Modeling Optimization of Sugarcane Transportation Routes in East Java Province
DOI:
https://doi.org/10.53572/ejavec.v10i1.167Keywords:
Sugarcane, Transportation, Big Data, Machine Learning, Efficiency, Economic Growth, East JavaAbstract
East Java makes a significant contribution to the national sugar industry, accounting for 47.34% of Indonesia’s total sugar production during the 2018-2022 period. Given East Java’s crucial position in the national sugar supply chain, efforts are needed to continually develop all aspects of improvement in this sector. This research aims to identify existing sugarcane transportation routes, determine optimal routes for sugarcane transportation, and measure efficiency and its contribution to the economy in East Java Province. Employing GIS, big data, and machine learning approaches, this study models existing routes and proposes more efficient optimal routes through a clustering approach. The results indicate that implementing optimal routes can reduce the average distance of sugarcane transportation by up to 63%, which translates to significant fuel cost savings. This efficiency also enables a reduction in sugarcane harvesting and transportation costs by approximately 12% and sugarcane production costs by approximately 3%. This study highlights the importance of synchronizing transportation policies and improving road infrastructure to support sugarcane logistics efficiency.
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