[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120379-en":3,"doc-seo-120379-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120379,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS IN CLASSIFYING THE QUALITY OF PALU SHALLOTS","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 described by 10 key features including size, color, texture, and moisture content. Five models—Naïve Bayes, Decision Tree, Random Forest, SVM, and Logistic Regression—are assessed using accuracy, precision, recall, and F1 score. Random Forest achieves the best overall performance and offers the most reliable quality classification for agricultural assurance.","COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS IN CLASSIFYING THE QUALITY OF PALU  \nSHALLOTS  \nDesy Lusiyanti1, Selvy Musdalifah2*, Agusman Sahari3, Iman Al Fajri4  \n1,2,3,4Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Tadulako Jln. Soekarno Hatta No.KM. 9, Tondo, Mantikulore Sub-District, Palu, Central Sulawesi, 94148, Indonesia  \n1Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya Jln. Veteran, Malang, East Java, 65145, Indonesia  \nCorresponding author’s e-mail: *[selvymusdalifah@gmail.com](selvymusdalifah@gmail.com)  \n\n| ABSTRACT |  |  |\n| --- | --- | --- |\n| Article History:\u003Cbr>Received: 5th November 2024\u003Cbr>Revised: 7th February 2025\u003Cbr>Accepted: 8th April 2025\u003Cbr>Published: 1st July 2025\u003Cbr>Keywords:\u003Cbr>Classification; Machine Learning;\u003Cbr>Palu Shallots;\u003Cbr>Random Forest;\u003Cbr>SVM. | 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 echnology for more precise and efficient agricultural quality assessm nt. |  |\n|  |  | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-ShareAlike 4.0 International License. |\n\nHow to cite this article:  \nD. Lusiyanti, S. Musdalifah, A. Sahari and I. Al 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, September, 2025.  \nCopyright © 2025 Author(s)  \nJournal homepage: [https://ojs3.unpatti.ac.id/index.php/barekeng/](https://ojs3.unpatti.ac.id/index.php/barekeng/)  \nJournal e-mail: [barekeng.math@yahoo.com](barekeng.math@yahoo.com); [barekeng.journal@mail.unpatti.ac.id](barekeng.journal@mail.unpatti.ac.id)  \nResearch Article ∙ Open Access  \n1. INTRODUCTION  \nShallots (Allium Ascalonium L.) are a horticultural commodity that plays an important role in the agricultural sector in Indonesia [1] . Shallots are not only a staple ingredient in Indonesian cuisine but also have high economic value due to their increasing demand. In Palu City, Central Sulawesi, shallots are one of the leading agricultural products that significantly contribute to the local economy [2] .  \nHowever, the quality of the shallots produced often varies. This quality variation can be attributed to various factors, including cultivation techniques, soil conditions, weather, and post-harvest practices. Inconsistent shallot quality can affect selling prices and consumer satisfaction. Therefore, classifying the quality of shallots is crucial to ensure that the products meet the standards desired by the market.  \nIn several previous studies, methods such as linear regression, artificial neural networks, and pattern recognition have been used to classify the quality of shallots. For instance, a Pradana (2023) study focused on detecting sh","cbCaitZAdUs2sBwW","https://ap.wps.com/l/cbCaitZAdUs2sBwW","pdf",616436,1,12,"English","en",105,"# Introduction\n## Background and importance of shallot quality classification\n## Motivation and related work\n## Research aims and contributions","[{\"question\":\"What dataset and features are used to classify Palu shallot quality?\",\"answer\":\"The study uses 1,500 samples of Palu shallots, each represented by 10 features such as size, color, texture, and moisture content.\"},{\"question\":\"Which machine learning models are compared in the study?\",\"answer\":\"Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), and Logistic Regression are evaluated.\"},{\"question\":\"Why does Random Forest perform best for classifying shallot quality?\",\"answer\":\"Random Forest achieves the highest accuracy (95.4%) and strong precision and F1 score, making it the most reliable model among those tested.\"}]","COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS IN CLASSIFYING THE QUALITY OF PALU SHALLOTS | PDF",1785729739,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"comparative-analysis-of-machine-learning-methods-in-classifying-the-quality-of-palu-shallots","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/comparative-analysis-of-machine-learning-methods-in-classifying-the-quality-of-palu-shallots/120379/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What dataset and features are used to classify Palu shallot quality?","Question",{"text":75,"@type":76},"The study uses 1,500 samples of Palu shallots, each represented by 10 features such as size, color, texture, and moisture content.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the study?",{"text":80,"@type":76},"Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), and Logistic Regression are evaluated.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does Random Forest perform best for classifying shallot quality?",{"text":84,"@type":76},"Random Forest achieves the highest accuracy (95.4%) and strong precision and F1 score, making it the most reliable model among those tested.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]