Determination of Optimum Clusters for Grouping Villages Based on Education Levels Using the K-Means Method (Case Study: Patimpeng District, Bone Regency)

Abstract

Patimpeng Subdistrict has residents with different levels of education with different educational problems. Therefore, different solutions are needed related to educational problems to improve the quality of education for the residents of Patimpeng Subdistrict. One of the information about the level of education in each village can be known through the number of graduates for each level of education. In order to classify the level of education, an appropriate method is needed that can make it easier to classify the level of education in each village. For this reason, the authors propose the optimum cluster method with the k-means algorithm for the number of graduates from various levels of education. The method used in making this system is using the waterfall method. Making this application uses PHP as the programming language using algorithms and using the Laravel framework and for system testing using blackbox testing and SUS (System Usability Scale). The results of this research are that the optimum cluster determination system for grouping villages based on education level using the k-means method has been successfully created. In addition, the results were obtained from the SUS (System Usability Scale) test with 80.71, which means that the value is an adjective rating of "Excellent".