[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120032-en":3,"doc-seo-120032-105":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":20,"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},120032,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Comparison of Machine Learning Algorithms in Detecting Tea Leaf Diseases -","Tea leaf disease detection supports maintaining Indonesia’s tea export performance, where export volumes and economic impact have declined despite competition. Early identification helps enable timely, appropriate treatment and reduce production costs. This study classifies tea leaf images to detect diseases by comparing machine learning models, including Random Forest, Support Vector Classifier, Extra Tree Classifier, Decision Tree, XGBoost, and a Convolutional Neural Network. Results show Extra Tree Classifier delivers the highest average accuracy at 77.47%, exceeding SVC 76.57%, RF 76.12%, DT 65.31%, XGB 71.62%, and CNN 59.08%.","Accredited SINTA 2 Ranking  \nDecree of the Director General of Higher Education, Research, and Technology, No. 158/E/KPT/2021 Validity period from Volume 5 Number 2 of 2021 to Volume 10 Number 1 of 2026  \nPublished online at: [http://jurnal.iaii.or.id](http://jurnal.iaii.or.id)  \nJURNAL RESTI  \n(Rekayasa Sistem dan Teknologi Informasi)  \nVol. 8 No. 1 (2024) 135-141 e-ISSN: 2580-0760  \nComparison of Machine Learning Algorithms in Detecting Tea Leaf  \nDiseases  \nCandra Nur Ihsan 1, Nova Agustina2, Muchammad Naseer3, Harya Gusdevi4, Jack Febrian Rusdi5, Ari  \nHadhiwibowo6, Fahmi Abdullah7  \n1Badan Riset dan Inovasi Nasional,  \n2,3,4,5,6,7Department of Informatics, Sekolah Tinggi Teknologi Bandung, Bandung, Indonesia  \n[1](1 candra.nur.ihsan@brin.go.id)[ candra.nur.ihsan@brin.go.id](1 candra.nur.ihsan@brin.go.id), [2](2nova@sttbandung.ac.id)[nova@sttbandung.ac.id](2nova@sttbandung.ac.id), [3](3naseer@sttbandung.ac.id)[naseer@sttbandung.ac.id](3naseer@sttbandung.ac.id), [4](4devi@sttbandung.ac.id)[devi@sttbandung.ac.id](4devi@sttbandung.ac.id),  \n[5](5jack@sttbandung.ac.id)[jack@sttbandung.ac.id](5jack@sttbandung.ac.id), [6](6 ari@sttbandung.ac.id)[ ari@sttbandung.ac.id](6 ari@sttbandung.ac.id), [7](7 fahmi@sttbandung.ac.id)[ fahmi@sttbandung.ac.id](7 fahmi@sttbandung.ac.id)  \nAbstract  \nTea is one of the top ten most exported products sent from Indonesia to foreign countries. However, in recent years, the amount of tea leaf exports from Indonesia has decreased, even though the export value impacts the country’s economic structure. Besides market competition, Indonesia needs to maintain tea leaf production so that the spike in export decline is not significant or even increases the export production of tea leaves. To improve the quality of production and reduce production costs, early detection of tea leaf diseases is necessary. This study aims to classify tea leaf images for early detection of tea leaf disease so that appropriate treatment can be carried out early on. This study compares Machine Learning algorithms to determine the best algorithm for detecting tea leaf diseases. The algorithms tested as performance comparisons in classifying the tea leaf diseases are Random Forest (RF), Support Vector Classifier (SVC), Extra Tree Classifier (ETC), Decision Tree (DT), XGBoost Classifier (XGB) and Convolutional Neural algorithms. Network (CNN). As a result, the average accuracy performance generated by ETC produces a higher value than other algorithms, i.e., getting an average accuracy performance of 77.47%. Another algorithm, i.e., SVC, has an average accuracy of 76.57%, RF of 76.12%, DT of 65.31%, XGB of 71.62%, and the lowest is CNN of 59.08%. ETC is proven to be the most superior Machine Learning algorithm for detecting tea leaf diseases in this study.  \nKeywords: comparison; machine learning; disease detection; tea leaves  \nHow to Cite: C. N. Ihsan,“Comparison of Machine Learning Algorithms in Detecting Tea Leaf Diseases”, J. RESTI (Rekayasa Sist. Teknol. Inf.), vol. 8, no. 1, pp. 135-141, Feb. 2024.  \nDOI: [https://doi.org/10.29207/resti.v8i1.5587](https://doi.org/10.29207/resti.v8i1.5587)  \n1. Introduction  \nIndonesia is a tropical climate country [1] and has fertile soil for farming [2]. Farming produces food production distributed for personal consumption, sold domestically, or exported abroad. One of the highest export products from Indonesia is tea leaves, which are included in the top 10 highest export products from Indonesia [3] . In 2019, Indonesia became one of the most tea leaf exporting countries, reaching 140 thousand tons of tea leaves that were exported abroad [4] . The tea leaves produced must be found to ensure the quality is maintained [5]-[7] . In recent years, the number of tea leaf exports from Indonesia has decreased, even though the value of exports affects the country’s economic structure [8] . Besides market competition, Indonesia needs to maintain the quality of tea leaves so that the surge in export d","cbCaihOnOEDQkzVj","https://ap.wps.com/l/cbCaihOnOEDQkzVj","pdf",718683,1,7,"English","en",105,"# Introduction\n## Problem background\n## Machine learning approaches\n# Abstract\n## Study objective and method","[{\"question\":\"What is the purpose of the study on tea leaf diseases?\",\"answer\":\"To classify tea leaf images for early detection of tea leaf diseases so appropriate treatment can be carried out early.\"},{\"question\":\"Which machine learning algorithms are compared in the study?\",\"answer\":\"Random Forest (RF), Support Vector Classifier (SVC), Extra Tree Classifier (ETC), Decision Tree (DT), XGBoost (XGB), and Convolutional Neural Network (CNN).\"},{\"question\":\"What is the best-performing algorithm according to the reported results?\",\"answer\":\"Extra Tree Classifier (ETC) produces the highest average accuracy of 77.47%, outperforming the other listed models.\"}]","Comparison of Machine Learning Algorithms in Detecting Tea Leaf Diseases - | PDF",1785727815,18,{"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},"comparison-of-machine-learning-algorithms-in-detecting-tea-leaf-diseases","",{"@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/comparison-of-machine-learning-algorithms-in-detecting-tea-leaf-diseases/120032/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the purpose of the study on tea leaf diseases?","Question",{"text":75,"@type":76},"To classify tea leaf images for early detection of tea leaf diseases so appropriate treatment can be carried out early.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are compared in the study?",{"text":80,"@type":76},"Random Forest (RF), Support Vector Classifier (SVC), Extra Tree Classifier (ETC), Decision Tree (DT), XGBoost (XGB), and Convolutional Neural Network (CNN).",{"name":82,"@type":73,"acceptedAnswer":83},"What is the best-performing algorithm according to the reported results?",{"text":84,"@type":76},"Extra Tree Classifier (ETC) produces the highest average accuracy of 77.47%, outperforming the other listed models.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"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"]