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All Journal Abdimasku : Jurnal Pengabdian Masyarakat Science Technology and Management Journal (STMJ)
Adji, Dian Restu
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Health Professions Industrial & Manufacturing Engineering Social Sciences

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Journal : Science Technology and Management Journal (STMJ)

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Metode dan Algoritma Dalam Data Clustering: Systematic Literature Review Adji, Dian Restu; Lutfina, Erba; Ferdianto, Bhekti Eka; Prashanti, Eva; Anwarri, Kenza Amelia Putri; Prayogo, Syahrul Rizqi
Science Technology and Management Journal Vol. 5 No. 1 (2025): Januari 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Nasional Karangturi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53416/stmj.v5i1.326

Abstract

This study is Systematic Literature Review (SLR) of 34 journals related to data grouping techniques (clustering). The main objective of the study is to investigate the use of clustering methods in various research fields. In order to achieve this goal, this study answers five main research questions. First, this study analyzes research fields where clustering methods are often used in data mining applications. Second, this study identifies the most frequently used clustering methods based on data from the journals that have been collected. Third, this study determines the clustering method that provides the most optimal number of groups (clustering) based on the analysis of these journals. Fourth, this study identifies the types of data sets that are most often used in the context of clustering. Finally, this study looks at the distribution of the publication years of these journals to present a time frame of the development of clustering research. The results of this study provide in-depth insight into the trend of the use of clustering methods in various research contexts, provide information on the most commonly used methods, identify methods that provide optimal results, describe the dominant types of data sets, and provide a chronological perspective on the development of clustering research. These findings can provide valuable guidance for researchers interested in applying or developing clustering methods in specific fields.
Co-Authors Anwarri, Kenza Amelia Putri Ferdianto, Bhekti Eka Handoyo, Dhiky Resandi Wur Lutfina, Erba Prashanti, Eva Prayogo, Syahrul Rizqi Saraswati, Galuh Wilujeng