Optimization of k-means clustering using particle swarm optimization algorithm for human development index
DOI:
https://doi.org/10.31763/businta.v8i1.678Keywords:
human development index, k-means clustering, particle swarm optimizationAbstract
K-Means algorithm can be used to cluster the Human Development Index in East Java in particular for the people, the hope is that with this development all the problems that exist in the community including poverty, unemployment, school dropouts, health and social inequality can be resolved. However, this algorithm has a weakness that is sensitive to the determination of the initial centroid. Initial centroids that are determined randomly will reduce the level of accuracy, often get stuck at the local optimum, and get random solutions. Optimization algorithms such as PSO can overcome this by determining the optimal initial centroid. The quality of clusters produced by K-Means algorithm with and without PSO algorithm is measured using the average Silhouette Coefficient (SC). In this study, better accuracy was obtained between pure kmeans and PSO based kmeans where the comparison value of pure kmeans was 0.27% while PSO based kmeans obtained a value of 0.34%. The Human Development Index data set was obtained from the official website of the Central Bureau of Statistics and used as secondary data in this study, especially the East Java region. In addition to program planning in the following year, the clustering carried out from 2019 to 2022 is also an evaluation of the East Java Provincial Government's program targets that have been implemented in that year, especially related to the human quality of life development program.
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