Unsafe Conditions Identification Using Social Networks in Power Plant Safety Reports

Abstract

Power plants in Indonesia grapple with significant challenges in managing occupational health and safety. Power generation companies urgently need to reduce workplace accidents every year and need an application for reporting every potential workplace hazard. The huge reporting data in applications such as IZAT requires thorough analysis to find out the pattern and distribution. This research aims to facilitate the company in hazard mitigation by identifying reported unsafe conditions and building a semantic association network to understand the nature of unsafe conditions between Paiton and Indramayu generating units. The research method uses social network analysis, which is carried out by preprocessing the data using programming to remove noise and then converting the data into a readable format. Then, semantic relationships between words were analyzed, and the data was visualized using the ForceAtlas2 algorithm. The findings revealed a different focus between the two units, where 6.597 reports from the Paiton generating unit mainly highlighted team response and accident-prone workplace conditions, while 5.840 reports from the Indramayu unit emphasized specific conditions, locations, and equipment that pose accident risks