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All Journal Jurnal Teknologi Pendidikan Jusikom: Jurnal Sistem Informasi Ilmu Komputer Majalah Ilmiah Kaputama REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer MEANS (Media Informasi Analisa dan Sistem) Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Media Informatika JURNAL TEKNOLOGI INFORMASI & KOMUNIKASI DALAM PENDIDIKAN Innovative: Journal Of Social Science Research Jurnal Sistem Informasi dan Teknologi Jaringan Jurnal Pengabdian Masyarakat (Jubdimas)
Hasugian, Penda Sudarto
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : Jurnal Sistem Informasi dan Teknologi Jaringan

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Pengembangan Model Predictive Maintenance Untuk Kendaraan Menggunakan Algoritma Pembelajaran Mesin Hasugian, Penda Sudarto; Kumar, Prasanth; Aispriyani; Sagala, Jijon Raphita
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 5 No 2 (2024): September
Publisher : CV. ADMITECH SOLUTIONS

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Abstract

Vehicle maintenance is an important aspect to ensure optimal performance and vehicle life. Conventional maintenance approaches based on time or mileage (Time-Based Maintenance) are often ineffective, because they do not consider the actual condition of the vehicle. Predictive Maintenance supported by machine learning algorithms offers a more accurate solution in detecting potential vehicle damage before failure occurs, so that maintenance can be carried out according to actual needs. This study aims to develop a machine learning-based predictive maintenance model with a case study at the Payung Auto Solution workshop, which leads to the repair of Nissan, Datsun, and other vehicle brands. The methods used in this study include collecting operational data and vehicle maintenance history at Payung Auto Solution. This data is analyzed and processed using machine learning algorithms, such as Random Forest and Neural Network, to build a predictive model that is able to identify damage patterns in vehicle components. This model is tested and evaluated using prediction accuracy metrics, to determine the effectiveness of the model in predicting maintenance needs.
Co-Authors Aispriyani Amallia, Dwi Novia Andika, Dhini Andriani, Santy Arikhifo, Arikhifo Bosker Sinaga Breukelen, Melisa Van Chandra Setiawan, Chandra Erwin Panggabean Eviyanti Br Barus, Eviyanti Br Barus Fitri Amalia Ginting, Feryanto Harefa, Jikarni Hutahaean, Harvei Desmon Jijon Raphita Sagala, Jijon Raphita Khairunnisa, Putri Kumar, Prasanth M, Melia Manurung, Oktaviani MARA UNTUNG RITONGA, MARA UNTUNG Marpaung, Endra Ary Prasasty N, Anisa Nantheni, W.S.Usha Nasution, Rizki Fadila Nuraisana, Nuraisana Romili Sitanggang, Kristiani Saragih, Diana Sari Muliani Sianipar, Baringin Sihotang, Hengki Tamando Simangunsong, Agustina Simangunsong, AgustinaS Simanjorang, R. Magdalena Simanjorang, R. Mahdalena Simbolon, Liber Sinaga, Dedi Candro Parulian Sitohang, Amran SITORUS, IRMA YOLANDA Sulindawaty T, Eliska Tarigan, Devri Wira Apriani