[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124436-en":3,"doc-seo-124436-105":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},124436,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Classification of Oil Loss Levels in Palm Oil Processing Using Near-Infrared Spectroscopy with Machine Learning - Research summary","Oil losses in palm oil processing materials such as final effluent, empty fruit bunches, kernels, pressed fiber, and decanter solids hinder production efficiency and increase both financial losses and environmental burden. Free and Open Source Software Near Infrared Spectroscopy (FOSS-NIRS) enables fast, non-destructive oil detection, but classification accuracy needs improved analytical support. This study builds a machine learning model to classify FOSS-NIRS data into above-standard (red) and below-standard (green) categories across five material groups to support more reliable decision-making.","Classification of Oil Loss Levels in Palm Oil Processing Using Near-Infrared Spectroscopy with Machine Learning  \nMuhamad Ilham Fauzan 1, Jaka Adi Baskara2, Wahyuningdiah Trisari Harsanti Putri3,  \nQuintin Kurnia Dikara4  \nParamadina University, Jl. Raya Mabes Hankam No.Kav 9, Setu, Kec. Cipayung, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta 13880, Telp. (021) 79181188 [Email : muhamad.fauzan@students.paramadina.ac.id](Email : muhamad.fauzan@students.paramadina.ac.id1)[1](Email : muhamad.fauzan@students.paramadina.ac.id1), [jaka.baskara@students.paramadina.ac.id](jaka.baskara@students.paramadina.ac.id2)[2](jaka.baskara@students.paramadina.ac.id2), [wahyuningdiah.trisari@paramadina.ac.id](wahyuningdiah.trisari@paramadina.ac.id3)[3](wahyuningdiah.trisari@paramadina.ac.id3),  \n[dikara.barcah@paramadina.ac.id](dikara.barcah@paramadina.ac.id4)[4](dikara.barcah@paramadina.ac.id4)  \nReceived 17 June 2025; Revised 13 July 2025; Accepted 15 July 2025  \nAbstract - Oil losses in palm oil processing materials, such as Final Effluent, Empty Fruit Bunches, Kernels, Pressed Fiber, and Decanter Solids, pose significant challenges in ensuring production efficiency. Free and Open Source Software Near Infrared Spectroscopy (FOSS-NIRS) technology has been proven capable of quickly and efficiently detecting oil content, but its detection accuracy requires further analytical support. This study aims to develop a machine learning model that can accurately classify FOSS-NIRS data to detect oil losses that are either above the standard (red category) or below the standard (green category) . By utilizing FOSSNIRS data across five material categories, the proposed model is expected to provide precise predictions and support decision-making in palm oil production processes. The results of the study indicate that applying machine learning methods to FOSS-NIRS data can enhance the accuracy of oil loss classification, making it a potential solution for broader implementation in the palm oil processing industry to optimize production efficiency.  \nKeywords-Oil, Palm Oil, Losses, FOSS-NIRS.  \nINTRODUCTION  \nThe palm-oil industry is a mainstay of many national economies—Indonesia’s in particular—yet much of the value it creates still leaks away through residual oil left in final effluent, empty-fruit bunches, kernels, pressed fibre, and decanter solids. Those losses eat into profit margins and weaken long-term factory sustainability [1] . Furthermore, such inefficiencies not only lead to financial waste but also exacerbate the environmental footprint of palm oil processing, contributing to deforestation and habitat loss [2] . Oil losses in the palm oil processing industry represent a significant challenge to production efficiency, particularly concerning materials such as Final Effluent, Empty Fruit Bunches (EFB), Kernels, Pressed Fiber, and Decanter Solids. These losses negatively impact the economic viability of palm oil production and worsen the environmental footprint of the industry. Effective monitoring and reduction of oil losses can be supported through advanced detection technologies combined with analytical machine learning methods.  \nThe palm oil industry has been criticized for its substantial land-use changes, particularly the clearing of peatlands for oil palm cultivation. This transformation contributes to significant greenhouse gas emissions and biodiversity loss [3] . Processing stages susceptible to oil loss include high-pressure steam treatments, where oil may be retained in biomass fractions like EFB, which is known to contain a quantifiable amount of recoverable oil [1] . Free and Open Source Software Near Infrared Spectroscopy (FOSS-NIRS) technology has been widely used to detect oil content quickly and non-destructively. However, manual interpretation ofFOSS-NIRS data is often inefficient for fast and accurate decision-making. A recent study concludes that while NIRS  \nis capable of measuring oil loss effectively, integrating it wit","cbCaipKYD6gsz2Uh","https://ap.wps.com/l/cbCaipKYD6gsz2Uh","pdf",628103,1,12,"English","en",105,"# Introduction\n## Oil losses in palm oil processing\n## FOSS-NIRS for oil content monitoring\n## Machine learning integration","[{\"question\":\"What problem does the document address in palm oil processing?\",\"answer\":\"It addresses oil losses in multiple processing materials that reduce production efficiency and worsen economic and environmental impacts.\"},{\"question\":\"How does FOSS-NIRS contribute to the solution?\",\"answer\":\"FOSS-NIRS provides fast, non-destructive detection of oil content based on near-infrared absorption spectra.\"},{\"question\":\"What is the main goal of the proposed machine learning model?\",\"answer\":\"To classify FOSS-NIRS data into above-standard and below-standard oil loss categories to improve prediction accuracy and support decision-making.\"}]","Classification of Oil Loss Levels in Palm Oil Processing Using Near-Infrared Spectroscopy with Machine Learning - Research summary | PDF",1785822286,30,{"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},"classification-of-oil-loss-levels-in-palm-oil-processing-using-near-infrared-spectroscopy-with-machine-learning-research-summary","",{"@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/classification-of-oil-loss-levels-in-palm-oil-processing-using-near-infrared-spectroscopy-with-machine-learning-research-summary/124436/",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","2026-08-04",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 problem does the document address in palm oil processing?","Question",{"text":75,"@type":76},"It addresses oil losses in multiple processing materials that reduce production efficiency and worsen economic and environmental impacts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does FOSS-NIRS contribute to the solution?",{"text":80,"@type":76},"FOSS-NIRS provides fast, non-destructive detection of oil content based on near-infrared absorption spectra.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main goal of the proposed machine learning model?",{"text":84,"@type":76},"To classify FOSS-NIRS data into above-standard and below-standard oil loss categories to improve prediction accuracy and support decision-making.","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"]