Comparison of K-Means and Fuzzy C-Means Clustering Algorithms in the Assessment of Outstanding Urban Villages in Jambi City
A Case Study at the Governance Division of the Regional Secretariat of Jambi City
DOI:
https://doi.org/10.33998/jms.2025.5.2.2298Keywords:
Clustering, Sub-district, K-Means, Fuzzy C-Means, PythonAbstract
Abstract−The assessment of outstanding urban villages at the city level in Jambi faces challenges in defining categories for coaching groups and selecting the appropriate algorithm to handle heterogeneous data. To address this issue, clustering techniques using K-Means and Fuzzy C-Means algorithms were applied to segment coaching needs based on homogeneous value proximity. This study analyzed 204 sub-district assessment data using Python, producing clustering results with nearly equivalent cluster separation quality. The K-Means algorithm achieved a silhouette score of 0.3854, slightly higher than Fuzzy C-Means at 0.3831. Both algorithms formed consistent cluster patterns with average values of 82, 72, and 61, classified into three clusters: (1) Cluster 0 receives awards and career development promotions, (2) Cluster 1 focuses on management training and bureaucratic reform, and (3) Cluster 2 requires coaching clinics and technical guidance. The findings indicate that K-Means is more advantageous due to its simplicity, effectiveness in handling linear datasets, and clear data distribution. This clustering approach supports the Jambi City Government, particularly the Regional Secretariat Governance Section, in designing data-driven coaching strategies to enhance the quality of sub-district development.
Downloads
References
G. N. Elwirehardja, T. Suparyanto, and B. Pardamean, Machine Learning Untuk Pemula. 2023.
Budi Raharjo, Pembelajaran Mesin. 2021.
Pemerintah Republik Indonesia, Pemerintah Republik Indonesia No. 17 Tahun 2018 tentang Kecamatan. Pemerintah Republik Indonesia, 2018.
E. Buulolo, “Data Mining Untuk Perguruan Tinggi,” 2020.
A. E. Pramitasari and Y. Nataliani, “PERBANDINGAN CLUSTERING KARYAWAN BERDASARKAN NILAI KINERJA DENGAN ALGORITMA K-MEANS DAN FUZZY C-MEANS,” JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 8, no. 3, 2021, doi: 10.35957/jatisi.v8i3.957.
S. Kurniawan, A. M. Siregar, and H. Y. Novita, “Penerapan Algoritma K-Means dan Fuzzy C-Means Dalam Mengelompokan Prestasi Siswa Berdasarkan Nilai Akademik,” Scientific Student Journal for Information, Technology and Science, vol. IV, no. 1, 2023.
I. Sufairoh, A. C. Rani, K. Amalia, and D. Rolliawati, “Perbandingan Hasil Analisis Clustering Metode K-Means, DBSCAN Dan Hierarchical Pada Data Marketplace Electronic Phone,” JOINS (Journal of Information System), vol. 8, no. 1, 2023, doi: 10.33633/joins.v8i1.8016.
F. Harahap, “Perbandingan Algoritma K-Means dan K-Medoids untuk Clustering Kelas Siswa Tunagrahita,” TIN: Terapan Informatika Nusantara, vol. 2, no. 4, 2021.
G. B. Kaligis and S. Yulianto, “ANALISA PERBANDINGAN ALGORITMA K-MEANS, K-MEDOIDS, DAN X-MEANS UNTUK PENGELOMPOKKAN KINERJA PEGAWAI,” IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi, vol. 1, no. 3, 2022, doi: 10.24246/itexplore.v1i3.2022.pp179-193.
A. Kurnia, “Perbandingan Algoritma K-Means dan Fuzzy C-Means Untuk Clustering Puskesmas Berdasarkan Gizi Balita Surabaya,” Jurnal PROCESSOR, vol. 18, no. 1, 2023, doi: 10.33998/processor.2023.18.1.696.
S. S. Rachmasari and A. Kudus, “Perbandingan Penerapan Algoritme K-Means dan Fuzzy C-Means untuk Mengelompokkan Data Kinerja Dosen Universitas Islam Bandung,” Spesia, 2021.
M. B. Johra, “SOFT CLUSTERING DENGAN ALGORITMA FUZZY K-MEANS (STUDI KASUS : PENGELOMPOKAN DESA DI KOTA TIDORE KEPULAUAN),” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 15, no. 2, 2021, doi: 10.30598/barekengvol15iss2pp385-392.
Agung Wijoyo, “Pembelajaran Machine Learning,” Jurnal Ilmu Komputer dan Science, Feb. 2024.
Chrisyantus Leto, “KONSEP DATA MINING DAN PENERAPAN,” CV. KERANJANGTEKNOLOGI MEDIA, Oct. 2023.
R. Hayati, “Pengertian Penelitian Studi Literatur, Ciri, Metode, dan Contohnya,” 2022.
H. Kurniawan, “Pengantar Praktis Penyusunan Instrumen Penelitian,” 2021.
Sola Huddin, “Penerapan Fuzzy C-Means Pada Klasterisasi Penerima Bantuan Pangan Non Tunai,” KLIK: Kajian Ilmiah Informatika dan Komputer, Aug. 2023.
M. Orisa, “Optimasi Cluster pada Algoritma K-Means,” Prosiding SENIATI, vol. 6, no. 2, 2022, doi: 10.36040/seniati.v6i2.5034.
A. M. Sikana and A. W. Wijayanto, “Analisis Perbandingan Pengelompokan Indeks Pembangunan Manusia Indonesia Tahun 2019 dengan Metode Partitioning dan Hierarchical Clustering,” Jurnal Ilmu Komputer, vol. 14, no. 2, 2021, doi: 10.24843/jik.2021.v14.i02.p01.
Amna, DATA MINING. Padang: PT GLOBAL EKSEKUTIF TEKNOLOGI, 2023.
P. Setiaji and W. A. Triyanto, “KLASTERING INDUSTRI DI KABUPATEN KUDUS MENGGUNAKAN METODE FUZZY C-MEANS,” Simetris : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, vol. 7, no. 2, 2016, doi: 10.24176/simet.v7i2.794.
E. Prasetyowati, “Data Mining Pengelompokan Data untuk Informasi dan Evaluasi,” 2017.
