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Arif Rahmat Mujassam
Universitas Jambi
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Klasterisasi Satuan Pendidikan Berdasarkan Data Pokok Pendidikan Menggunakan Algoritma Fuzzy C-Means Arif Rahmat Mujassam; Pradita Eko Prasetya Utomo; Muti Fadhila Putri
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 5 (2025): Oktober 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i5.9868

Abstract

Abstrak − Penelitian ini bertujuan menganalisis pola persebaran pendidikan di Provinsi Jambi melalui pendekatan data mining dengan metode clustering algoritma Fuzzy C-Means (FCM). Data yang digunakan berasal dari Data Pokok Pendidikan (Dapodik) tahunan pada 2019–2024, yang mencakup variabel peserta didik, rombel, guru, dan pegawai. Evaluasi validitas klaster menggunakan Partition Coefficient (PC), Modified Partition Coefficient (MPC), dan Partition Entropy (PE) menunjukkan bahwa dua klaster merupakan jumlah optimal. Hasil analisis mengindikasikan karakteristik yang berbeda, yakni klaster dengan komposisi variabel pendidikan besar dan klaster dengan komposisi variabel pendidikan kecil. Dinamika label antar klaster yang tidak konsisten menggambarkan adanya dualitas dalam distribusi variabel pendidikan dari waktu ke waktu. Klaster komposisi variabel yang besar cenderung mendekati standar ideal menurut Permendikbud, meskipun jumlah anggotanya lebih sedikit dibandingkan klaster komposisi variabel yang kecil.Kata Kunci: Data Mining; Clustering; Fuzzy C-Means; Dapodik; Abstract − This study aims to analyze the spatial distribution patterns of education in Jambi Province through a data mining approach using the Fuzzy C-Means (FCM) clustering algorithm. The data were obtained from the annual Data Pokok Pendidikan (Dapodik) between 2019 and 2024, covering variables such as students, study groups (rombel), teachers, and staff. The cluster validity evaluation using Partition Coefficient (PC), Modified Partition Coefficient (MPC), and Partition Entropy (PE) indicated that two clusters were the optimal number. The analysis results reveal distinct characteristics between clusters, namely a cluster with a high composition of educational variables and another with a lower composition. The inconsistent labeling dynamics between clusters over time illustrate a duality in the distribution of educational variables. The cluster with a higher composition of educational variables tends to be closer to the ideal standards defined by the Ministry of Education and Culture Regulation (Permendikbud), although it comprises fewer members compared to the cluster with lower composition.Keywords: Data Mining; Clustering; Fuzzy C-Means; Dapodik;
Co-Authors Muti Fadhila Putri Pradita Eko Prasetya Utomo