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All Journal Natural Science: Journal of Science and Technology Electronic Journal of Graph Theory and Applications (EJGTA) JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan Mathvision : Jurnal Matematika Jurnal Pengabdian Farmasi dan Sains
Selvy Musdalifah
Algebra And Combinatorics Research Group, Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, Universitas Tadulako
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation

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COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS IN CLASSIFYING THE QUALITY OF PALU SHALLOTS Lusiyanti, Desy; Musdalifah, Selvy; Sahari, Agusman; Fajri, Iman Al
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1853-1864

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.
Co-Authors Afni, N Agusman Sahari Al Fajri, Iman Andi Hendra Andri Anita Ahmad Kasim Armayani Arsal Desy Lusiyanti Faisal Susanto Hartayuni Sain Hervisari, Hervisari Huzaimah Huzaimah I W. Sudarsa I Wayan Sudarsana Iman Al Fajri Iman Al Fajri Ismiyanti, Ismiyanti Jeffry Kusuma Merdekawati, Dian Ayu Nasria Nacong Noviana Noviana Nurainun Nurainun Nurwijayanti Rizki Amalia S Fatimah, S Siswahyudianto Tandi, Andri Wahyuningsi, Sri Werokati, I Yesi, Indah