Menentukan Cluster Yang Tepat Dengan K-Means Dalam Rangka Mengukur Efektivitas Pelaksanaan Anggaran Pada Kementerian Agraria Dan Tata Ruang/Badan Pertanahan

Abstract

The effectiveness of budget implementation is one of the benchmarks for the success of a Ministry/Agency in implementing its programs, activities and expenditures in accordance with a predetermined plan. The problem faced is that the achievement in budget execution is often not optimal, one of which is caused by the determination of inappropriate K/L budget allocations resulting in the implementation of activities that are not in accordance with the plan, the realization of budget absorption is not optimal and has the potential to cause idle cash. For this reason, it is necessary to do a mapping in order to identify the main causes or constraints in budget execution using the clustering method. This study tries to find the right cluster with the K-Means algorithm clustering method. The expected results are finding the right cluster model in measuring the effectiveness of budget implementation that can be used when determining budget allocations and preventing idle cash in the budget of the Ministry of Agrarian and Spatial Planning.

Comparison Analysis of K-Means and DBSCAN Algorithms for Improving Budget Absorption Efficiency in EIS

“…They explored how this algorithm could be a crucial instrument in anticipating and managing credit risks. In line with this, another study by Murti Suyoto, Rachmadi, and Parulian (2022) remained within the context of using the K-Means algorithm, this time to measure the effectiveness of budget implementation in the Ministry of Agrarian and Spatial Planning/National Land Agency. Overall, the findings from these studies synergize to enrich the understanding of the role of clustering algorithms, particularly K-Means, in governmental sector budget management.…”

“…For this research, the realized budget achievement data from each regional device are used as the primary data for analysis. Before conducting the experiments, the data undergo a preprocessing stage to remove outliers and apply normalization processes to ensure that the data is ready for further analysis (Murti Suyoto, Rachmadi, & Parulian, 2022). Afterward, both algorithms, K-Means and DBSCAN, are applied to the realized budget achievement data to generate clusters that correspond to their respective characteristics.…”

“…Focused on the village of Dermaji, the study aimed to comprehend how EIS could serve as a pillar in budget management at the village level. Furthermore, Murti Suyoto, Rachmadi, and Parulian (2022) highlighted the application of the K-Means algorithm to determine appropriate clusters in measuring the effectiveness of budget implementation in the Ministry of Agrarian and Spatial Planning/National Land Agency. This study contributed to understanding how data clustering with K-Means could assist in evaluating budget implementation within governmental institutions.…”