KLASIFIKASI KELAYAKAN PENERIMA BANTUAN SEMBAKO MENGGUNAKAN METODE DECISION TREE - Rizaldy, Farhan; Suprapti, Tati; Dwilestari, Gifthera (PELITA JURNAL PENELITIAN DAN KARYA ILMIAH, 2025)
The K-Means method is one of the Data Mining methods that is widely used in clustering research. Based on the results of research that has been conducted to build a process model for Poverty Data Clustering Analysis Using the K-Means Method Approach in Teluk Agung Village, Indramayu District, Indramayu Regency, can use RapidMiner tools by creating operators and processing parameters used for clustering the P3KE class category. The operators used in this study are Read Excel, Set Role, Select Attributes, Replace Missing Values, Nominal to Numerical, Multiply, Clustering (K-Means) and Performance operators. The operators used are 8 operators by applying the stages of Knowledge Discovery in Database (KDD). This research will apply the Davies Bouldin Index (DBI) as a way of optimising the number of clusters to group data, from the best cluster value experiment, the closest to 0 is K9 with a DBI value of -2.257, from this we can conclude that approximately 43 items from clusters 2 - 10 are included in the P3KE category, and other than the 43 items can be interpreted as still not included in the P3KE category.