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All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar J-MAS (Jurnal Manajemen dan Sains) Jurnal Ilmiah Kajian Keimigrasian (JIKK) Jurnal Abdimas Imigrasi (JaIM) TEMATICS : TECHNOLOGY MANAGEMENT AND INFORMATICS RESEARCH JOURNALS Journal of Dinda : Data Science, Information Technology, and Data Analytics Annals of Human Resource Management Research Return : Study of Management, Economic and Bussines Innovative: Journal Of Social Science Research Journal of Multidisciplinary Science: MIKAILALSYS Journal of Administration and International Development
Nursanto, Gunawan Ari
Politeknik Imigrasi
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Physics Public Health Social Sciences Other

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SYSTEMATIC LITERATURE REVIEW: PEMETAAN KARAKTERISTIK IMPLEMENTASI DATA MINING PADA BERBAGAI BIDANG Makarim, Amhar Nabil; Nursanto, Gunawan Ari; Assiroj, Priati
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 02 (2026): Volume 11 No. 02 Juni 2026 Press
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i02.56859

Abstract

This study aims to map the characteristic of data mining implementation based on 37 journals reviewed systematically. The method employed was a Systematic Literature Review (SLR) with PRISMA approach, consisting of identification, screening, eligibility, and inclusion stages. The data were analyzed descriptively to identify methods, algorithms, data types, and implementation result of data mining across various domains. The findings reveal that the most dominant task was clustering, with 27 articles, while K-Means was the most frequently used algorithm, appearing in 26 studies. The most widely studied implementation domain was economics and business with 12 articles, followed by health with 7 articles, social and government with 6 articles, also tourism and education with 5 articles each. In addition to K-Means, other algorithms identified in the reviewed studies included Apriori, FP-Growth, K-NN, Decision Tree/C4.5, Naïve Bayes, K-Medoids, and U-KMeans. In terms of evaluation, classification studies commonly used accuracy, association rule studies relied on support and confidence, while clustering studies tended to use measures such as the Davies-Bouldin Index and silhouette score. This study concludes that K-Means remains the most dominant algorithm in data mining implementation. The result of this review are expected to serve as a reference for understanding trends in data mining implementation and for supporting more focused future research.
Co-Authors Abharina Afiah Ghassani A Akbar, Rasona Sunara Alma’ruf Indrasrani, Ramadhani Ikhsan Alyuhdi Arifuddin Agung Pramana Aqbal Bagus Nafadilla Arief Febrianto Arief Febrianto, Arief Assiroj, Priati Ayi Abdurahman Besse Hartati Budy Mulyawan, Budy Cakra Trinata Catur Susaningsih Galuh Boy Hertantyo Geraldo Nathaniel Hendrick Alif Arsyandy Maftuh, Naufal Makarim, Amhar Nabil Martadireja, Okky Pratama Maulidia, Hanifa Mila Rosmaya Muhamad Dafaburhani Rifan Muhammad Fahrury Romdendine Muhammad Rafli Nico Harmono Putra Nurul Maharani Piranti Piranti, Nurul Maharani Prabadhi, Isidorus Anung Priati Priati, Priati R. Yohanes Dhimas Agung Radica Arino Rahman Rahmawati Nurul Jannah Rainhard Bounkey Marbun Rita Kusuma Astuti Seno Setyo Pujonggo Subkhi Mubarok Vira Widhaningsih Virra Wirdhiningsih Wilonotmo Wilonotmo Wilonotomo