Clustering Dataset Numerik Multivariat menggunakan Algoritma K-Means dengan Evaluasi Silhouette dan Davies–Bouldin Index - Mei Yanda Maharani Messakh; Jusuf Ezra Rudolf Fransz; David Hermanus; Adriana Fanggidae; Yulianto Triwahyuadi Polly (Jurnal Teknik Informatika dan Teknologi Informasi, 2026)
Clustering is a data mining technique used to group data based on similarity. This study applies the K-Means clustering algorithm to analyze multivariate numerical data from the Standard Minimum Service (SPM) dataset of Nusa Tenggara Timur Province. The dataset is structured and consists of six numerical attributes representing basic service sectors, namely education, health, public works, housing and settlement, public order and community protection, and social services. The research stages include data cleaning, data standardization using the Z-score method, determination of the optimal number of clusters, and clustering using the K-Means algorithm. The quality of the clustering results is evaluated using the Silhouette Score and Davies–Bouldin Index. The results show that the optimal number of clusters is three (k = 3), with a Silhouette Score of 0.327 and a Davies–Bouldin Index of 0.872. The clustering process produces three data groups with different characteristics based on the average values of each sector. These results indicate that the K-Means algorithm can be used to group SPM data into meaningful clusters based on similarity patterns.