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All Journal Journal of Information System, Applied, Management, Accounting and Research
Sausan Salsabila Musriyanti
Politeknik Imigrasi dan Pemasyarakatan
Computer Science & IT Decision Sciences, Operations Research & Management

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TINJAUAN PUSTAKA SISTEMATIS PERBANDINGAN ALGORITMA K-MEANS DAN DBSCAN PADA KLASTERISASI DATA LAYANAN PUBLIK Sausan Salsabila Musriyanti; Muhammad Fahrury Romdendine; Cakra Trinata
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 3 (2026): JISAMAR (August 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i3.2560

Abstract

This study presents a systematic literature review comparing K-Means and DBSCAN in clustering public service data. The article adopts the PRISMA approach to make the review process systematic, transparent, and reproducible. Searches were conducted in Google Scholar, ScienceDirect, and IEEE Xplore using keywords related to K-Means, DBSCAN, clustering, and public service data. A total of 11,700 records were identified, 140 studies were retained as candidates after initial screening, and 30 studies were included in the final synthesis. The review shows that K-Means is often used because it is simple, fast, and efficient for structured data, while DBSCAN performs better for datasets containing noise, density variation, and irregular cluster shapes. In public service data, algorithm selection depends on data characteristics, analytical objectives, and parameter sensitivity.
Co-Authors Cakra Trinata Muhammad Fahrury Romdendine