.indonesiafocus.net/
publisher: yocto brain
language: en
publication name: Indonesian Journal of Data and Science
issn: 2715-9930
copyright: Authors retain copyright and full publishing rights to their articles. Upon acceptance, authors grant Indonesian Journal of Data and Science a non-exclusive license to publish the work and to identify itself as the original publisher. Self-archiving. Authors may deposit the submitted version, accepted manuscript, and version of record in institutional or subject repositories, with citation to the published article and a link to the version of record on the journal website. Commercial permissions. Uses intended for commercial advantage or monetary compensation are not permitted under CC BY-NC 4.0. For permissions, contact the editorial office at ijodas.journal@gmail.com. Legacy notice. Some earlier PDFs may display “Copyright © [Journal Name]” or only a CC BY-NC logo without the full license text. To ensure clarity, the authors maintain copyright, and all articles are distributed under CC BY-NC 4.0. Where any discrepancy exists, this policy and the article landing-page license statement prevail.
items: Drug Recommendation Using Multilabel Classification with Decision Tree Based on Patient Complaints and Diagnoses
items: The Effect of Clinical Rule-Based Domain Filtering on the Performance of FP-Growth-Based Drug Recommendation Systems
items: Implementation of an AI Agent Chatbot with a Dynamic Knowledge Base from Google Drive for Journal Information Service
items: Comparison of Naïve Bayes and SVM in Sentiment Analysis of ChatGPT for Learning on X and YouTube
items: Kodály Hand Sign Recognition from Hand Landmarks Using XGBoost
items: Comparing Sentiment Labeling with RoBERTa and IndoBERTweet on Public Opinion of Program Makan Bergizi Gratis
items: Deep Learning-Based Blood Cell Image Classification Using ResNet18 Architecture
items: Sentiment Analysis of Student Comments on Facilities and Infrastructure at Instiki Using Retrieval Augmented Generation
items: Sales Forecasting Analysis Using Fuzzy Time Series and Simple Linear Regression Methods at Toko Ari
items: Medium Range Meteorological Drought Prediction Based on SPEI-3 Using Ensemble Machine Learning and Deep Learning in North West Province, South Africa
items: Sentiment Analysis of BRImo Reviews on Google Play Store Using SVM and KNN
items: Sentiment Analysis of Public Opinion on Pi Network on Reddit Using FinBERT
items: Smart Waste Bin Prototype for University Waste Management
items: https://jurnal.yoctobrain.org/index.php/ijodas/article/view/366
items: Transfer Learning with VGG-16 for Image Classification of Endemic Papuan Orchids
items: Comparative Analysis of Speech-to-Text APIs for Supporting Communication of the Deaf Community
items: Application Of K-Means Clustering Algorithm to Identify the Best-Selling Digital Printing Services
items: Indonesian Cross-Platform Sentiment Analysis: DANN Transfer from General Applications to TradingView
items: Integrating Clustering Models and RCA to Identify Emerging Textile Export Destinations for Indonesia
items: Application of the DeepSurv Model to Predict Survival in Patients with Kidney Failure Undergoing Hemodialysis
items: Automated Waste Image Classification with Weighted Scoring Using MobileNetV2 on the OLSAM Platform
items: Sentiment Classification and Influential Actor Detection on Twitter (Case Study: The Raja Ampat Mining Conflict)
items: A Hybrid Convolutional Neural Network and Bidirectional LSTM Architecture for Multi-Sector Export Forecasting: A Macroeconomic Time Series Analysis of Indonesia
items: A Website-Based Management Information System For Pratama Sidhi SAI Clinic
items: Artificial Intelligence (AI) using Long Short-Term Memory (LSTM) for Sales Prediction in Campus Minimarkets
items: Sarcasm and Irony Detection in Lazada App Reviews Using IndoBERT
items: Vehicle Detection Using YOLOv8 on Low-Resolution Images
items: Zero-Shot Sentiment Analysis Of DeepSeek AI App Reviews Using DeepSeek-R1
items: Classification of Cavendish Banana Ripeness With CNN Method
items: Hybrid CNN-LSTM and Cox Model for Bipolar Risk Analysis Using Social Media Data

Drug Recommendation Using Multilabel Classification with Decision Tree Based on Patient Complaints and Diagnoses
type: item
description: This study develops a drug recommendation system using multilabel classification with the Decision Tree algorithm based on patient complaint and diagnosis data from electronic medical records. The dataset consists of patient visit records from a community health center in Pangkajene and Kepulauan Regency and is transformed using multi-hot encoding. Model performance is evaluated under three dataset scenarios (N=500, N=800, and N=1000) using multilabel metrics, including Micro-F1, Samples-F1, Hamming Loss, Jaccard Similarity, Hit@5, Precision@K, and Recall@K. The best Decision Tree model achieved a Micro-F1 score of 0.292, Samples-F1 of 0.281, and Hit@5 of 0.690 on the N=1000 dataset scenario. Bootstrap validation with 1000 iterations indicates relatively stable performance, with narrow confidence intervals across evaluation metrics. These results show that the multilabel Decision Tree model is capable of capturing relationships between patient complaints, diagnoses, and drug therapies while maintaining an interpretable decision structure
subject: Data Science
  topic: section
subject: Multilabel Classification
  topic: keywords
subject: Decision Tree
  topic: keywords
subject: Drug Recommendation System
  topic: keywords
subject: Electronic Medical Records
  topic: keywords
subject: Clinical Decision Support
  topic: keywords
creator: Muh Aristsyah Malik
creator: Harlinda
creator: Herdianti Darwis
rights: Copyright (c) 2026 Muh Aristsyah Malik, Harlinda, Herdianti Darwis https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 145
ending page: 159
doi: 10.56705/ijodas.v7i1.397

The Effect of Clinical Rule-Based Domain Filtering on the Performance of FP-Growth-Based Drug Recommendation Systems
type: item
description: This study analyzes the effect of domain filtering on drug recommendation systems based on association rule mining using the FP-Growth algorithm with Neural Collaborative Filtering (NCF) as a comparison. The dataset used was derived from patient medical records containing attributes such as complaints, diagnoses, and drug therapies, with a total of 1,000 patient transactions. To avoid data leakage, the dataset was randomly divided into 70% training data and 30% test data before the modeling process was carried out. Domain filtering was applied by limiting the rule structure so that complaints and diagnoses acted as antecedents and drugs as consequents. The performance of the recommendation system was evaluated using the Precision@5, Recall@5, and Normalized Discounted Cumulative Gain (NDCG@5) metrics. The results of the experiment show that the FP-Growth approach with domain filtering produces higher Precision@5 and NDCG@5 values than the non-filtering approach. The Wilcoxon Signed-Rank test shows that the difference is statistically significant, while effect size analysis using Cliff's Delta shows a practically meaningful impact. Furthermore, a comparison with Neural Collaborative Filtering shows that the collaborative filtering-based approach is less effective on transactional clinical prescription data with limited historical interactions. These findings indicate that integrating medical domain knowledge into FP-Growth can improve the clinical relevance and quality of drug recommendation rankings
subject: Data Science
  topic: section
subject: Association Rule Mining
  topic: keywords
subject: FP-Growth
  topic: keywords
subject: Domain Filtering
  topic: keywords
subject: Drug Recommendation System
  topic: keywords
subject: Medical Data
  topic: keywords
creator: Muhammad Zaqly Luluang
creator: Irawati
creator: Herdianti Darwis
rights: Copyright (c) 2026 Muhammad Zaqly Luluang, Irawati, Herdianti Darwis https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 95
ending page: 107
doi: 10.56705/ijodas.v7i1.398

Implementation of an AI Agent Chatbot with a Dynamic Knowledge Base from Google Drive for Journal Information Service
type: item
description: This study presents the implementation of an AI agent chatbot to support journal information services on the Open Journal Systems (OJS) platform and the Telegram messaging application using a dynamic knowledge base sourced from Google Drive. The chatbot provides automated responses to user inquiries related to journal scope, publication fees, submission procedures, and review timelines, while allowing journal administrators to update information content without modifying the system. Functional testing results indicate that the chatbot delivers accurate and consistent information with acceptable response times across both platforms. The implementation demonstrates that integrating an AI agent chatbot with a dynamic knowledge base can enhance information accessibility, reduce administrative workload, and improve service efficiency in academic journal management
subject: Data Science
  topic: section
subject: AI agent chatbot
  topic: keywords
subject: Open Journal Systems
  topic: keywords
subject: Dynamic Knowledge Base
  topic: keywords
subject: Journal Information Services
  topic: keywords
subject: Telegram
  topic: keywords
creator: Huzain Azis
creator: Randika sakha Ramadani Putra Ahmad
creator: Irawati Syahriawan
rights: Copyright (c) 2026 Huzain Azis, Randika sakha Ramadani Putra Ahmad, Irawati Syahriawan https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 61
ending page: 68
doi: 10.56705/ijodas.v7i1.384

Comparison of Naïve Bayes and SVM in Sentiment Analysis of ChatGPT for Learning on X and YouTube
type: item
description: The rapid development of artificial intelligence technology has encouraged users to actively express opinions on social media platforms such as X and YouTube, including discussions on the use of ChatGPT as a learning support tool. This study aims to analyze public sentiment toward the use of ChatGPT in learning contexts by comparing the performance of the Naïve Bayes and Support Vector Machine (SVM) classification methods. A total of 5,500 comments from platform X and 5,543 comments from YouTube were collected through a crawling process using relevant keywords during the period from January 2023 to December 2025. The data were preprocessed and labeled into three sentiment classes (positive, negative, and neutral) using a lexicon-based approach with the INSET Lexicon. Feature extraction was conducted using the Term Frequency–Inverse Document Frequency (TF-IDF) method, and the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results show that the SVM classifier consistently outperformed the Naïve Bayes method on both platforms. On platform X, SVM achieved an accuracy of 76.67%, while Naïve Bayes obtained 74.60%. On YouTube, SVM achieved an accuracy of 73.10%, significantly higher than Naïve Bayes at 62.04%. These findings indicate that SVM is more effective for sentiment analysis of social media data related to the use of ChatGPT in learning environments
subject: Data Science
  topic: section
subject: Naive Bayes
  topic: keywords
subject: Support Vector Machine(SVM)
  topic: keywords
subject: Social Media
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: Chat GPT
  topic: keywords
creator: Ni Putu Eka Swari
creator: Ni Wayan Jeri Kusuma Dewi
creator: Ni Ketut Utami Nilawati
creator: Aniek Suryanti Kusuma
creator: Ni Luh Wiwik Sri Rahayu Ginantra
rights: Copyright (c) 2026 Ni Putu Eka Swari, Ni Wayan Jeri Kusuma Dewi, Ni Ketut Utami Nilawati, Aniek Suryanti Kusuma, Ni Luh Wiwik Sri Rahayu Ginantra https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 69
ending page: 81
doi: 10.56705/ijodas.v7i1.382

Kodály Hand Sign Recognition from Hand Landmarks Using XGBoost
type: item
description: Introduction: Angklung is a traditional Indonesian musical instrument that continues to evolve through digital technology. However, computer vision–based gesture recognition for controlling physical angklung instruments remains limited. This study investigates landmark-based recognition of Kodály hand signs and evaluates its application for real-time angklung interaction. Method: Hand landmarks were extracted using MediaPipe Hands from RGB camera input. Each gesture was represented by 63 normalized numerical features derived from 21 landmarks. The dataset consists of 8,000 images representing eight Kodály gesture classes (Do–Do'). Gesture classification was performed using the Extreme Gradient Boosting (XGBoost) algorithm. Model evaluation applied a subject-independent two-fold scheme using accuracy, precision, recall, F1-score, and confusion matrix analysis. Real-time system trials were conducted under different lighting conditions and capture distances, and TCP communication with an ESP32 controller was evaluated. Results: The model achieved 96.63% accuracy in Fold 1 and 96.40% in Fold 2. Misclassifications were mainly observed between visually similar gestures, particularly La and Mi. Separate real-time system trials showed consistent recognition under bright lighting, while accuracy decreased under dim lighting, especially for Do (90%) and Mi (86.7%). Gesture recognition remained reliable up to approximately 1.5 m. TCP testing over 200 command events recorded 0% failed acknowledgments with a mean round-trip time of 87.36 ms. Conclusion: These indicate that landmark-based Kodály gesture classification using MediaPipe Hands and XGBoost can support real-time angklung interaction under controlled conditions, although improvements are needed for low-light environments and visually similar gestures
subject: Data Science
  topic: section
subject: Angklung
  topic: keywords
subject: Gesture Recognition
  topic: keywords
subject: XGBoost
  topic: keywords
subject: MediaPipe Hands
  topic: keywords
subject: Kodály Hand Sign
  topic: keywords
creator: Achmad Zulfikar
creator: Farniwati Fattah
creator: Andi Widya Mufila Gaffar Gaffar
rights: Copyright (c) 2026 Achmad Zulfikar, Farniwati Fattah, Andi Widya Mufila Gaffar https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 82
ending page: 94
doi: 10.56705/ijodas.v7i1.391

Comparing Sentiment Labeling with RoBERTa and IndoBERTweet on Public Opinion of Program Makan Bergizi Gratis
type: item
description: The Program Makan Bergizi Gratis (MBG) is a flagship program of the Prabowo Subianto administration launched in 2024, triggering diverse public responses on social media. Sentiment analysis using deep learning models offers an effective approach to understanding public opinion at scale. However, selecting the appropriate model for Indonesian social media text remains challenging. This study aims to compare the performance of two pretrained transformer models, RoBERTa Base and IndoBERTweet Base, in conducting automatic sentiment labeling on Indonesian tweets related to the MBG program using a zero-shot labeling approach without human-annotated ground truth. A total of 1,831 tweets were collected from platform X and preprocessed using case folding, normalization, and stopword removal. Both models were applied in parallel to label each tweet with sentiment categories (positive, neutral, negative) along with confidence scores. The comparison was evaluated using agreement rate, Cohen's Kappa, and confidence score analysis. RoBERTa Base exhibits a conservative tendency with 75.20% neutral labels, while IndoBERTweet Base produces a more balanced distribution (68.16% neutral). The comparison shows 77.28% agreement with Cohen's Kappa of 0.490 (Moderate Agreement). RoBERTa Base achieves higher confidence (mean: 0.9559, 83.01% above 0.95) compared to IndoBERTweet Base (mean: 0.9236, 68.65% above 0.95). IndoBERTweet Base is more effective in detecting negative sentiment, identifying nearly twice as many negative tweets (13.54% vs. 7.48%). This study recommends IndoBERTweet Base for exploratory research requiring sensitive sentiment detection and RoBERTa Base for precision-critical applications. An ensemble approach combining both models is recommended for production-critical applications
subject: Data Science
  topic: section
subject: Makan Bergizi Gratis (MBG) Program
  topic: keywords
subject: IndoBERTweet
  topic: keywords
subject: Public Opinion, RoBERTa
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: Transformer
  topic: keywords
creator: Putri Nur Rezky
creator: Dolly Indra
creator: Herdianti
rights: Copyright (c) 2026 Putri Nur Rezky, Dolly Indra, Herdianti https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 108
ending page: 121
doi: 10.56705/ijodas.v7i1.381

Deep Learning-Based Blood Cell Image Classification Using ResNet18 Architecture
type: item
description: The classification of white blood cells (WBC) plays a critical role in haematological diagnostics, yet manual examination remains a labour-intensive and subjective process. In response to this challenge, this study investigates the application of deep learning, specifically the ResNet18 convolutional neural network architecture, for the automated classification of blood cell images into four classes: eosinophils, lymphocytes, monocytes, and neutrophils. The dataset used comprises microscopic images annotated by cell type and is divided into training and validation sets with an 80:20 ratio. Standard pre-processing techniques such as image normalization and augmentation were applied to enhance model robustness and generalization. The model was fine-tuned using transfer learning with pre-trained weights from ImageNet and optimized using the Adam optimizer. Performance was evaluated through a comprehensive set of metrics including accuracy, precision, recall, F1-score, mean squared error (MSE), and root mean squared error (RMSE). The best model achieved a validation accuracy of 86.89%, with macro-averaged precision, recall, and F1-score of 0.8738, 0.8690, and 0.8688, respectively. Lymphocyte classification yielded the highest F1-score (0.9515), while eosinophils posed the greatest classification challenge, as evidenced by lower precision and higher misclassification rates in the confusion matrix. Error-based evaluation further supported the model’s consistency, with an MSE of 0.7125 and RMSE of 0.8441. These results confirm that ResNet18 is capable of learning discriminative features in complex haematological imagery, providing an efficient and reliable alternative to manual analysis. The findings suggest potential for practical implementation in clinical workflows and pave the way for further research involving multi-model ensembles or cell segmentation pre-processing for improved precision
subject: Data Science
  topic: section
subject: Blood Cell Classification
  topic: keywords
subject: ResNet18
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Medical Imaging
  topic: keywords
subject: Multi-class Evaluation
  topic: keywords
creator: Thomas Edyson Tarigan
creator: Agung Budi Prasetyo
creator: Emy Susanti
rights: Copyright (c)
date: 2025-12-02
publication date: 2025-12-02
starting page: 294
ending page: 300
doi: 10.56705/ijodas.v6i2.300

Sentiment Analysis of Student Comments on Facilities and Infrastructure at Instiki Using Retrieval Augmented Generation
type: item
description: This research was conducted to analyze the sentiment of student comments on infrastructure facilities at the Indonesian Institute of Business and Technology (INSTIKI) to overcome the problem of comment analysis that was previously done manually. The data used is in the form of student comments in 2024. The method used in this study is Retrieval Augmented Generation (RAG) with data labeling using Lexicon-Based. The test was carried out on three Large Language Models (LLMs), namely indobenchmark/indobert-base-p1, TinyLlama/TinyLlama-1.1B-Chat-v1.0, and w11wo/indonesian-roberta-base-sentiment-classifier. The test results showed that the indobenchmark/indobert-base-p1 model produced the highest accuracy of 80% in both test sessions compared to other models. The TinyLlama/TinyLlama-1.1B-Chat-v1.0 model produced 60% accuracy in session 1 and 65% in session 2, while the w11wo/indonesian-roberta-base-sentiment-classifier model produced 60% accuracy in both test sessions. The difference in the performance of these three LLMs shows that the model's understanding of Indonesian can affect the results of sentiment predictions.
subject: Data Science
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: Facilities and Infrastructure
  topic: keywords
subject: Retrieval Augmented Generation (RAG)
  topic: keywords
subject: Lexicon-Based
  topic: keywords
subject: Student Comments
  topic: keywords
creator: Ni Putu Juliana Dewi
creator: I Kadek Dwi Gandika Supartha
creator: I Putu Yoga Indrawan
creator: Ketut Jaya Atmaja
rights: Copyright (c) 2025 Ni Putu Juliana Dewi, I Kadek Dwi Gandika Supartha, I Putu Yoga Indrawan, Ketut Jaya Atmaja https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 575
ending page: 587
doi: 10.56705/ijodas.v6i3.377

Sales Forecasting Analysis Using Fuzzy Time Series and Simple Linear Regression Methods at Toko Ari
type: item
description: Introduction: Forecasting, often referred to as prediction, can actually help assess conditions or predict future sales. In the business world forecasting is crucial because it can help companies plan their future operations especially when faced with sudden increases and decreases in sales and stockpiles. Especially in retail forecasting is extremely helpful in purchasing merchandise, managing inventory in the warehouse, and reducing losses due to changing customer preferences. Ari's shop, located on Jalan Raya Samu, Singapadu Kaler, Gianyar, Bali, also experiences increases and decreases in monthly sales. Therefore, it is hoped that this sales forecasting can help maintain more stable and smooth operations. Methods: This study used two methods to forecast sales: Fuzzy Time Series (FTS) and Simple Linear Regression (SLR), to predict figures from Ari's shop's monthly sales data. Both methods use the same dataset, which is Ari's Store sales data for 13 months, from January 2024 to January 2025. The forecast results are then compared using the Mean Absolute Percentage Error (MAPE), which measures the model's accuracy in predicting results. Results: Based on the sales forecasts performed, both models produced fairly accurate predictions due to their low MAPE values, below 10%. Of the two methods, Simple Linear Regression provided more accurate results with a MAPE of 3.57%. Meanwhile, the Fuzzy Time Series method produced a MAPE of 5.53%. This difference in values indicates that the linear regression model is more appropriate for Ari's Store sales data, especially since the data pattern tends to follow a linear trend.
subject: Data Science
  topic: section
subject: Sales Forecasting
  topic: keywords
subject: Fuzzy Time Series
  topic: keywords
subject: Simple Linear Regression
  topic: keywords
subject: MAPE
  topic: keywords
subject: Retail Business
  topic: keywords
creator: Ni Luh Sri April Yanti
creator: Ni Wayan Jeri Kusuma Dewi
creator: I Gede Made Yudi Antara
creator: Desak Made Dwi Utami Putra
creator: Putu Wirayudi Aditama
rights: Copyright (c) 2025 Ni Luh Sri April Yanti, Ni Wayan Jeri Kusuma Dewi, I Gede Made Yudi Antara, Desak Made Dwi Utami Putra, Putu Wirayudi Aditama https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 473
ending page: 480
doi: 10.56705/ijodas.v6i3.368

Medium Range Meteorological Drought Prediction Based on SPEI-3 Using Ensemble Machine Learning and Deep Learning in North West Province, South Africa
type: item
description: Meteorological drought monitoring is a pivotal action in everyday humankinds’ activities around the globe. It evaluates atmospheric conditions using weather observation instruments to measure atmospheric variables. Due to the highly sophisticated atmospheric environment, errors in drought monitoring and uncertain observation have been observed. Therefore, this research paper develops a lightweight Machine Learning (ML) and Deep Learning (DL) framework to forecast medium term meteorological drought in North West, South Africa using Standardized Precipitation Evapotranspiration Index at 3 -months (SPEI-3) timescale. This time scale reflects moisture deficits directly impacting agricultural production, early warning decisions and water management. The dataset used in this research study was obtained from South African Weather Services through a formal data request submission and not publicly accessible over a period of 10 years. Furthermore, the dataset consists of 20085 data entries and 11 data columns collected from 10 weather stations. The proposed models include SVR-RF, and, CNN-LSTM-ANN, compared to benchmark models, such as SVR, RF, CNN, LSTM, ANN, CNN-LSTM evaluated using statistical metrics, such as MSE, MAE, and . The results demonstrated irregular drought patterns during the defined period with SPEI-3 values clustered below normal conditions. Similarly, validation results showed that SVR demonstrated strong predictive performance with competitive MSE of 0.28, low MAE of 0.34 and of 0.86. Although, the proposed CNN-LSTM-ANN and SVR-RF models did not exhibit competitive performance compared to benchmarking models, the result provides valuable comprehension, data collection, distribution, architecture, and computational power
subject: Data Science
  topic: section
subject: Ensemble Machine Learning
  topic: keywords
subject: Meteorological Drought Prediction
  topic: keywords
subject: NWP
  topic: keywords
subject: SPEI
  topic: keywords
creator: Reatlegile Phiri
creator: Bukohwo Michael Esiefarienrhe
creator: Ibidun Christiana Obagbuwa
rights: Copyright (c) 2025 Reatlegile Phiri, Bukohwo Michael Esiefarienrhe , Ibidun Christiana Obagbuwa https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 393
ending page: 411
doi: 10.56705/ijodas.v6i3.354

Sentiment Analysis of BRImo Reviews on Google Play Store Using SVM and KNN
type: item
description: The rapid growth of digital banking has increased user interaction through mobile banking apps such as BRImo (Bank Rakyat Indonesia). Google Play Store reviews provide valuable insight into app quality, but their unstructured format makes manual analysis inefficient. This study analyzes user sentiment toward BRImo and compares the performance of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) for sentiment classification. Reviews were collected using Google Play Scraper from May 2024 to May 2025, yielding 15,945 raw reviews. After cleaning (removing duplicates, symbols, links, emojis) and language filtering, 15,233 valid reviews remained. Sentiment labels were generated using two lexicon-based methods: INSET and VADER. Using INSET, the data consisted of 6,238 positive, 4,987 negative, and 4,383 neutral reviews, producing 11,225 reviews for modeling. Using VADER, 10,496 positive, 2,903 negative, and 1,834 neutral reviews were obtained, totaling 13,399 reviews. Datasets were split into 80% training and 20% testing with stratified sampling. Features were extracted using TF-IDF unigrams. Classification was performed using linear SVM and KNN, with the optimal K=3 selected via Grid Search. Models were evaluated using 5-fold cross-validation, reporting mean accuracy, precision, recall, and F1-score (macro-average for INSET; weighted-average for VADER due to class imbalance). Results show SVM consistently outperforms KNN, achieving 98.36% mean accuracy and 98.34% mean F1-score on INSET, and 95.59% mean accuracy and 95.56% mean F1-score on VADER. Overall, BRImo user sentiment is predominantly positive, and findings can guide developers in improving app stability and quality
subject: Data Science
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: BRImo
  topic: keywords
subject: Application Performance
  topic: keywords
subject: Reviews Google Play Store
  topic: keywords
subject: SVM
  topic: keywords
subject: KNN
  topic: keywords
creator: Olivia Sutriani Jelni
creator: Made Leo Radhitya
creator: Gede Wirya Wardhana
creator: Ni Wayan Jeri Kusuma
creator: Ni Made Mila Rosa Desmayani
rights: Copyright (c) 2025 Olivia Sutriani Jelni, Made Leo Radhitya, Gede Wirya Wardhana, Dewi, Ni Made Mila Rosa Desmayani https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 548
ending page: 562
doi: 10.56705/ijodas.v6i3.365

Sentiment Analysis of Public Opinion on Pi Network on Reddit Using FinBERT
type: item
description: The rapid growth of blockchain technology has led to the emergence of new cryptocurrencies, including Pi Network, which emphasizes accessibility through mobile-based mining. This study aims to answer the research question of whether FinBERT, a financial domain-specific transformer model, can effectively classify public sentiment in informal Reddit discussions related to Pi Network. FinBERT was first evaluated on a labeled financial sentiment dataset to assess its performance in a structured financial context before being applied to Reddit data. Model performance was measured using accuracy, precision, recall, and F1-score. After validation, the model was used to analyze one thousand twenty Reddit comments discussing Pi Network. Text preprocessing included cleaning, case folding, tokenization, stopword removal, stemming, and sequence standardization. The evaluation results show that FinBERT achieved an accuracy of eighty-five point ninety-eight percent on the financial validation dataset, with strong precision and recall across sentiment classes. When applied to Reddit comments, neutral sentiment was the most dominant, followed by positive and negative sentiments. Pi Network was selected as the case study because, unlike more established cryptocurrencies, it is still in an early stage of development and relies heavily on community participation, making public opinion particularly important for understanding its adoption and credibility
subject: Data Science
  topic: section
subject: FinBERT
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: Reddit
  topic: keywords
subject: Cryptocurrency
  topic: keywords
subject: Pi Network
  topic: keywords
creator: Sindy Indira Wiguna
creator: Adhitia Erfina
creator: Cecep Warman
rights: Copyright (c) 2025 Sindy Indira Wiguna, Adhitia Erfina, Cecep Warman https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 426
ending page: 432
doi: 10.56705/ijodas.v6i3.342

Smart Waste Bin Prototype for University Waste Management
type: item
description: Background: Waste mismanagement remains a critical issue in Indonesian campuses, where ineffective segregation and collection practices contribute to environmental pollution. Smart technologies offer opportunities to improve waste handling efficiency and monitoring in university environments. Methods: This study developed a smart waste bin prototype that integrates Internet of Things (IoT) sensors, machine learning–based image classification (MobileNetV2 with TensorFlow Lite), GPS tracking, and LoRa communication. The system was designed to classify three types of waste—plastic bottles, snack packaging, and cans—while enabling fill-level monitoring, automated sorting, and real-time location reporting. Results: Experimental results showed strong classification accuracy for plastic bottles (100%), but lower performance for snack packaging (53–80%) and cans (40–67%), especially in low-light conditions or with darker materials. The overall real-time testing accuracy reached 45.1%. LoRa communication provided long-range connectivity but was affected by electromagnetic interference, while GPS tracking was reliable in open areas but inconsistent indoors. Conclusions: The prototype demonstrates the feasibility of integrating AI and IoT for scalable campus waste management. Despite environmental and hardware limitations, it offers a modular framework that can be refined with improved lighting, EMI shielding, and enhanced datasets. This research contributes a practical model for smart campus initiatives and supports the adoption of sustainable waste management practices in higher education environments.
subject: Data Science
  topic: section
subject: Smart Waste Bin
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: LoRa Communication
  topic: keywords
subject: Waste -Classification
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Automated-Segregation
  topic: keywords
subject: GPS
  topic: keywords
creator: Fauzy Fathrurahman
creator: Dolly Indra
creator: Tasrif Hasanuddin
creator: Herdianti Darwis
creator: Tanaka Kazuaki
rights: Copyright (c) 2025 Fauzy Fathrurahman, Dolly Indra, Tasrif Hasanuddin, Herdianti Darwis, Tanaka Kazuaki https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 378
ending page: 392
doi: 10.56705/ijodas.v6i3.324

https://jurnal.yoctobrain.org/index.php/ijodas/article/view/366
type: item
title: Comparison of Naïve Bayes and Random Forest in Sentiment Analysis of State-Owned Banks Management by Danantara on X and YouTubeComparison of Naïve Bayes and Random Forest in Sentiment Analysis of State-Owned Banks Management by Danantara on X and YouTube
description: The advancement of digital technology has increased public engagement in expressing opinions and responding to issues on social media platforms such as X and YouTube. A prominent topic of recent public debate concerns Danantara's management of state-owned banks. This study analyzes public sentiment regarding this issue by comparing the performance of the Naïve Bayes and Random Forest classification methods. A dataset comprising 25,565 entries was collected from both platforms between January 2025 and May 2025. The data underwent text pre-processing, labeling with the InSet Lexicon, and feature weighting using term frequency-inverse document frequency (TF-IDF). The dataset was split at 80:20, and class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE) prior to classification. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results demonstrate that Random Forest performed stably, achieving 84% accuracy both before and after sampling. In contrast, Naïve Bayes achieved 74% accuracy before sampling, which increased to 79% after sampling. These findings suggest that Random Forest is more robust to data imbalance than Naïve Bayes, which is more susceptible to bias toward the majority class.
subject: Data Science
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: Naïve Bayes
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Danantara
  topic: keywords
subject: Stated-owned Bank
  topic: keywords
creator: Ni Wayan Indah Juliandewi
creator: Aniek Suryanti Kusuma
creator: Kompiang Martina Dinata Putri
creator: I Gusti Agung Indrawan
creator: I Gusti Ayu Agung Mas Aristamy
rights: Copyright (c) 2025 Ni Wayan Indah Juliandewi, Aniek Suryanti Kusuma, Kompiang Martina Dinata Putri, I Gusti Agung Indrawan, I Gusti Ayu Agung Mas Aristamy https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-28
publication date: 2025-12-28
starting page: 527
ending page: 537
doi: 10.56705/ijodas.v6i3.366

Transfer Learning with VGG-16 for Image Classification of Endemic Papuan Orchids
type: item
description: This study applies a transfer-learning approach using the VGG16 architecture to classify three Papuan endemic orchid species—Dendrobium spectabile, Dendrobium lineale, and Dendrobium mirbelianum. A total of 810 field-photographed images were collected, followed by preprocessing and data augmentation to enhance data diversity. The VGG16 model pretrained on ImageNet was used as a fixed feature extractor by freezing its convolutional layers and removing the fully connected layers, while a custom classification head was added to distinguish among the three species. Experimental results demonstrated a validation accuracy of 94.44% and a macro-average F1-score of 0.94, confirming the robustness of the model under limited-data conditions. These findings suggest that transfer learning using VGG16 can effectively support orchid species recognition and serve as a foundation for developing AI-based biodiversity monitoring and conservation systems in Indonesia
subject: Data Science
  topic: section
subject: Papuan endemic orchids
  topic: keywords
subject: Classification
  topic: keywords
subject: Convolutional Neural Network (CNN)
  topic: keywords
subject: Transfer Learning
  topic: keywords
subject: VGG16
  topic: keywords
creator: Iftinan Mardhiyyah
creator: Christian D. Suhendra
creator: Agustina Y. S. Arobaya
rights: Copyright (c) 2025 Iftinan Mardhiyyah, Christian D. Suhendra, Agustina Y. S. Arobaya https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 609
ending page: 617
doi: 10.56705/ijodas.v6i3.355

Comparative Analysis of Speech-to-Text APIs for Supporting Communication of the Deaf Community
type: item
description: Hearing impairment can have a profound impact on the mental and emotional state of sufferers, as well as hinder communication and delay in accessing information directly that relies on interpreters. Advances in assistive technology, especially speech recognition systems that are able to convert spoken language into written text (speech-to-text). However, its implementation faces various challenges related to the level of accuracy of each speech-to-text Application Programming Interface (API), thus requiring an appropriate deep learning model. This study serves to analyze and compare the performance of speech-to-text API services (Deepgram API, Google API and Whisper AI) based on Word Error Rate (WER) and Words Per Minute (WPM), to determine the most optimal API in a web-based real-time transcription system using the JavaScript programming language and Glitch.com. The three API services were tested by calculating their error rates and transcription speeds, then evaluated to see how low the error accuracy rate was and how high the transcription speed was. On average, Whisper AI had a WER of 0% across all word categories, but its speed was lower than the other two APIs. Deepgram API displayed the best balance between accuracy and speed, with an average WER of 13.78% and 67 WPM. Google API performed stably, but its WER value was slightly higher than Deepgram API. In conclusion, based on the results, Deepgram API was deemed the most optimal for live transcription, as it is capable of producing fast and error-free transcriptions, significantly increasing the accessibility of information for the deaf community.
subject: Data Science
  topic: section
subject: Speech-to-Text
  topic: keywords
subject: API
  topic: keywords
subject: Word Error Rate (WER)
  topic: keywords
subject: Word Per Minute (WPM)
  topic: keywords
subject: Deaf Community
  topic: keywords
creator: Anik Nur Handayani
creator: Hariyono Hariyono
creator: Ahmad Munjin Nasih
creator: Rochmawati Rochmawati
creator: Imanuel Hitipeuw
creator: Harits Ar Rosyid
creator: Jevri Tri Ardiansah
creator: Rafli Indar Praja
creator: Ahmad Nurdiansyah
creator: Desi Fatkhi Azizah
rights: Copyright (c) 2025 Anik Nur Handayani, Hariyono Hariyono, Ahmad Munjin Nasih, Rochmawati Rochmawati, Imanuel Hitipeuw, Harits Ar Rosyid, Jevri Tri Ardiansah, Rafli Indar Praja, Ahmad Nurdiansyah, Desi Fatkhi Azizah https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 492
ending page: 500
doi: 10.56705/ijodas.v6i3.327

Application Of K-Means Clustering Algorithm to Identify the Best-Selling Digital Printing Services
type: item
description: The digital printing industry in Indonesia is experiencing rapid growth thanks to the increasing demand from companies for printing services such as banners, stickers, brochures, and business cards. CV. Copy Paste is one of the companies operating in the digital printing industry that fulfills various printing orders every month. However, the company has difficulty identifying the most popular printing services, which makes it difficult to develop a targeted promotional strategy. In view of this problem, the aim of this study is to group digital printing services according to their popularity using the K-Means Clustering method. This study uses a quantitative approach, collecting sales data from the last 12 months, covering 160 types of services. The steps taken include preliminary data processing, namely attribute selection, data cleaning, and data transformation so that it can be effectively processed using the K-Means algorithm, implemented in the Python programming language. The test results show that digital printing services can be divided into three clusters: 115 less popular services (C1), 31 fairly popular services (C2), and 14 very popular services (C3). The results of this study provide information that can be used as a basis for strategic decisions regarding promotion and service management. In this way, the K-Means Clustering algorithm has proven effective in helping companies group products in a more objective and measurable way based on historical data.
subject: Data Science
  topic: section
subject: K-Means Clustering
  topic: keywords
subject: Data Mining
  topic: keywords
subject: Digital Printing
  topic: keywords
subject: Python
  topic: keywords
subject: Best-selling Services
  topic: keywords
creator: Ana Fatahali Ramadhan
creator: Sudin Saepudin
creator: Carti Irawan
creator: Mupaat
rights: Copyright (c) 2025 Ana Fatahali Ramadhan; Sudin Saepudin, Carti Irawan, Mupaat https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 356
ending page: 369
doi: 10.56705/ijodas.v6i3.316

Indonesian Cross-Platform Sentiment Analysis: DANN Transfer from General Applications to TradingView
type: item
description: Introduction: Cross-platform sentiment analysis for Indonesian language presents significant challenges when adapting models from general applications to specialized domains. Domain Adversarial Neural Networks (DANN) offer promising solutions for transfer learning, yet their effectiveness for Indonesian language remains largely unexplored, particularly under extreme class imbalance conditions common in trading platforms. Methods: This study investigates DANN effectiveness for transferring sentiment analysis knowledge from four strategically selected source domains to TradingView trading platform. The research utilizes 5,990 Indonesian reviews after preprocessing from an initial 6,000 samples, with source domains showing 66.5% positive sentiment while target domain exhibits 85.1% positive sentiment, creating an 18.7% distribution gap. Four experimental approaches were compared with statistical validation across multiple random initializations: Source-Only training, Multi-Domain training, Limited Target training, and DANN implementation. Results: DANN demonstrates stable cross-platform adaptation, achieving 87.77% ± 0.97% accuracy with consistent performance across initializations, outperforming Source-Only baseline (87.10% ± 0.84%) and Multi-Domain approach (86.98% ± 0.64%). While Limited Target baseline achieves higher accuracy (88.10% ± 2.23%), its high variance poses deployment risks. A-distance analysis reveals substantial domain gaps (193.00 ± 1.06), with DANN's adversarial training achieving modest domain separation reduction (72.90% ± 8.81% domain discrimination accuracy). Conclusions: This research contributes the first systematic evaluation of DANN for Indonesian cross-platform sentiment analysis, demonstrating that deployment consistency outweighs peak accuracy for production environments. The findings provide practical value for Indonesian fintech startups requiring robust sentiment analysis with limited labeled data. Future work should explore multi-targe…
subject: Data Science
  topic: section
subject: Domain adaptation; Indonesian sentiment analysis; Cross-platform transfer; DANN; Statistical validation.
  topic: keywords
creator: Muh. Rifqi Zulkifli
creator: Purnawansyah
creator: Herdianti Darwis
rights: Copyright (c) 2025 Muh. Rifqi Zulkifli, Purnawansyah, Herdianti Darwis https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 481
ending page: 491
doi: 10.56705/ijodas.v6i3.318

Integrating Clustering Models and RCA to Identify Emerging Textile Export Destinations for Indonesia
type: item
description: This research investigates the strategic identification of new export destinations for Indonesian textile products by integrating international market segmentation and product competitiveness analysis. The study employs clustering techniques (K-Means, K-Medoids, and Hierarchical) validated through Silhouette and Davies-Bouldin indices to classify 149 countries based on trade indicators (import growth, trade balance, global market share), economic indicators (population, purchasing power parity, industrial proportion to GDP), and trade barrier indicators (logistics performance index, geographic distance, free trade agreements). Complementarily, the Revealed Comparative Advantage (RCA) framework is applied to evaluate Indonesia’s product-level competitiveness in the global textile market. The results reveal that export opportunities are can be concentrated in 20 countries across Europe, Asia, Africa, the Caribbean, and Melanesia, characterized by positive import growth, significant trade deficits, large market capacities, and relatively low trade barriers. Moreover, Indonesia demonstrates high comparative advantages in artificial and synthetic fibbers, wigs, and leather footwear, while apparel products such as suits, shirts, knitwear, and brassieres represent moderately competitive but globally demanded items. The study concludes that Indonesia’s export strategy should balance high purchasing power markets and emerging economies with high import dependency.
subject: Data Science
  topic: section
subject: Clustering
  topic: keywords
subject: RCA
  topic: keywords
subject: International Market Segmentation
  topic: keywords
subject: Textile
  topic: keywords
subject: Export
  topic: keywords
creator: Muhammad Glenn Yunifer
creator: Samidi
rights: Copyright (c) 2025 Muhammad Glenn Yunifer, Samidi https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 501
ending page: 516
doi: 10.56705/ijodas.v6i3.337

Application of the DeepSurv Model to Predict Survival in Patients with Kidney Failure Undergoing Hemodialysis
type: item
description: This study aims to improve survival prediction in patients with kidney failure undergoing hemodialysis, given their high mortality risk. Traditional models such as Cox Proportional Hazards (Cox PH) have limitations in capturing complex and nonlinear relationships in clinical data. Therefore, this study applies DeepSurv, a deep learning–based survival model, and compares its performance with Cox PH and Cox PH Spline. A total of 300 patients were included, with 165 events and 135 censored observations. The data were split into training and testing sets. DeepSurv was implemented using two hidden layers (64 and 32 neurons), a dropout rate of 0.2, and a learning rate of 1e-3. The model was trained for up to 1000 epochs with early stopping at epoch 435. Performance was evaluated using the concordance index (C-index) and time-dependent AUC at 365, 544, and 730 days. Patients were stratified into low-, medium-, and high-risk groups based on predicted scores. Results showed that Cox PH achieved a C-index of 0.913 and average AUC of 0.964, while Cox PH Spline reached 0.917 and 0.971. DeepSurv achieved a C-index of 0.920 and average AUC of 0.969. Performance differences were small, but DeepSurv provided consistent individual risk estimates. In conclusion, DeepSurv is a flexible approach with performance comparable to Cox-based models. Further external validation and clinical evaluation are needed before wider application
subject: Data Science
  topic: section
subject: DeepSurv
  topic: keywords
subject: Hemodialysis
  topic: keywords
subject: Kidney Failure
  topic: keywords
subject: Survival Analysis
  topic: keywords
creator: Rizki Amanda
creator: Aviolla Terza Damaliana
creator: Muhammad Idhom
rights: Copyright (c) 2026 Rizki Amanda, Aviolla Terza Damaliana, Muhammad Idhom, Muhamad Liswansyah Pratama https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 122
ending page: 134
doi: 10.56705/ijodas.v7i1.389

Automated Waste Image Classification with Weighted Scoring Using MobileNetV2 on the OLSAM Platform
type: item
description: This study presents the development of an automated waste image classification system for the OLSAM platform to enhance community participation in waste management. The objective is to integrate a lightweight CNN-based classifier with a weighted point calculation mechanism for five waste categories. A dataset of 1,500 images was used, split into 80% training, 10% validation, and 10% testing. The MobileNetV2 architecture was applied to perform image classification, while a weighted reward mechanism assigned points based on the detected waste type and its weight. The model achieved its best performance at epoch 65, reaching an accuracy of 96.67% and a weighted F1-score of 0.97. These results indicate that combining CNN-based recognition with a weighted point system effectively supports user engagement and promotes sustainable waste-sorting behavior within community waste management systems.
subject: Data Science
  topic: section
subject: Convolutional Neural Network
  topic: keywords
subject: MobileNetV2
  topic: keywords
subject: Smart Waste Management
  topic: keywords
subject: Waste Classification
  topic: keywords
subject: Weighted Approach
  topic: keywords
creator: Kartika Nur Anggraeni
creator: Arin Yuli Astuti
creator: Ismail Abdurrozzaq Zulkarnain
rights: Copyright (c) 2025 Kartika Nur Anggraeni, Arin Yuli Astuti, Ismail Abdurrozzaq Zulkarnain https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 370
ending page: 377
doi: 10.56705/ijodas.v6i3.295

Sentiment Classification and Influential Actor Detection on Twitter (Case Study: The Raja Ampat Mining Conflict)
type: item
description: The nickel mining conflict in Raja Ampat has attracted extensive public attention due to the region’s global ecological significance and the potential environmental risks posed by extractive activities. Social media platforms, particularly Twitter, have become important spaces for public discussion and opinion exchange regarding this issue. This study aims to analyze public sentiment and identify influential actors in online discussions of the Raja Ampat mining conflict by integrating sentiment analysis and Social Network Analysis (SNA). This study adopts a cross-sectional design using Indonesian-language tweets collected between 15-27 November 2025. A total of 11,671 tweets were obtained through keyword-based crawling, and after preprocessing and duplicate removal, 8,909 tweets were retained for analysis. Sentiment labeling was performed using a lexicon-based approach, categorizing tweets into positive, neutral, and negative classes. The dataset was divided using an 80:20 train–test split. Sentiment classification was conducted using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes algorithms. Model performance was evaluated using confusion matrix–based metrics, including accuracy, precision, recall, and F1-score. Social Network Analysis was carried out by constructing a directed interaction network based on mentions, replies, and retweets, with influential actors identified using degree and betweenness centrality measures. The results indicate that neutral sentiment dominates the discourse (51.58%), followed by negative and positive sentiments. SVM and Naive Bayes demonstrate more stable classification performance than KNN, while network analysis shows that influence is concentrated among a limited number of central actors
subject: Data Science
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: Social Network Analysis
  topic: keywords
subject: SVM
  topic: keywords
subject: KNN
  topic: keywords
subject: Naive Bayes
  topic: keywords
subject: Nickel Mining
  topic: keywords
subject: Raja Ampat
  topic: keywords
subject: Twitter
  topic: keywords
creator: Micguel Arter Imbiri
creator: Lorna Yertas Baisa
creator: Josua Josen A. Limbong
rights: Copyright (c) 2026 Micguel Arter Imbiri, Lorna Yertas Baisa, Josua Josen A. Limbong https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 15
ending page: 31
doi: 10.56705/ijodas.v7i1.376

A Hybrid Convolutional Neural Network and Bidirectional LSTM Architecture for Multi-Sector Export Forecasting: A Macroeconomic Time Series Analysis of Indonesia
type: item
description: Accurately predicting export values is key for a country in formulating its economic plans. Unfortunately, export data often exhibits complex time series patterns that are difficult to predict, characterized by non-linearity, high volatility, and complex temporal dependencies. This study offers a solution by testing a combined deep learning model, specifically a fusion of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), to address the challenges of export time series forecasting. This study uses this approach to forecast Indonesia's monthly export time series data from 2016 to 2023, covering various sectors ranging from oil and gas, non-oil and gas, agriculture, industry, mining, and others. The core idea is to leverage the CNN's ability to identify hidden features within time series patterns, while the BiLSTM is tasked with understanding the temporal flow of data from both directions to capture the inherent long-term temporal dependencies within economic time series data. As a result, this combined model proved to be far superior to the standard BiLSTM model in handling the complexity of export time series. In the Non-Oil and Gas sector, the proposed model achieved a high level of accuracy with an MSE value of 3,330,239.74, an RMSE of 1,824.89, and an average prediction error (MAPE) of only 8.17%, representing a significant improvement of 69% over the baseline BiLSTM model. Similar success was also found in all other sectors, proving that this hybrid approach is highly promising for complex economic time series analysis
subject: Data Science
  topic: section
subject: Bidirectional LSTM
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Hybrid Architecture
  topic: keywords
subject: Export Forecasting
  topic: keywords
subject: Time Series Analysis
  topic: keywords
creator: Desi Anggreani
creator: Nurmisba Nurmisba
creator: Aedah Abd Rahman
rights: Copyright (c) 2025 Nurmisba Nurmisba, Desi Anggreani, Aedah Abd Rahman https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-27
publication date: 2025-12-27
starting page: 538
ending page: 547
doi: 10.56705/ijodas.v6i3.330

A Website-Based Management Information System For Pratama Sidhi SAI Clinic
type: item
description: Healthcare services in Indonesia currently need to be improved given Indonesia's dense population, which results in patient queues at health service facilities. This is due to several factors, one of which is the manual processing of health data, as is the case at the Sidhi Sai Pratama Clinic. This research aims to improve healthcare services and provide easy access to information for both clinic staff and patients. The stages of this research method are needs analysis, system design, implementation, and testing. In the needs analysis stage, data was collected through direct observation and interviews with one of the clinic staff. The system design stage was carried out by creating a system flowchart and database model required to ensure the clinic's needs for the system were met. The results of the study showed that the system can run effectively in terms of managing patient data, patient medical records, and managing medication data. Based on the results of testing using the black box testing method, all features in the system are functioning well according to the objectives. With this system, it is hoped that the problem of patient queues can be overcome by providing effective and efficient healthcare services
subject: Data Science
  topic: section
subject: Management Information Systems
  topic: keywords
subject: Health System
  topic: keywords
subject: Queues
  topic: keywords
creator: Ni Made Diah Nandita Pangestu
creator: Made Leo Radhitya
creator: Made Wahyu Adhiputra
creator: I Made Subrata Sandhiyasa
creator: I Putu Agus Eka Darma Udayana
rights: Copyright (c) 2025 Ni Made Diah Nandita Pangestu, Made Leo Radhitya, Made Wahyu Adhiputra, I Made Subrata Sandhiyasa, I Putu Agus Eka Darma Udayana https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-27
publication date: 2025-12-27
starting page: 454
ending page: 461
doi: 10.56705/ijodas.v6i3.370

Artificial Intelligence (AI) using Long Short-Term Memory (LSTM) for Sales Prediction in Campus Minimarkets
type: item
description: This study applies Artificial Intelligence (AI) using the Long Short-Term Memory (LSTM) algorithm to predict daily sales at the FIKOM-UMI Minimarket. Sales data from 2023 to 2024 involving 82 items were used and processed into a time series format. Five LSTM architectural scenarios were tested, including baseline, bigger model, lightweight, bidirectional LSTM, and single-layer medium, to identify the most effective model in capturing sales patterns. The data underwent preprocessing stages, including daily aggregation, reindexing to fill missing dates, and normalization using MinMaxScaler before being transformed into sequences with a 30-day time step. Model performance was evaluated using MSE, RMSE, MAPE, and accuracy metrics. The results show that the Bidirectional LSTM (Scenario 4) achieved the best performance, with the lowest MAPE of 19.43% and the highest accuracy of 80.57%. The model successfully generated stable predictions for 7-day and 30-day forecasting with a range of 153–155 units per day, indicating consistent sales patterns. Testing on the top 10 best-selling items showed significant performance variation, with GARUDA ROSTA BWNG 100 Gram achieving the highest accuracy (46.97%), while aoka rasa pandan showed the lowest performance (-76.05%). These findings demonstrate that the LSTM model can be effectively applied for sales prediction in campus minimarkets; however, a hybrid approach with product segmentation is recommended to optimize inventory management across product categories with varying levels of predictability
subject: Data Science
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: LSTM
  topic: keywords
subject: Sales Prediction
  topic: keywords
subject: Minimarket
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Harlinda L
creator: Abdul Rachman Manga
creator: Ramdan Satra
rights: Copyright (c) 2026 Harlinda L, Abdul Rachman Manga, Ramdan Satra https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 42
ending page: 52
doi: 10.56705/ijodas.v7i1.373

Sarcasm and Irony Detection in Lazada App Reviews Using IndoBERT
type: item
description: Digital technology has reshaped consumer behavior, particularly in e-commerce, where Google Play Store reviews provide rich feedback but often include sarcasm and irony that conventional sentiment models misread. This study proposes an Indonesian sarcasm–irony detection model using IndoBERT, a transformer pre-trained on Indonesian corpora. A dataset of 1,998 Lazada app reviews was collected via web scraping and preprocessed through text cleaning, tokenization, and stopword removal with the Sastrawi library. IndoBERT was fine-tuned to classify reviews into three classes: sarcasm, irony, and literal. Performance was assessed using accuracy, precision, recall, F1-score, and a confusion matrix. The model achieved 96.40% accuracy, with F1-scores of 0.9725 (sarcasm), 0.9675 (irony), and 0.9267 (literal). Word cloud visualizations revealed distinct lexical patterns across classes, supporting IndoBERT’s ability to capture contextual cues behind implicit sentiment. The findings indicate IndoBERT is effective for advanced opinion mining in Indonesian e-commerce, with potential applications in customer feedback monitoring, surfacing hidden complaints, and improving recommendation systems beyond surface polarity. Limitations include reliance on a single platform (Google Play) and text-only input, without modeling non-textual signals such as emojis or punctuation intensity. Future work should test cross-platform generalization, incorporate non-textual cues, and apply data augmentation to reduce class imbalance, particularly for the less frequent literal class, to improve robustness for real-world deployment
subject: Data Science
  topic: section
subject: Natural Language Processing
  topic: keywords
subject: IndoBERT
  topic: keywords
subject: Sarcasm Detection
  topic: keywords
subject: Irony Detection
  topic: keywords
subject: E-Commerce Reviews
  topic: keywords
creator: Nabila Putri
creator: Adhitia Erfina
creator: Cecep Warman
rights: Copyright (c) 2025 Nabila Putri https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 618
ending page: 625
doi: 10.56705/ijodas.v6i3.307

Vehicle Detection Using YOLOv8 on Low-Resolution Images
type: item
description: Vehicle detection in low-resolution images remains a significant challenge in computer vision, particularly for embedded devices such as ESP32-CAM with limited computational resources and simple image resolution. This study evaluates the performance of YOLOv8 on low-resolution QVGA (320 × 240 pixels) images for vehicle detection and classification. The dataset was independently collected in a controlled laboratory environment using miniature vehicles, covering four vehicle classes (motorcycle, car, bus, and truck) with a total of 4,000 images and a 70:20:10 data split. A pretrained YOLOv8 model was fine tuned for 100 epochs and tested on an ESP32-CAM prototype. The evaluation results demonstrate excellent performance, achieving precision of 0.999, recall of 1.000, mAP@0.5 of 0.995, and mAP@0.5-0.95 of 0.995 on the validation data, as well as real-time detection accuracy of 97% for motorcycles and cars, and 99% for buses and trucks. These findings indicate that YOLOv8 can deliver reliable vehicle detection performance on low-resolution images and is suitable for implementation in embedded device-based systems
subject: Data Science
  topic: section
subject: YOLOv8
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: Intelligent Transport System
  topic: keywords
subject: Traffic Management
  topic: keywords
subject: KY-037
  topic: keywords
creator: Nifal
creator: Farniwati Fattah
creator: Andi Widya Mufila Gaffar
rights: Copyright (c) 2026 Nifal, Farniwati Fattah, Andi Widya Mufila Gaffar https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 32
ending page: 41
doi: 10.56705/ijodas.v7i1.371

Zero-Shot Sentiment Analysis Of DeepSeek AI App Reviews Using DeepSeek-R1
type: item
description: This study aims to evaluate the effectiveness of the Zero-Shot Learning (ZSL) approach using the DeepSeek-R1-Distill-Qwen-1.5B model in performing sentiment classification on Indonesian-language reviews of the DeepSeek AI application from the Google Play Store. A total of 2,000 unlabeled user reviews were collected and processed through instructional prompts to guide the model in classifying sentiments into three categories: positive, negative, and neutral. The model operates without fine-tuning and relies entirely on Zero-Shot Learning using Indonesian-language prompts. Out of 2,000 reviews, 1,348 were successfully classified with valid sentiment labels. Of these, 1,131 reviews (83.9%) were labeled as positive, 211 reviews (15.7%) as negative, and only 6 reviews (0.4%) as neutral. Evaluation results indicated an overall accuracy of 77.67%. The F1-Score for the positive class reached 86.66%, while the negative and neutral classes scored 33.56% and 16.66%, respectively, highlighting the performance disparity between dominant and underrepresented sentiment categories. These findings demonstrate that the DeepSeek-R1 model has strong potential in detecting positive sentiment in Indonesian without requiring additional training. However, its performance on negative and neutral sentiments remains limited, revealing the challenge of handling low-resource and imbalanced data in Zero-Shot settings. Future research should explore improved prompt engineering or multilingual adaptation to address the current limitations and enhance classification consistency across all sentiment categories
subject: Data Science
  topic: section
subject: Zero-Shot Learning
  topic: keywords
subject: DeepSeek-R1
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: Indonesian Language
  topic: keywords
subject: Google Play Store
  topic: keywords
creator: Restu sri Pamungkas
creator: Adhitia Erfina
creator: Cecep Warman
rights: Copyright (c) 2025 Restu sri Pamungkas https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 626
ending page: 636
doi: 10.56705/ijodas.v6i3.303

Classification of Cavendish Banana Ripeness With CNN Method
type: item
description: Cavendish bananas are one of the most widely consumed tropical fruits in Indonesia due to their sweet taste and high nutritional content. However, as they ripen, the sugar content in bananas increases, which can be a problem for diabetics. To help diabetics choose bananas with the right level of ripeness, this study developed a Cavendish banana ripeness classification model using artificial intelligence technology, namely the ResNet50 Convolutional Neural Network (CNN) architecture. The banana data is divided into five ripeness categories: green, yellowish green, yellow, spotted yellow, and spotted brownish yellow. The model was trained with two approaches, with and without data augmentation, using two types of training algorithms (optimizers), namely Adam and SGD, as well as a k-fold cross-validation method to ensure accurate results. The results showed that the ResNet50 model produced the highest accuracy of 98% when trained using data augmentation and the Adam optimizer with a learning rate setting of 0.0001.
subject: Data Science
  topic: section
subject: Augmentation
  topic: keywords
subject: Classification
  topic: keywords
subject: CNN
  topic: keywords
subject: K-Fold Cross Validation
  topic: keywords
subject: ResNet 50
  topic: keywords
creator: Tjokorda Istri Agung Pandu Yuni Maharani
creator: I Gusti Agung Indrawan
creator: Gede Dana Pramitha
creator: Christina Purnama Yanti
creator: I Made Marthana Yusa
rights: Copyright (c)
date: 2025-07-31
publication date: 2025-07-31
starting page: 203
ending page: 210
doi: 10.56705/ijodas.v6i2.259

Hybrid CNN-LSTM and Cox Model for Bipolar Risk Analysis Using Social Media Data
type: item
description: Introduction: Mental disorders such as bipolar disorder are becoming increasingly prominent, particularly with the rise of emotional expression through social media. Early detection remains a significant challenge due to the lack of non-invasive, real-time assessment methods. Methods: This study proposes a hybrid deep learning approach combining Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) and the Cox Proportional Hazards (Cox PH) model to analyze the risk and timing of bipolar disorder onset. A dataset of 3,511 tweets from 517 Twitter users was collected. The CNN-LSTM model classified bipolar risk levels based on text data, while the Cox PH model estimated the time-to-event for high-risk conditions using behavioral features and predicted risk labels. Results: The hybrid model demonstrated strong predictive performance. The risk label significantly influenced the time to high-risk condition (hazard ratio = 5.39, p < 0.005). The model achieved a concordance index (C-index) of 0.816, indicating high reliability in survival prediction. Conclusions: This case study highlights the potential of integrating deep learning and survival analysis for early bipolar disorder detection using social media data. The proposed non-invasive method can support mental health monitoring while raising awareness of ethical and privacy considerations
subject: Data Science
  topic: section
subject: Bipolar
  topic: keywords
subject: CNN-LSTM
  topic: keywords
subject: Cox Proportional Hazard
  topic: keywords
subject: Mental Health
  topic: keywords
subject: Risk Analysis
  topic: keywords
creator: Rizki Amanda
creator: Jasmine Aulia
rights: Copyright (c)
date: 2025-07-31
publication date: 2025-07-31
starting page: 222
ending page: 231
doi: 10.56705/ijodas.v6i2.265