[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120366-id":3,"doc-seo-120366-113":31,"detail-sidebar-cat-0-id-113":92},{"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},120366,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",54,"Penelitian & Laporan","Strategi Pemeliharaan Preskriptif - Optimalisasi Keandalan Mesin Berbasis Machine Learning Guna Mencegah Terjadinya Downtime pada Mesin Industri","Downtime pada mesin industri menimbulkan kerugian besar bagi produktivitas dan efisiensi operasional. Keterbatasan pada Corrective, Descriptive, Diagnostic, dan Predictive Maintenance membuat mitigasi downtime belum mencapai tingkat optimal. Penelitian ini menerapkan Prescriptive Maintenance berbasis Machine Learning dengan model XGBoost untuk memprediksi probabilitas kegagalan sekaligus merekomendasikan langkah korektif menuju zero downtime. Dataset AI4I 2020 digunakan dengan preprocessing Min-Max Scaling, SMOTE, korelasi, VIF, serta deteksi outlier Z-Score dan IQR. Evaluasi memperoleh akurasi 98.12%, precision 97.95%, recall 98.23%, dan AUC 99.59, didukung IoT dan basis data realtime untuk meningkatkan keandalan operasional serta mengurangi downtime tak terduga.","Strategi Pemeliharaan Preskriptif: Optimalisasi Keandalan Mesin Berbasis Machine Learning Guna Mencegah Terjadinya Downtime pada Mesin Industri  \nBima Bagus Setyobudi*1  \n1Departemen Teknik Mesin, Fakultas Teknologi Industri dan Rekayasa Sistem, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia  \nE-mail: [5007221221@students.its.ac.id](5007221221@students.its.ac.id1)[1](5007221221@students.its.ac.id1)  \nAbstrak. Downtime pada mesin industri dapat menyebabkan kerugian yang signifikan dalamproduktivitas dan efisiensi operasional. Berbagai metode pemeliharaan seperti Corrective, Descriptive, Diagnostic, dan Predictive Maintenance memiliki keterbatasan dalam mengoptimalkan strategi mitigasi downtime. Oleh karena itu, penelitian ini mengimplementasikan Prescriptive Maintenance berbasis Machine Learning (XGBoost) untuk tidak hanya memprediksi kegagalan mesin tetapi juga memberikan rekomendasi langkah korektif guna mencapai zero downtime. Dataset AI4I 2020 Predictive Maintenance digunakansebagai sumber data, dengan menerapkan berbagai teknik preprocessing, seperti Min-Max Scaling, SMOTE, Heatmap Korelasi, VIF, serta deteksi outlier menggunakan Z-Score dan IQR. Model XGBoost dilatih untuk memprediksi probabilitas kegagalan mesin, yang kemudian dianalisis menggunakan Feature Importance untuk mengidentifikasi penyebab utama kegagalan. Evaluasi model menunjukkan akurasi 98.12%, precision 97.95%, recall 98.23%, dan AUCScore 99.59%, membuktikan keandalan sistem dalam mendeteksi dan mengklasifikasikan kegagalan mesin. Dengan implementasi strategi ini yang didukung oleh IoT dan database realtime, industri dapat mengoptimalkan efisiensi pemeliharaan, mengurangi downtime tak terduga, serta meningkatkan keandalan operasional.  \nKata Kunci: Prescriptive Maintenance; Machine Learning; XGBoost; Zero Downtime.  \nAbstract. Downtime in industrial machinery can cause significant losses in productivity and operational efficiency. Various maintenance methods such as Corrective, Descriptive, Diagnostic, and Predictive Maintenance have limitations in optimizing downtime mitigation strategies. Therefore, this study implements Prescriptive Maintenance using Machine Learning (XGBoost) to not only predict machine failures but also provide corrective recommendations to achieve zero downtime. The AI4I 2020 Predictive Maintenance Dataset is utilized, incorporating several preprocessing techniques, including Min-Max Scaling, SMOTE, Correlation Heatmap, VIF, and outlier detection using Z-Score and IQR. The XGBoost model is trained to predict the probability of machine failure, which is further analyzed using Feature Importance to identify the root cause of failures. Model evaluation results demonstrate 98.12% accuracy, 97.95% precision, 98.23% recall, and a 99.59% AUC-Score, proving the system’s reliability in detecting and classifying machine failures. With the implementation of this strategy, supported by IoT and real-time databases, industries can optimize maintenance efficiency, reduce unexpected downtime, and enhance operational reliability.  \nKeywords: Prescriptive Maintenance; Machine Learning; XGBoost; Zero Downtime.  \n1. Pendahuluan  \nDowntime adalah periode ketika suatu sistem, mesin, atau perangkat tidak beroperasi atau tidak dapat digunakan. Dalam konteks industri, downtime dapat terjadi karena perawatan terjadwal, perbaikan, ataumasalah teknis yang tidak terduga. Kondisi ini dapat berdampak signifikan terhadap produktivitas danefisiensi operasional perusahaan [1]. Kasus downtime dapat terjadi di berbagai macam industri, terutama industri yang sangat bergantung pada peralatan mekanis, otomatisasi, atau sistem digital. Misalnya, industri manufaktur secara global tercatat mengalami rata-rata downtime mencapai 800 jam per tahun, yang mengakibatkan kerugian finansial sekitar 50 miliar USD akibat hilangnya produksi dan biaya perbaikan [2] . Selain itu, industri minyak dan gas juga mengalami dampak serupa, dengan rata-rata downtime yang dapat menyebab","cbCainyZLTIYW5bb","https://ap.wps.com/l/cbCainyZLTIYW5bb","pdf",626985,3,1,13,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang downtime pada industri\n## Keterbatasan metode maintenance yang ada\n## Urgensi pendekatan preskriptif berbasis machine learning\n## Konsep zero downtime","[{\"question\":\"Apa masalah utama yang dibahas dalam dokumen ini?\",\"answer\":\"Dokumen membahas dampak downtime pada mesin industri yang menyebabkan penurunan produktivitas dan efisiensi, serta kerugian finansial. Selain itu, dibahas keterbatasan metode maintenance yang ada dalam mengoptimalkan mitigasi downtime.\"},{\"question\":\"Bagaimana penelitian ini menggunakan Prescriptive Maintenance berbasis machine learning?\",\"answer\":\"Penelitian mengimplementasikan Prescriptive Maintenance menggunakan model XGBoost untuk memprediksi probabilitas kegagalan. Model kemudian dianalisis dengan feature importance untuk mengidentifikasi penyebab utama serta memberikan rekomendasi langkah korektif.\"},{\"question\":\"Dataset dan teknik preprocessing apa yang digunakan?\",\"answer\":\"Dataset yang digunakan adalah AI4I 2020 Predictive Maintenance. Teknik preprocessing meliputi Min-Max Scaling, SMOTE, heatmap korelasi, VIF, serta deteksi outlier menggunakan Z-Score dan IQR.\"}]","Strategi Pemeliharaan Preskriptif - Optimalisasi Keandalan Mesin Berbasis Machine Learning Guna Mencegah Terjadinya Downtime pada Mesin Industri | PDF",1785729686,20,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"prescriptive-maintenance-strategy-optimizing-machine-reliability-using-machine-learning-to-prevent-industrial-machine-downtime","",{"@graph":37,"@context":86},[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/penelitian-laporan/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/prescriptive-maintenance-strategy-optimizing-machine-reliability-using-machine-learning-to-prevent-industrial-machine-downtime/120366/",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",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Apa masalah utama yang dibahas dalam dokumen ini?","Question",{"text":76,"@type":77},"Dokumen membahas dampak downtime pada mesin industri yang menyebabkan penurunan produktivitas dan efisiensi, serta kerugian finansial. Selain itu, dibahas keterbatasan metode maintenance yang ada dalam mengoptimalkan mitigasi downtime.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Bagaimana penelitian ini menggunakan Prescriptive Maintenance berbasis machine learning?",{"text":81,"@type":77},"Penelitian mengimplementasikan Prescriptive Maintenance menggunakan model XGBoost untuk memprediksi probabilitas kegagalan. Model kemudian dianalisis dengan feature importance untuk mengidentifikasi penyebab utama serta memberikan rekomendasi langkah korektif.",{"name":83,"@type":74,"acceptedAnswer":84},"Dataset dan teknik preprocessing apa yang digunakan?",{"text":85,"@type":77},"Dataset yang digunakan adalah AI4I 2020 Predictive Maintenance. Teknik preprocessing meliputi Min-Max Scaling, SMOTE, heatmap korelasi, VIF, serta deteksi outlier menggunakan Z-Score dan IQR.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]