Application of the K-Means Algorithm for the Grouping of Regional Income Patterns in Kudus Regency - Alif Miftachul Nasikhah; Ade Ima Afifa Himayati; Findasari Findasari (Journal of Engineering and Science Application, 2026)
Regional revenue is one of the indicators of regional financial ability that needs to be analyzed to determine the realization of revenue. Analyses that are still descriptive have not been able to group data based on similar characteristics. This study aims to apply the K-Means algorithm to classify the regional income pattern of Kudus Regency based on monthly income realization data. The research uses a quantitative approach with secondary data in the form of the realization of regional revenue in Kudus Regency in 2020–2024 obtained from the Regional Revenue, Finance, and Asset Management Agency (BPPKAD) of Kudus Regency. Data processing is carried out using the RapidMiner application through the Read Excel, Set Role, Normalize, K-Means Clustering, and Performance stages, The number of clusters is set to three (k = 3). The results of the study showed that the K-Means algorithm succeeded in grouping data into three Cluster 0 clusters consisting of 4 data, namely September, October, November, and December. Cluster 1 consists of 1 data, namely August, while Cluster 2 consists of 7 data, namely January, February, March, April, May, June, and July.  Centroid analysis showed that each cluster had different characteristics, while evaluation using Performance Vector yielded a Davies-Bouldin Index value of -0.444 which showed good grouping results based on the RapidMiner evaluation. The results of this study are expected to be supporting information in the evaluation and planning of regional revenue management in Kudus Regency