[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120008-id":3,"doc-seo-120008-113":31,"detail-sidebar-cat-0-id-113":88},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},120008,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",52,"Teknologi","Predictive Sparepart Maintenance Menggunakan Algoritma Machine Learning Extreme Gradiant Boosting Regressor","Sparepart berperan penting dalam menjaga kinerja kendaraan, dan penggantian yang tidak tepat dapat menimbulkan downtime serta ketidakefisienan operasional layanan purna jual. Dokumen ini membahas upaya predictive sparepart maintenance dengan analisis machine learning untuk memprediksi waktu penggantian suku cadang, dengan fokus pada suku cadang baterai. Regresi menggunakan Extreme Gradient Boosting Regressor (XGBoost Regressor) diterapkan berdasarkan indikator tertentu. Hasil prediksi menghasilkan R2-Score train 90% dan test 87%, untuk mendukung perencanaan layanan preventif, meningkatkan kepuasan pelanggan, serta mengoptimalkan persediaan.","Predictive Sparepart Maintenance Menggunakan Algoritma Machine Learning Extreme Gradiant Boosting Regressor  \n1Syahrul Usman, 2Rahmat Fuadi Syam  \n[Syahrul.usman@unpacti.ac.id](Syahrul.usman@unpacti.ac.id), [rahmat@unpacti.ac.id](rahmat@unpacti.ac.id)  \n1,2Ilmu Komputer, Universitas Pancasakti, Makassar  \nAbstrak  \nSparepart merupakan komponen penyusun dari suatu kesatuan benda yang memiliki fungsitertentu, pada kendaraan mobil, sparepart berfungsi dalam menjaga kinerja dan fungsi kendaraan, Predictive Sparepart Maintenance merupakan upaya untuk meningkatkan efisiensi operasional, pelayanan pelanggan, dan mengurangi waktu henti kendaraan melalui penerapan analisis dan algoritma machine learning untuk memprediksi waktu penggantian suku cadang, dengan fokus pada suku cadang baterai. Penerapan machine learning dapat dilakukan untuk melakukan prediksi terhadap waktu maintenance suku cadang mobil, dimana salah satu algoritma yang bisa dipakai adalah XGBoost Regressor. Melalui pendekatan ini, penelitian ini bertujuan untuk memperbaiki perencanaan layanan dengan melakukan prediksi waktu penggantian suku cadang berdasarkan indikator tertentu, Dengan implementasi penelitian ini, diharapkan dapat meningkatkan efisiensi operasional pada layanan purna jual otomotif, meningkatkan kepuasan pelanggan, mengurangi downtime kendaraan, dan memperbaiki perencanaan layanan secara keseluruhan serta yang terpenting bisa memberikan informasi yang bersifat pemeliharaan preventif kepada pelanggan, penelitian ini memberikan hasil prediksidengan nilai R2-Score sebagai berikut data train: 90%, Test: 87%  \nKata Kunci: Predictive, Machine_Learning, XGBoost Regressor, Sparepart, Purna Jual  \nAbstract  \nSpare parts are components that make up a single object that has a specific function. In car vehicles, spare parts have the function of maintaining the performance and function of the vehicle. Predictive Spare Part Maintenance is an effort to improve operational efficiency, customer service, and reduce vehicle downtime through the application of analysis and machine learning algorithms to predict spare part replacement times. A machine learning approach can be used to predict maintenance times for car spare parts, where one of the algorithms that can be used is XGBoost Regressor. Through this approach, this research aims to improve service planning by predicting spare part replacement times based on certain indicators, With the implementation of this research, it is hoped that it can increase operational efficiency in automotive after-sales services, increase customer satisfaction, reduce vehicle downtime, and improve overall service planning and most importantly can provide preventive maintenance information to customers. This research provides prediction results with R2-Score values as follows: train data: 90%, Test: 87%  \nKata Kunci: Predictive, Machin Learning, XGBoost Regressor, Sparepart, After Sales  \n1. Pendahuluan  \nSparepart merupakan komponen penyusun dari suatu kesatuan benda yang memiliki fungsitertentu(Ovilianda & Ginting, 2021) . Fungsi setiap sparepart berbeda-beda dan dapat saling terikat satu sama lain. Berdasarkan fungsi tersebut, suatu sparepart memiliki peran yang penting dalamberjalannya alur kerja suatu alat atau barang, termasuk pada kendaraan bermotor, yaitu mobil (Sampul et al., 2021) .  \nPada kendaraan mobil, sparepart berfungsi dalam menjaga kinerja dan kegunaan kendaraan. Beberapa contoh sparepart pada mobil diantaranya baterai, kampas rem, packing drain, busi, suspensi, filter oli, dan lainnya (Cadang et al., 2019; Usman et al., 2022) . Agar kendaraandapat terus optimal, penggunaan sparepart yang berkualitas dan perawatan yang teratur perludilakukan.  \nPerkembangan ilmu teknologi yang begitu pesat merambah ke berbagai bidang, termasukpada maintenance sparepart kendaraan mobil. Salah satu pendekatan yang dapat dilakukan agar mengurangi waktu pengecekan adalah dengan predictive maintenance, dimana jika kendaraan belum layak secara p","cbCaisE7NVlZsbDk","https://ap.wps.com/l/cbCaisE7NVlZsbDk","pdf",392651,3,1,10,"Indonesian","id",113,"# Pendahuluan\n## Konsep sparepart dan perannya pada kendaraan\n## Predictive maintenance untuk mengurangi waktu pengecekan\n## Penerapan machine learning dan regresi\n## Rumusan masalah\n## Dampak bagi layanan purna jual otomotif\n## Penelitian terkait","[{\"question\":\"Algoritma apa yang digunakan untuk memprediksi waktu maintenance suku cadang?\",\"answer\":\"Pendekatan menggunakan regresi dengan algoritma Extreme Gradient Boosting Regressor (XGBoost Regressor).\"},{\"question\":\"Berapa hasil R2-Score dari model prediksi pada data train dan test?\",\"answer\":\"Nilai R2-Score pada data train sebesar 90% dan pada data test sebesar 87%. \"}]","Predictive Sparepart Maintenance Menggunakan Algoritma Machine Learning Extreme Gradiant Boosting Regressor | PDF",1785727685,15,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":83,"head_meta":85,"extra_data":87,"updated_unix":29},"predictive-sparepart-maintenance-using-machine-learning-algorithm-extreme-gradiant-boosting-regressor","",{"@graph":37,"@context":82},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/id/document/teknologi/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/predictive-sparepart-maintenance-using-machine-learning-algorithm-extreme-gradiant-boosting-regressor/120008/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-15","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78],{"name":73,"@type":74,"acceptedAnswer":75},"Algoritma apa yang digunakan untuk memprediksi waktu maintenance suku cadang?","Question",{"text":76,"@type":77},"Pendekatan menggunakan regresi dengan algoritma Extreme Gradient Boosting Regressor (XGBoost Regressor).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Berapa hasil R2-Score dari model prediksi pada data train dan test?",{"text":81,"@type":77},"Nilai R2-Score pada data train sebesar 90% dan pada data test sebesar 87%.","https://schema.org",{"og:url":52,"og:type":84,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":86,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":89},[90,95,99,103,107,111,115,119,121,125,129],{"id":91,"doc_module":4,"doc_module_name":47,"category_name":92,"show_sort_weight":93,"slug":94},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":93,"slug":98},48,"Cerita & Novel","story-novel",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":93,"slug":102},56,"Gaya Hidup","lifestyle",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":93,"slug":106},51,"Komik","comic",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":93,"slug":110},53,"Layanan Kesehatan","healthcare",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":93,"slug":114},54,"Penelitian & Laporan","research-report",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":93,"slug":118},49,"Sastra","literature",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":93,"slug":120},"technology",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":93,"slug":124},50,"Ujian","exam",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":93,"slug":128},57,"Umum","general",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":4,"slug":132},181,"Formulir","formulir"]