Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm
Keywords:
Empon Plants, Image Processing, Hu Moments, K-Nearest Neighbors (K-NN), Plant Classification
Abstract
The study "Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm" investigates the potential of image processing and machine learning techniques in the classification of empon plants, specifically ginger and turmeric. Utilizing a dataset of leaf images, the research employed the Canny method for image segmentation and Hu Moments for feature extraction, followed by classification using the K-Nearest Neighbors (K-NN) algorithm. The performance of the model was evaluated through a 5-fold cross-validation method, focusing on metrics such as accuracy, precision, recall, and F1-score. The results showcased the model's variable performance, with the highest accuracy reaching 65.33%. The study contributes to the field by demonstrating the application of Hu Moments in plant classification and by assessing the K-NN algorithm's effectiveness in this context. These findings offer insights into the potential of combining image processing techniques with machine learning for accurate plant classification, paving the way for further research in the area.
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References
[1] M. Hanafi and B. Agustian, “Deep Neural Network Method to Classify Empon-Empon Herb Based on E-Nose,” 2020 Fifth Int. Conf. …, 2020, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9288553/.
[2] B. Iqbal, “Canny edge detection and Hough transform for high resolution video streams using Hadoop and Spark,” Cluster Comput., vol. 23, no. 1, pp. 397–408, 2020, doi: 10.1007/s10586-019-02929-x.
[3] J. Trivedi, “Canny edge detection based real-time intelligent parking management system,” Sci. J. Silesian Univ. Technol. Ser. Transp., vol. 106, pp. 197–208, 2020, doi: 10.20858/sjsutst.2020.106.17.
[4] Y. Jusman, “Machine Learnings of Dental Caries Images based on Hu Moment Invariants Features,” Proc. - 2021 Int. Semin. Appl. Technol. Inf. Commun. IT Oppor. Creat. Digit. Innov. Commun. within Glob. Pandemic, iSemantic 2021, pp. 296–299, 2021, doi: 10.1109/iSemantic52711.2021.9573208.
[5] H. Azis, P. Purnawansyah, F. Fattah, and I. P. Putri, “Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung,” Ilk. J. Ilm., vol. 12, no. 2, pp. 81–86, Aug. 2020, doi: 10.33096/ilkom.v12i2.507.81-86.
[6] A. Aisyah and S. Anraeni, “Analisis penerapan metode K-Nearest Neighbor (K-NN) pada dataset citra penyakit malaria,” Indones. J. Data Sci., 2022, [Online]. Available: https://www.jurnal.yoctobrain.org/index.php/ijodas/article/view/22.
[7] S. Sahar, “Analisis Perbandingan Metode K-Nearest Neighbor dan Naïve Bayes Clasiffier Pada Dataset Penyakit Jantung,” Indones. J. Data Sci., vol. 1, no. 3, pp. 79–86, 2020, doi: https://doi.org/10.33096/ijodas.v1i3.20.
[8] I. P. Putri, “Analisis Performa Metode K- Nearest Neighbor (KNN) dan Crossvalidation pada Data Penyakit Cardiovascular,” Indones. J. Data Sci., vol. 2, no. 1, pp. 21–28, 2021, doi: 10.33096/ijodas.v2i1.25.
[9] S. S. Gornale, “Automatic Detection and Classification of Knee Osteoarthritis Using Hu’s Invariant Moments,” Front. Robot. AI, vol. 7, 2020, doi: 10.3389/frobt.2020.591827.
[10] A. M. Argina, “Application of the K-Nearest Neighbor Classification Method on a Dataset of Diabetes Patients,” Indones. J. Data Sci., 2020.
[11] D. N. Lohare, “Comparative Study of Prewitt and Canny Edge Detector Using Image Processing Techniques,” Adv. Intell. Syst. Comput., vol. 1187, pp. 705–713, 2021, doi: 10.1007/978-981-15-6014-9_86.
[12] A. Sinra, B. S. W. Poetro, H. Angriani, H. Zein, and ..., “Optimizing Neurodegenerative Disease Classification with Canny Segmentation and Voting Classifier: An Imbalanced Dataset Study,” … Artif. Intell. …, 2023, [Online]. Available: https://www.jurnal.yoctobrain.org/index.php/ijaimi/article/view/97.
[13] R. Setiawan, A. Parewe, A. J. Latipah, and ..., “Assessing Bagging-meta Estimator in Imbalanced CT Kidney Disease Classification: A Focus on Sobel and Hu Moment Techniques,” … Artif. Intell. …, 2023, [Online]. Available: https://www.jurnal.yoctobrain.org/index.php/ijaimi/article/view/100.
[14] N. Rismayanti, A. Naswin, U. Zaky, M. Zakariyah, and D. A. Purnamasari, “Evaluating Thresholding-Based Segmentation and Humoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes,” Int. J. Artif. Intell. Med. Issues, vol. 1, no. 2, 2023.
[15] S. AbuRass, “Enhancing Convolutional Neural Network using Hu’s Moments,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 12, pp. 130–137, 2020, doi: 10.14569/IJACSA.2020.0111216.
[16] Y. Jusman, “Classification System of Malaria Disease with Hu Moment Invariant and Support Vector Machines,” Proc. - 2022 2nd Int. Conf. Electron. Electr. Eng. Intell. Syst. ICE3IS 2022, pp. 365–368, 2022, doi: 10.1109/ICE3IS56585.2022.10010304.
[17] H. Basri, P. Purnawansyah, H. Darwis, and F. Umar, “Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Convolutional Neural Network dengan Ekstraksi Fourier Descriptor,” J. Teknol. dan Manaj. …, 2023.
[18] A. Nurjulianty, P. Purnawansyah, and ..., “Perbandingan Metode Naïve Bayes dan K-NN dengan Ekstraksi Fitur GLCM pada Klasifikasi Daun Herbal,” J. MEDIA …, 2023, [Online]. Available: http://www.ejurnal.stmik-budidarma.ac.id/index.php/mib/article/view/6262.
[19] E. Najwaini, T. E. Tarigan, and F. P. Putra, “Application of the K-Nearest Neighbors (KNN) Algorithm on the Brain Tumor Dataset,” … Artif. Intell. …, 2023, [Online]. Available: https://www.jurnal.yoctobrain.org/index.php/ijaimi/article/view/85.
[20] H. Azis, P. Purnawansyah, N. Nirwana, and ..., “The Support Vector Regression Method Performance Analysis in Predicting National Staple Commodity Prices,” Ilk. J. …, 2023, [Online]. Available: https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1686.
[21] N. Rismayanti and A. P. Utami, “Improving Multi-Class Classification on 5-Celebrity-Faces Dataset using Ensemble Classification Methods,” Indones. J. Data …, 2023, [Online]. Available: https://www.jurnal.yoctobrain.org/index.php/ijodas/article/view/78.
[2] B. Iqbal, “Canny edge detection and Hough transform for high resolution video streams using Hadoop and Spark,” Cluster Comput., vol. 23, no. 1, pp. 397–408, 2020, doi: 10.1007/s10586-019-02929-x.
[3] J. Trivedi, “Canny edge detection based real-time intelligent parking management system,” Sci. J. Silesian Univ. Technol. Ser. Transp., vol. 106, pp. 197–208, 2020, doi: 10.20858/sjsutst.2020.106.17.
[4] Y. Jusman, “Machine Learnings of Dental Caries Images based on Hu Moment Invariants Features,” Proc. - 2021 Int. Semin. Appl. Technol. Inf. Commun. IT Oppor. Creat. Digit. Innov. Commun. within Glob. Pandemic, iSemantic 2021, pp. 296–299, 2021, doi: 10.1109/iSemantic52711.2021.9573208.
[5] H. Azis, P. Purnawansyah, F. Fattah, and I. P. Putri, “Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung,” Ilk. J. Ilm., vol. 12, no. 2, pp. 81–86, Aug. 2020, doi: 10.33096/ilkom.v12i2.507.81-86.
[6] A. Aisyah and S. Anraeni, “Analisis penerapan metode K-Nearest Neighbor (K-NN) pada dataset citra penyakit malaria,” Indones. J. Data Sci., 2022, [Online]. Available: https://www.jurnal.yoctobrain.org/index.php/ijodas/article/view/22.
[7] S. Sahar, “Analisis Perbandingan Metode K-Nearest Neighbor dan Naïve Bayes Clasiffier Pada Dataset Penyakit Jantung,” Indones. J. Data Sci., vol. 1, no. 3, pp. 79–86, 2020, doi: https://doi.org/10.33096/ijodas.v1i3.20.
[8] I. P. Putri, “Analisis Performa Metode K- Nearest Neighbor (KNN) dan Crossvalidation pada Data Penyakit Cardiovascular,” Indones. J. Data Sci., vol. 2, no. 1, pp. 21–28, 2021, doi: 10.33096/ijodas.v2i1.25.
[9] S. S. Gornale, “Automatic Detection and Classification of Knee Osteoarthritis Using Hu’s Invariant Moments,” Front. Robot. AI, vol. 7, 2020, doi: 10.3389/frobt.2020.591827.
[10] A. M. Argina, “Application of the K-Nearest Neighbor Classification Method on a Dataset of Diabetes Patients,” Indones. J. Data Sci., 2020.
[11] D. N. Lohare, “Comparative Study of Prewitt and Canny Edge Detector Using Image Processing Techniques,” Adv. Intell. Syst. Comput., vol. 1187, pp. 705–713, 2021, doi: 10.1007/978-981-15-6014-9_86.
[12] A. Sinra, B. S. W. Poetro, H. Angriani, H. Zein, and ..., “Optimizing Neurodegenerative Disease Classification with Canny Segmentation and Voting Classifier: An Imbalanced Dataset Study,” … Artif. Intell. …, 2023, [Online]. Available: https://www.jurnal.yoctobrain.org/index.php/ijaimi/article/view/97.
[13] R. Setiawan, A. Parewe, A. J. Latipah, and ..., “Assessing Bagging-meta Estimator in Imbalanced CT Kidney Disease Classification: A Focus on Sobel and Hu Moment Techniques,” … Artif. Intell. …, 2023, [Online]. Available: https://www.jurnal.yoctobrain.org/index.php/ijaimi/article/view/100.
[14] N. Rismayanti, A. Naswin, U. Zaky, M. Zakariyah, and D. A. Purnamasari, “Evaluating Thresholding-Based Segmentation and Humoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes,” Int. J. Artif. Intell. Med. Issues, vol. 1, no. 2, 2023.
[15] S. AbuRass, “Enhancing Convolutional Neural Network using Hu’s Moments,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 12, pp. 130–137, 2020, doi: 10.14569/IJACSA.2020.0111216.
[16] Y. Jusman, “Classification System of Malaria Disease with Hu Moment Invariant and Support Vector Machines,” Proc. - 2022 2nd Int. Conf. Electron. Electr. Eng. Intell. Syst. ICE3IS 2022, pp. 365–368, 2022, doi: 10.1109/ICE3IS56585.2022.10010304.
[17] H. Basri, P. Purnawansyah, H. Darwis, and F. Umar, “Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Convolutional Neural Network dengan Ekstraksi Fourier Descriptor,” J. Teknol. dan Manaj. …, 2023.
[18] A. Nurjulianty, P. Purnawansyah, and ..., “Perbandingan Metode Naïve Bayes dan K-NN dengan Ekstraksi Fitur GLCM pada Klasifikasi Daun Herbal,” J. MEDIA …, 2023, [Online]. Available: http://www.ejurnal.stmik-budidarma.ac.id/index.php/mib/article/view/6262.
[19] E. Najwaini, T. E. Tarigan, and F. P. Putra, “Application of the K-Nearest Neighbors (KNN) Algorithm on the Brain Tumor Dataset,” … Artif. Intell. …, 2023, [Online]. Available: https://www.jurnal.yoctobrain.org/index.php/ijaimi/article/view/85.
[20] H. Azis, P. Purnawansyah, N. Nirwana, and ..., “The Support Vector Regression Method Performance Analysis in Predicting National Staple Commodity Prices,” Ilk. J. …, 2023, [Online]. Available: https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1686.
[21] N. Rismayanti and A. P. Utami, “Improving Multi-Class Classification on 5-Celebrity-Faces Dataset using Ensemble Classification Methods,” Indones. J. Data …, 2023, [Online]. Available: https://www.jurnal.yoctobrain.org/index.php/ijodas/article/view/78.
Published
2024-01-26
How to Cite
Hayatou Oumarou, & Rismayanti, N. (2024). Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm. Indonesian Journal of Data and Science, 4(3), 206-214. https://doi.org/10.56705/ijodas.v4i3.115
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