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All Journal INFORMAL: Informatics Journal Algoritme Jurnal Mahasiswa Teknik Informatika
Safwandi Safwandi
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Computer Science & IT

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Analisis K-Means Clustering pada Data Sepeda Motor Rozzi Kesuma Dinata; Safwandi Safwandi; Novia Hasdyna; Nur Azizah
INFORMAL: Informatics Journal Vol 5 No 1 (2020): INFORMAL - Informatics Journal
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v5i1.17071

Abstract

K-Means is a data mining algorithm that can be used to grouping or clustering data. This research using k-means for clustering the data of motorcycle based on consumer needs. The dataset used in this research is Honda and Yamaha motorcycle which taken from the dialers in Dewantara District, Aceh. The data tested by grouping 300 data of motorcycle with different attributes into 3 clusters, which are cheap, normal, and expensive. The distribution of the data we separate it using 45 data in 15 times of test. Each test used 3 different data randomly selected on each test. To calculate the distance of each motorcycle data that have been inputted to each centroid, we used the Euclidean Distance formula. Data in this cluster system can be used as a recommendation for users in selecting the motorcycle that they interest the most. The results of the performance on each test finished in 15 times shown that the average value of Precision by 76%, Recall by 76% and the accuracy by 81%.
Optimasi Website Pengelompokan Pekerja Menggunakan Metode Fuzzy C-Means di Disnaker Perindag Kota Binjai Salma Kharida; Safwandi Safwandi; Rini Meiyanti
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 7 No 1 (2026): October 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v7i1.15715

Abstract

This study aims to develop a web-based worker clustering system at Disnaker Perindag Binjai City using the Fuzzy C-Means (FCM) method. Data from 387 registered workers between 2022–2025 were processed based on age, education, work experience, expertise, and certifications. Data processing steps included cleaning, normalization, and clustering with parameters (c)=4, (m)=2, (ε) =0,0001, and a 100-iteration limit. The system classifies workers into four groups based on work readiness and competence. Implemented using Python, Flask, and SQLite, the web application features data management, automated clustering, and result visualization. Evaluation using the Silhouette Coefficient demonstrates that FCM effectively clusters workers into four groups with a balanced distribution and high clustering performance. This system is expected to support more structured workforce management.
Co-Authors Novia Hasdyna Nur Azizah Rini Meiyanti Rozzi Kesuma Dinata Salma Kharida