Publication Search

Measuring coal volume in the Stock ROM area played an important role in production control and mining evaluation. This research was conducted in the Stock ROM area at PT. Victor Dua Tiga Mega, where volume measurements were generally carried out using Total Station (TS) as the main method, howefer the use of Unmanned Aerial Vehicles (UAV) has begun to be implemented as a more efficient alternative. This study aims to compare the results of Stock ROM coal volume calculations using the Total Station and UAV methods. The research method was carried out by collecting data in the field, processing the digital Elevation Model (DEM), and calculating the volume using the Cut and Fill method. The results of the study the difference in volume between the two methods, where the UAV measurement results tend to be greater than those of the Total Station. The difference in Fine Coal volume was 724,15 m3 or 16,74% and Raw Coal volume of 9.335,98 m3 or 8,03%. Based on a comparison with weighing data, measurements using the Total Station provided results that were closer to the actual conditions in the field.

This study discusses the arrangement of queue lanes in the supervision of weighing freight transportation at UPPKB Singosari to improve service efficiency. Based on the analysis of the Minimum Service Standards (SPM), three main indicators, namely compliance, conformity, and implementation, have reached 100%, while the accuracy indicator has only reached 50% due to the duration of weighing exceeding the set time limit. The main factor causing long queues is the lack of clear lane markings, making it difficult for vehicles to be directed without an officer. In addition, the parking area of 3,330.6 m² has not been utilized optimally. Improvement efforts include the implementation of queue lane markings to reduce dependence on officers, optimization of facilities such as Pos 2 which is parallel to Pos 1, and improvement of human resource management to suit operational needs. The use of Weight in Motion (WIM) technology is also recommended to reduce workload and accelerate the supervision of freight transport vehicles. By implementing this strategy, it is hoped that services at UPPKB Singosari can be more efficient, transparent, and in accordance with applicable standards.

In this research, a machine learning approach, especially a decision tree model, is implemented to improve the analysis and visualization of weighbridge data in Indonesia. The evaluation results show that the decision tree model provides better insight in predicting the carrying capacity, dimensions and loading procedures of vehicles. The advantage of this model lies in its combination of low Mean Squared Error (MSE) and high R-squared, indicating its effectiveness in capturing data variance and providing accurate predictions. The use of decision tree models can be a valuable tool in improving the visualization of bridge weighing data, allowing users to gain additional insights and understand the complex dynamics within the data. In addition, the model's adaptability to various types of data makes it a versatile analysis tool. The positive implications of using this model open up opportunities to understand more deeply the logic of predictions and make more informed decisions. As a suggestion, increasing the number and quality of weighing equipment, wider application of information and communication technology, human resource training, and cross-sector collaboration can further strengthen weighbridge management in Indonesia.