Segmentasi Pengadilan Berdasarkan Jumlah Putusan Dan Lokasi Menggunakan Algoritma Hierarchical Clustering
Abstract
The grouping of court data in Indonesia provides a significant understanding of the pattern of workload distribution in judicial institutions. To identify court characteristics, the grouping method in data mining is used by applying the Hierarchical Clustering algorithm. The dataset used was obtained from Kaggle and includes information on the number of judgments and court locations in various provinces. The data is processed through the cleanup stage to address missing values, data type conversion, and normalization so that numerical attributes are at a uniform scale and ready for analysis. The results of the grouping divided the courts into two main categories, namely the group with a very low number of judgments and the group with a medium to high number of judgments. This segmentation provides a clearer picture of the distribution of workloads between regions and can be used by stakeholders to support strategic planning, resource distribution, and the development of more effective and equitable judicial policies. This approach is expected to be able to improve the efficiency and equity of the judicial system in Indonesia.
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