Leveraging K-Nearest Neighbors for Enhanced Fruit Classification and Quality Assessment

I Gede  Iwan Sudipa; Rezania Agramanisti  Azdy; Ika  Arfiani; Nicodemus Mardanus  Setiohardjo; Sumiyatun

doi:10.56705/ijodas.v5i1.125

Authors

I Gede Iwan Sudipa Institut Bisnis dan Teknologi Indonesia
Rezania Agramanisti Azdy Universitas Bina Darma
Ika Arfiani Universitas Ahmad Dahlan
Nicodemus Mardanus Setiohardjo Politeknik Negeri Kupang
Sumiyatun Universitas Teknilogi Digital Indonesia

DOI:

https://doi.org/10.56705/ijodas.v5i1.125

Keywords:

K-Nearest Neighbors, Fruit Classification, Quality Assessment, Agricultural Technology, Machine Learning, Cross-Validation

Abstract

This study investigates the application of the K-Nearest Neighbors (KNN) algorithm for fruit classification and quality assessment, aiming to enhance agricultural practices through machine learning. Employing a comprehensive dataset that encapsulates various fruit attributes such as size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality, the research leverages a 5-fold cross-validation method to ensure the reliability and generalizability of the KNN model's performance. The findings reveal that the KNN algorithm demonstrates high accuracy, precision, recall, and F1-Score across all metrics, indicating its efficacy in classifying fruits and predicting their quality accurately. These results not only validate the algorithm's potential in agricultural applications but also align with existing research on machine learning's capability to tackle complex classification problems. The study's discussions extend to the practical implications of implementing a KNN-based model in the agricultural sector, highlighting the possibility of revolutionizing quality control and inventory management processes. Moreover, the research contributes to the field by confirming the hypothesis regarding the effectiveness of KNN in agricultural settings and lays the foundation for future explorations that could integrate multiple machine learning techniques for enhanced outcomes. Recommendations for subsequent studies include expanding the dataset and exploring algorithmic synergies, aiming to further the advancements in agricultural technology and machine learning applications.

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