295 KEYWORDS Adaptive learning, operator behavior, telematics, machine learning, productivity optimization, mining operations INTRODUCTION Open pit coal mining is a method to remove the overburden or waste covering the coal deposit and to get the coal deposit that occurred on earth surface. The overburden is loaded to truck using excavator and transported to disposal area. Once the overburden has completely been removed, then the exposed coal is mined and transported to coal stock pile. The process to remove overburden is known as overburden or waste removal. While the process to mine the exposed coal is known as coal getting. However, in open pit mining contractor business process, waste removal accounts the bigger portion than coal getting. In PT. Pamapersada Nusantara, waste removal contributes for about 80% of revenue. Thus, enhancing the efficiency and effectiveness of waste removal process is vital to boost business performance. Trucks (haulers) and excavators (loaders) are the main entity in overburden removal for open pit coal mine. The excavator digs the broken material of overburden, which previously has been drilled and blasted, at the front area. Each excavator is supplied with several trucks which constantly travel to front and ready to receive the overburden material to be filled in its bucket. Once the truck’s bucket is full, the truck will haul the material to designed disposal area. Pair of a loader with several trucks in a same objective to load and haul overburden from front to disposal area is known as fleet. Operational inefficiency particularly suboptimal speed selection of the trucks can contribute to increased cycle times, higher fuel consumption, brake and tire wear, vehicle overheating, and production delays. Current systems often use fixed speed limits or rule-based recommendations without adapting to real-time vehicle conditions (payload, grade, weather, operator behavior). As mining environments change daily, static models fail to deliver optimal guidance. Adaptive Machine Learning (Adaptive ML) integrates new data in near-realtime, retraining predictive models to reflect the latest road conditions and operator performance. This enables dynamic operator coaching that improves productivity and safety. Truck operators require optimal speed guidelines tailored to road grade and curvature, payload, engine RPM and power capability, rolling resistance, weather and road deterioration, operator behavior which the traditional guidance systems cannot adapt to this complexity. This research aims to design and evaluate a system that: 1. Segments the haul road automatically using GPS. 2. Computes correlations between speed, grade, acceleration, payload, curvature,
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