COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS IN CLASSIFYING THE QUALITY OF PALU SHALLOTS

  • Desy Lusiyanti Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Tadulako, Indonesia https://orcid.org/0000-0001-5176-7867
  • Selvy Musdalifah Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Tadulako, Indonesia https://orcid.org/0009-0008-8763-1943
  • Agusman Sahari Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Tadulako, Indonesia https://orcid.org/0009-0004-5520-4243
  • Iman Al Fajri Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Tadulako, Indonesia https://orcid.org/0009-0004-5520-4243
DOI: https://doi.org/10.30598/barekengvol19iss3pp1853-1864
Keywords: Classification, Machine Learning, Palu Shallots, Random Forest, SVM

Abstract

This study conducts a comparative analysis of various machine learning methods for classifying the quality of Palu shallots based on the Indonesian National Standard (SNI). The dataset consists of 1,500 samples of Palu shallots, each characterized by 10 key features, including size, color, texture, and moisture content. Five machine learning models—Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), and Logistic Regression—were evaluated using accuracy, precision, recall, and F1 score as performance metrics. The results indicate that Random Forest achieved the best performance with an accuracy of 95.4%, followed by Decision Tree (90.7%) and SVM (90.2%). Random Forest also excelled in precision (93.6%) and F1 Score (93.5%), making it the most reliable model for shallot quality classification. Meanwhile, SVM demonstrated a good balance between recall and precision, making it a strong alternative. Implementing machine learning models has the potential to enhance the efficiency and accuracy of agricultural product quality assurance. The findings of this study provide valuable insights for farmers, agribusiness practitioners, and researchers adopting artificial intelligence technology for more precise and efficient agricultural quality assessment.

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Published
2025-07-01
How to Cite
[1]
D. Lusiyanti, S. Musdalifah, A. Sahari, and I. A. Fajri, “COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS IN CLASSIFYING THE QUALITY OF PALU SHALLOTS”, BAREKENG: J. Math. & App., vol. 19, no. 3, pp. 1853-1864, Jul. 2025.
Issue
Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
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Articles

Copyright (c) 2025 Desy Lusiyanti, Selvy Musdalifah, Agusman Sahari, Iman Al Fajri

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