p-Index From 2021 - 2026
4.658
P-Index
This Author published in this journals
All Journal International Journal of Evaluation and Research in Education (IJERE) Jurnal Teknologi Informasi dan Ilmu Komputer JUSIFO : Jurnal Sistem Informasi International Journal of Artificial Intelligence Research Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Information System Jurnal Nasional Komputasi dan Teknologi Informasi Journal of Computer Science and Informatics Engineering (J-Cosine) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Antivirus : Jurnal Ilmiah Teknik Informatika International Journal of Artificial Intelligence Mobile and Forensics ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Teknika Kontribusia : Research Dissemination for Community Development E-Link: Jurnal Teknik Elektro dan Informatika REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal Pengabdian dan Pemberdayaan Nusantara (JPPNu) Indexia Jurnal Abdi Masyarakat Indonesia KREATIF: Jurnal Pengabdian Masyarakat Nusantara Digital Transformation Technology (Digitech) Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Scientica: Jurnal Ilmiah Sains dan Teknologi Kohesi: Jurnal Sains dan Teknologi SULUH ABDI : Jurnal Ilmiah Pengabdian Kepada Masyarakat Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Mobile and Forensics

Hybrid ABC–K Means for Optimal Cluster Number Determination in Unlabeled Data Rosyid, Harunur; bin Lakulu, Muhammad Modi; bt. Mailok , Ramlah
Mobile and Forensics Vol. 6 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v6i2.11529

Abstract

This study presents the ABC K Means GenData algorithm, an enhancement over traditional K Means clustering that integrates the Artificial Bee Colony (ABC) optimization approach. The ABC K Means GenData algorithm addresses the issue of local optima commonly encountered in standard K Means algorithms, offering improved exploration and exploitation strategies. By utilizing the dynamic roles of employed, onlooker, and scout bees, this approach effectively navigates the clustering space for categorical data. Performance evaluations across several datasets demonstrate the algorithm's superiority. For the Zoo dataset, ABC K Means GenData achieved high Accuracy (0.8399), Precision (0.8089), and Recall (0.7286), with consistent performance compared to K Means and Fuzzy K Means. Similar results were observed for the Breast Cancer dataset, where it matched the Accuracy and Precision of K Means and surpassed Fuzzy K Means in Precision and Recall. In the Soybean dataset, the algorithm also performed excellently, showing top scores in Accuracy, Precision, Recall, and Rand Index (RI), outperforming both K Means and Fuzzy K Means.. The comprehensive results indicate that ABC K Means GenData excels in clustering categorical data, providing robust and reliable performance. Future research will explore its application to mixed data types and social media datasets, aiming to further optimize clustering techniques. .