[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119537-en":3,"doc-seo-119537-105":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},119537,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Enhancing Crude Palm Oil Quality Detection Using Machine Learning Techniques","Enhancing crude palm oil (CPO) quality relies on reducing subjectivity in human evaluation that can cause inconsistent and less objective results. The study collects CPO quality data, performs pre-processing, and classifies CPO into two quality categories using machine learning models including ANN, KNN, SVM, decision tree (DT), naïve Bayes (NB), and C.45. Cross-validation is used for evaluation, and the best performance is achieved by C.45 and DT with an accuracy of 99.98%.","Enhancing crude palm oil quality detection using machine  \nlearning techniques  \nNovianti Puspitasari1, Ummul Hairah1, Vina Zahrotun Kamila2, Hamdani Hamdani1, Anindita Septiarini1, Amin Padmo Azam Masa2  \n1Department of Informatics, Faculty of Engineering, Mulawarman University, Samarinda, Indonesia 2Department of Information System, Faculty of Engineering, Mulawarman University, Samarinda, Indonesia  \nArticle history:  \nReceived Sep 8, 2024 Revised May 10, 2025 Accepted Jun 8, 2025  \nKeywords:  \nCrude palm oil Data acquisition Machine learning Pre-processing Quality evaluation  \nCorresponding Author:  \nIndonesia, a leading nation in the palm oil industry, experienced a significant increase of 15.62% in crude palm oil (CPO) exports in 2020, effectively meeting the global need for vegetable oil and fat. Therefore, the subjective assessment of CPO quality, influenced by differences in human evaluations, may lead to inconsistencies, necessitating the adoption of machine learning methods. There are several categories of CPO, such as bad and excellent. Machine learning can determine the quality of CPO itself. This study utilizes two distinct categories to measure the quality of CPO. CPO quality data is collected and processed into pre-processing data, in classifying using several methods such as artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naïve Bayes (NB), and C.45 using the cross-validation evaluation parameter. The best results are obtained by C.45 and DT with an accuracy of99.98% .  \nThis is an open access article under the CC BY-SA license.  \nNovianti Puspitasari  \nDepartment of Informatics, Faculty of Engineering, Mulawarman University [Sambaliung St. no. 9](Sambaliung St. no. 9), Samarinda, Indonesia [Email: novipuspitasari@unmul.ac.id](Email: novipuspitasari@unmul.ac.id).  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIndonesia, an agricultural nation, is a major palm oil producer due to its large land area and low production cost. The country's palm oil production rose 14% in August 2019, with a 33.88% increase in output from people's plantations and 58.56% growth from private large plantations. However, the state plantation (SP) has a sluggish output rate of 7.55% . The country's palm oil industry contributes to economic prosperity [1] . Indonesia's crude palm oil (CPO) exports increased by 15.62% in 2020 to 26.47 million tons, meeting the growing global demand for vegetable oils and fats [2] . Superior CPO is produced using mature oil palm fruits, classified as unripe for raw, ripe for harvestable, and overripe for entirely ripe. This results in high oil extraction efficiency and low free fatty acids (FFA) . The subjective nature of CPO quality assessment due to individual appraisal differences can lead to inconsistencies, potentially lowering accuracy and objectivity, thus necessitating machine learning methods for CPO quality assessment.  \nNumerous researchers have thoroughly investigated the utilization of computerized technology in agriculture. This task includes IoT [3], remote sensing data [4], land suitability [5], identifying disease on fruit [6], leaves [7]–[9] and stem [10], segmenting fruit [11], [12], estimating the mass of fruit [13], [14], and fruit maturity [15]–[17] . In classification, several methods have been applied in several machine learning such as artificial neural network (ANN) [18], [19], support vector machine (SVM) [20]–[22], random forest (RF) and gradient boosting [3], [23], decision tree (DT) [24], [25], and k-nearest neighbor (KNN) [26], [27] .  \nSeveral studies related to oil palm objects were developed by implementing machine learning approaches. Three machine learning algorithms were employed: multilayer perceptron, support vector regression (SVR), and linear regression to forecast CPO production. The SVR method surpassed the other two in prediction accuracy, with a positive predictive accuracy of 0.694, mean squared error (M","cbCaiuzcMrELrLvi","https://ap.wps.com/l/cbCaiuzcMrELrLvi","pdf",534314,1,9,"English","en",105,"# Introduction\n## Background and problem of subjective CPO quality assessment\n## Related machine learning research on oil palm\n# Method\n## Data collection and pre-processing\n## Classification models and evaluation","[{\"question\":\"Why is machine learning needed for crude palm oil (CPO) quality evaluation?\",\"answer\":\"Human-based quality assessment is subjective, leading to inconsistencies that reduce accuracy and objectivity. Machine learning provides a more consistent classification approach.\"},{\"question\":\"How is the CPO quality dataset prepared in the study?\",\"answer\":\"CPO quality data are collected and converted into pre-processing data. The preparation includes purification, transformation, normalization, and resampling before classification.\"},{\"question\":\"Which models achieved the best classification performance and what accuracy was reported?\",\"answer\":\"C.45 and decision tree (DT) produced the best results. Both achieved an accuracy of 99.98% under cross-validation evaluation.\"}]","Enhancing Crude Palm Oil Quality Detection Using Machine Learning Techniques | PDF",1785724834,23,{"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},"enhancing-crude-palm-oil-quality-detection-using-machine-learning-techniques","",{"@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/enhancing-crude-palm-oil-quality-detection-using-machine-learning-techniques/119537/",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},"Why is machine learning needed for crude palm oil (CPO) quality evaluation?","Question",{"text":75,"@type":76},"Human-based quality assessment is subjective, leading to inconsistencies that reduce accuracy and objectivity. Machine learning provides a more consistent classification approach.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the CPO quality dataset prepared in the study?",{"text":80,"@type":76},"CPO quality data are collected and converted into pre-processing data. The preparation includes purification, transformation, normalization, and resampling before classification.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models achieved the best classification performance and what accuracy was reported?",{"text":84,"@type":76},"C.45 and decision tree (DT) produced the best results. 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