https://jurnal.stiekrakatau.ac.id/index.php/rmaps <div align="justify"> <p align="justify">Published by Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM), Sekolah Tinggi Ilmu Ekonomi Krakatau, Review of Multidisciplinary Academic and Practice Studies (RMAPS) is an online and open-access academic journal focusing on critical and original multidisciplinary approaches. Review of Multidisciplinary Academic and Practice Studies welcomes high-quality manuscripts covering original research articles, review articles, book reviews, case reports, and discussions from academics, researchers, practitioners, and professionals from various fields, including but not limited to social sciences, natural sciences, humanities, and technology aimed at providing a better understanding of contemporary issues in various fields of study.</p> </div> en-US [email protected] (Admin Journal STIE Krakatau) [email protected] (Fiqqi Ahludzikri) Thu, 22 Oct 2026 06:59:44 +0000 OJS 3.3.0.10 http://blogs.law.harvard.edu/tech/rss 60 https://jurnal.stiekrakatau.ac.id/index.php/rmaps/article/view/745 <p><strong>Purpose</strong>: This study revisits the K-means clustering algorithm as a core technique of unsupervised machine learning and explains, in plain terms, how it groups unlabeled data into meaningful clusters.<br><strong>Methodology</strong>: The paper combines a narrative review of studies published mostly between 2021 and 2026 with a manual, step-by-step computation of K-means on a small one-dimensional dataset of nine values, using Euclidean distance and K=2. The final partition is then checked with the within-cluster sum of squares and the silhouette coefficient for K values from one to four.<br><strong>Results</strong>: The algorithm stabilized after three reassignment rounds and produced the clusters {2, 3, 4, 10, 11, 12} and {20, 25, 30}, with centroids of 7 and 25. The within-cluster sum of squares fell from 348.00 to 150.00, and the mean silhouette coefficient reached 0.660, although K=3 gave a higher value of 0.711.<br><strong>Conclusions</strong>: K-means is simple, fast, and easy to interpret, yet its output depends strongly on the chosen number of clusters and on the starting centroids.<br><strong>Limitations</strong>: The worked example relies on a small one-dimensional dataset and does not test high-dimensional, noisy, or streaming data.<br><strong>Contributions</strong>: The paper offers a transparent teaching case that links the classic procedure to current validation practice and to recent applied studies.</p> Zahraa Radhi Waad Copyright (c) 2026 Review of Multidisciplinary Academic and Practice Studies https://jurnal.stiekrakatau.ac.id/index.php/rmaps/article/view/745 Thu, 22 Oct 2026 00:00:00 +0000