Optimization of MSMEs promotion strategy using GIS with random forest algorithm and A-star algorithm

Abstract

Micro, small, and medium enterprises (MSMEs) make a main contribution to improving a country’s economy, but it still faces various problems including limited market access, ineffective promotion, and lack of understanding in business profiles. The purpose of this study is to design a geographic information system (GIS) combined with the random forest (RF) algorithm and the A-star algorithm. The RF algorithm functions are to classify MSMEs. Meanwhile, the A-star algorithm is to recommend the shortest route to support accessibility. In addition, it is equipped with a dashboard and MSMEs profile as a reference for stakeholders in making MSMEs development policies and as information to the public in order to expand the reach of promotion in introducing MSMEs and attracting investors, thus optimizing MSMEs promotion strategies. In addition, the evaluation results of the classification model show an accuracy value of 99.6%, indicating that the model is able to predict very well, making it highly recommended for MSME classification.