Instrumen Situation Awareness pada Industri Pertambangan: Systematic Literature Review Pendekatan Human-Centered dan Machine Learning
Abstract
Industri pertambangan merupakan salah satu sektor dengan tingkat risiko keselamatan kerja tertinggi. Human factors dan human error diidentifikasi sebagai penyebab utama kecelakaan, terutama seiring transformasi industri menuju smart mining yang meningkatkan kompleksitas interaksi manusia, mesin, dan lingkungan kerja. Penelitian ini bertujuan mengidentifikasi dan menganalisis instrumen Situation Awareness (SA) yang digunakan dalam konteks operasional pertambangan, memetakan karakteristik pendekatan berbasis manusia (human-centered) dan teknologi cerdas (Machine Learning), serta mengevaluasi kekuatan dan keterbatasan masing-masing pendekatan dalam mendukung keselamatan kerja. Penelitian menggunakan pendekatan Systematic Literature Review (SLR) mengacu pada panduan PRISMA dengan kerangka PICOC. Pencarian dilakukan pada database Scopus dan PubMed menggunakan kombinasi kata kunci terstruktur, dengan kriteria inklusi artikel tahun 2016–2026 yang membahas instrumen SA di lingkungan pertambangan. Sebanyak 8 artikel memenuhi kriteria inklusi. Hasil sintesis menunjukkan pergeseran paradigma SA dari pendekatan human-centered assessment menuju cyber-physical awareness yang mengintegrasikan machine learning, sensor fusion, IoT, LiDAR, dan computer vision. Pendekatan berbasis ML/AI mendominasi dengan keunggulan pada hazard detection dan monitoring real-time, sedangkan pendekatan human-centered unggul dalam cognitive assessment. Tingkat validasi instrumen masih bervariasi, dengan sebagian besar berada pada tahap prototipe atau simulasi. Instrumen SA berpotensi mendukung transformasi keselamatan pertambangan menuju pendekatan yang lebih proaktif dan prediktif. Pengembangan ke depan perlu diarahkan pada integrasi human-AI collaboration, penguatan validasi lapangan, dan implementasi operasional jangka panjang.
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