Machine learning-based detection of underreported occupational injuries and diseases for sustainable occupational health surveillance in Indonesia

Abstract

Occupational injury and disease surveillance plays an important role in supporting prevention-oriented occupational safety and health (OSH) governance. However, under-reporting of occupational injuries and diseases remains a persistent challenge, particularly in emerging economies where surveillance systems are often fragmented and administratively oriented. This study examines potential under-reporting patterns within Indonesia's occupational surveillance system across five industrial sectors, manufacturing, mining, palm oil, fisheries, and telematics, during 2019–2024. The study employed a quantitative analytical design integrating administrative claims analysis, Random Forest classification, K-Means clustering, and regulatory review using de-identified data from BPJS Ketenagakerjaan. The findings reveal substantial disparities between occupational injury (JKK) and occupational disease (PAK) reporting across sectors. Manufacturing recorded the highest occupational injury burden, while occupational disease claims remained limited in all sectors. All recorded PAK cases in manufacturing, mining, and palm oil were classified under non-specific "lain-lain" coding categories, whereas fisheries and telematics reported no occupational disease claims during the study period. Random Forest analysis identified diagnosis specificity, JKK–PAK ratio imbalance, and sectoral characteristics as the variables most strongly associated with potential under-reporting signals. K-Means clustering further categorized sectors into distinct surveillance typologies, distinguishing sectors characterized by minimal occupational disease visibility and limited diagnosis diversity from sectors with comparatively lower occupational risk profiles. The study suggests that potential under-reporting within Indonesia's occupational surveillance system may involve not only missing cases but also limited diagnostic specificity and inconsistent disease attribution. The integration of machine learning analytics and administrative surveillance data demonstrates the potential of data-driven approaches for identifying hidden reporting inconsistencies and supporting more prevention-oriented occupational health governance.