AIRAAIRA

Journal of Information Systems and Technology ResearchJournal of Information Systems and Technology Research

The coal production and distribution industry faces persistent challenges in data management, operational coordination, and decision-making efficiency. Conventional monitoring methods often result in delayed reporting, low data accuracy, and limited adaptability to dynamic market demands. This study addresses the lack of an intelligent and integrated information system by designing and developing a real-time IoT-based solution for coal production and distribution management. The system was built using the Software Development Life Cycle (SDLC) with the Waterfall model and integrates IoT sensors to automatically capture critical parameters such as pressure, temperature, and coal quality indicators. Artificial Intelligence (AI) components were incorporated to enhance data analysis and support predictive decision-making. System evaluation through simulation with dummy data demonstrated notable improvements, including a 40% reduction in reporting response time and a 95% increase in operational data accuracy. The system also enabled faster production monitoring, streamlined distribution processes, and provided decision-makers with reliable real-time insights. User feedback confirmed the systems effectiveness in improving accessibility, monitoring efficiency, and overall operational performance in coal production and distribution management.

This study successfully designed, developed, and evaluated a real-time IoT-based integrated information system to address inefficiencies in coal production and distribution management.The system demonstrated significant performance improvements, reducing reporting response time by up to 40% and increasing data accuracy to 95%.These findings underscore the importance of adopting digital transformation technologies like IoT and AI in the coal industry to enhance operational efficiency, safety, and long-term competitiveness.

Berdasarkan hasil penelitian ini, beberapa saran penelitian lanjutan dapat dipertimbangkan. Pertama, penelitian lebih lanjut dapat dilakukan untuk menguji sistem ini dalam lingkungan operasional yang sesungguhnya di tambang batu bara, dengan mempertimbangkan tantangan konektivitas jaringan dan ketahanan perangkat keras. Kedua, pengembangan model prediktif berbasis AI dapat diperluas untuk memprediksi potensi kegagalan peralatan dan mengoptimalkan jadwal pemeliharaan, sehingga mengurangi waktu henti dan meningkatkan efisiensi produksi. Ketiga, integrasi sistem ini dengan teknologi lain seperti drone dan citra satelit dapat memberikan pemantauan yang lebih komprehensif terhadap area pertambangan, memungkinkan deteksi dini masalah lingkungan dan peningkatan keselamatan kerja. Penelitian-penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam mendorong adopsi teknologi digital di industri pertambangan batu bara dan meningkatkan keberlanjutan operasionalnya.

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