Menuju Condition Based Maintenance yang Cerdas: Systematic Review atas Integrasi IoT dan Mesin Learning pada Alat Berat
Keywords:
Condition-Based Maintenance, Internet of Things, Machine Learning, Alat Berat, Pemeliharaan Prediktif, Pemeliharaan CerdasAbstract
Condition-Based Maintenance (CBM) telah merevolusi manajemen aset dengan memungkinkan pengambilan keputusan berbasis data secara real-time, khususnya di sektor alat berat. Integrasi teknologi Internet of Things (IoT) dan algoritma Machine Learning (ML) membuka peluang baru untuk diagnosis prediktif, deteksi anomali, dan optimasi pemeliharaan pada unit alat berat. Artikel ini menyajikan Systematic Literature Review (SLR) menggunakan metode PRISMA-based 4-step selection mengenai bagaimana IoT dan ML diintegrasikan secara arsitektural dan fungsional dalam sistem CBM untuk alat berat, dengan mengidentifikasi tren utama, tantangan, dan kesenjangan penelitian. Sebanyak 130 sumber ilmiah yang diterbitkan antara tahun 2015–2025 dianalisis secara sistematis, menyoroti kemajuan dalam penerapan sensor, pemrosesan data real-time, pemodelan prediktif, dan strategi pemeliharaan terdesentralisasi. Tantangan utama yang ditemukan mencakup 1) kualitas data, 2) keterbatasan skalabilitas, 3) adaptabilitas model, serta 4) keamanan siber. Arah penelitian di masa depan diusulkan dengan menekankan pentingnya preprocessing, edge computing, skema pembelajaran ML secara online, pendekatan model hibrid untuk ML, dan infrastruktur IoT yang aman. Studi ini bertujuan untuk memberikan pandangan bagi peneliti dan praktisi dalam memahami pengembangan ekosistem CBM yang tangguh, terukur, dan cerdas khususnya untuk aplikasi industri berat.
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