CLUSTERING MENGGUNAKAN ALGORITMA K-MEANS DAN DBSCAN PADA ANGGARAN PENDAPATAN DAN BELANJA DAERAH DI INDONESIA

Abstract

The regional government budget is an annual financial plan that affects the Indonesian economy for one year. Administration of APBD data has not been effectively implemented due to limited human resources. Clustering algorithms are used to group provinces based on regional government budget according to data similarities to facilitate the government in future financial planning. In this study using regional government budget data in 2021 using the k-means and DBSCAN methods. The results of this study using k-means with 2 clusters with cluster 1 contain of 33 provinces and cluster 2 contain of 1 province, that is DKI Jakarta. Meanwhile, using the DBSCAN method with 2 clusters, cluster 1 contain of 30 provinces, cluster 2 contain of 2 provinces there are Central Java and East Java, and 2 noise data, there are DKI Jakarta and West Java.