Main Article Content


Legal Documentation and Information Network (JDIH) is one of the government agencies in the field of law, which is regulated by Presidential Regulation Number 33 of 2012. JDIH consists of central and regional levels, at the provincial level the Bureau of Law and Human Rights is the center of JDIH in its territory. JDIH at the provincial level has the duties and functions to provide guidance and evaluation at the members. To measure the level of community satisfaction with JDIH members in West Java, a survey was conducted using the 360  review ​​method on the website The results contained 18,045 raw data. After preprocessing, 46 datasets and 11 attributes were generated. Cluster modeling uses the K-Means algorithm, the results are evaluated by the Davies Boulding Index (DBI) method. Evaluation results show a low level of similarity so that the distance between clusters is getting higher. On this study is classified into 4 clusters, the lowest satisfaction indicator is known to be in cluster 3 which consists of 10 regions.  In this research can be used  to determining policies for  the government  of West Java Province.


kepuasan layanan review 360 algoritma k-means Davies Boulding Index service satisfaction review 360 algoritma k-means Davies Boulding Index

Article Details

Author Biographies

Beny Ruhiman, STMIK LIKMI Bandung

STMIK LIKMI Bandung Magister Sistem Informasi Provinsi Jawa Barat, Biro Hukum dan HAM

Ade Ramdan, STMIK LIKMI Bandung

STMIK LIKMI Bandung Magister Sistem Informasi Badan Riset dan Inovasi Nasional (BRIN)

Christina Juliane, STMIK LIKMI Bandung

STMIK LIKMI Bandung Magister Sistem Informasi
How to Cite
Ruhiman, B., Ramdan, A. ., & Juliane, C. (2022). Algorithm K-Means Clustering Algorithm to Classify the Level of Legal Information Service Objectives in West Java Province : K-Means Clustering Algorithm to Classify the Level of Legal Information Service Objectives in West Java Province . Jurnal Komputer Terapan , 8(1), 178–185.


  1. JDIH Provinsi Jawa Barat, Keputusan Gubernur Jawa Barat Nomor 180/Kep.267-Hukham/2021 tentang Indikator Penilaian Penghargaan Jaringan Dokumentasi dan Informasi Hukum Daerah Provinsi Jawa Barat. Indonesia, [Online]. Available:, 2021. (peraturan)
  2. Jiawei Han, “Data mining: Concepts and Techniques Second Edition”, Elsevier, AID International, Sabre Foundation, 2006. (buku)
  3. H. Juanedi, H. Budianto, I. Maryati, Y. Melani, “Data Transformation Pada Data Mining”, 2011. (Prosiding Konferensi Nasional “Inovasi dalam Desain dan Teknologi” (IdeaTech))
  4. A. Yang, W. Zhang, J. Wang, K. Yang, Y. Han, L. Zhang, “Review on the Application of Machine Learning Algorithms in the Sequence Data Mining of DNA”, Frontiers in Bioengineering and Biotechnologi, 2020. (jurnal)
  5. G. Biswas; J.B. Weinberg; D.H. Fisher, “ITERATE: a conceptual clustering algorithm for data mining” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Volume: 28, Issue: 2, May 1998. (jurnal)A. Bastian., “Penerapan Algoritma K-Means Clustering Analysis Pada Penyakit Menular Manusia (Studi Kasus Kabupaten Majalengka)”, Jurnal Sistem Informasi, 2018.(jurnal)
  6. Raykov, Y. P., Boukouvalas, A., Baig, F., and Little, M. A., “What to do when K-means clustering fails: a simple yet principled alternative algorithm”, PloS one, vol. 11, no. 9, 2016. (jurnal)
  7. I. Budiman, T. Prahasto, and Y. Christyono, “Data Clustering Menggunakan Metodologi Crisp-DM Untuk Pengenalan Pola Proporsi Pelaksanaan Tridharma”, presented in 2012 Seminar Nasional Aplikasi Teknologi Informasi (SNATI 2012), Yogyakarta. (seminar)
  8. Sitompul, B.J.D., Sitompul, O.S., Sihombing, P., “Enhancement Clustering Evaluation Result of Davies-Bouldin Index with Determining Initial Centroid of K-Means Algoritma”, The 3rd International Conference on Computing and Applied Informatics 2018, (konferensi).
  9. Nawrin, S., Rahman, M.R. & Akhter, S., “Exploreing K-Means with internal validity indexes for data clustering in traffic management system”, International Journal of Advanced Computer Science and Applications, 2017.(jurnal)
  10. Arora Pr., Deepali Dr., Varshney Sh., “Analysis of K-Means and K-Medoids Algorithm For Big Data”, International Conference on Information Security & Privacy (ICISP 2015). (konferensi)
  11. Thakare, Y.S. & Bagal, S.B., “Performance evaluation of k-means clustering algorithm with various distance metrics”, International Journal of Computer Applications, 2015. (jurnal)
  12. Davies, D. L.; Bouldin, D. W. "A Cluster Separation Measure", IEEE Transactions on Pattern Analysis and Machine Intelligence, 1979.(jurnal)