Klasterisasi Sumber Daya Pendidikan Tinggi Antarprovinsi di Indonesia Tahun 2025 Menggunakan K-Means: K-Means Clustering of Higher Education Resources Across Indonesian Provinces in 2025

Kristia Yulilawan; Agung Hardiansyah

doi:10.57152/malcom.v6i3.3112

Authors

Kristia Yulilawan 1christianpesat@gmail.com
Agung Hardiansyah STMIK Pesat Nabire

DOI:

https://doi.org/10.57152/malcom.v6i3.3112

Keywords:

Data Mining, Distribusi Sumber Daya, K-Means Clustering, Orange Data Mining, Pendidikan Tinggi

Abstract

Ketimpangan distribusi perguruan tinggi dan sumber daya pendidikan tinggi di Indonesia memengaruhi pemerataan akses dan kualitas layanan pendidikan tinggi. Dengan menggunakan data tahun 2025, penelitian ini mengelompokkan 38 provinsi menggunakan algoritma K-Means pada Orange Data Mining berdasarkan jumlah perguruan tinggi, tenaga pendidik, mahasiswa, rasio mahasiswa terhadap tenaga pendidik, dan kebutuhan tenaga pendidik. Data melalui tahap pra-pemrosesan, standardisasi z-score, pembentukan tiga cluster, dan evaluasi menggunakan Silhouette Score. Nilai silhouette rata-rata sebesar 0,520 menunjukkan struktur cluster yang cukup baik dan terpisah. Cluster 1 menggambarkan provinsi dengan kapasitas pendidikan tinggi yang relatif terbatas, Cluster 2 menunjukkan tekanan kebutuhan tenaga pendidik, dan Cluster 3 mencakup pusat-pusat aktivitas akademik nasional. Kebaruan penelitian terletak pada integrasi skala institusi, sumber daya akademik, jumlah mahasiswa, dan kecukupan tenaga pendidik dalam satu model clustering tingkat provinsi. Hasil penelitian ini menyediakan dasar empiris bagi kebijakan diferensial terkait pengembangan institusi, distribusi tenaga pendidik, dan penguatan kapasitas pendidikan tinggi.

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References

A. Yunita, H. B. Santoso, and Z. A. Hasibuan, “‘Everything is data’: towards one big data ecosystem using multiple sources of data on higher education in Indonesia,” J. Big Data, vol. 9, no. 1, p. 91, 2022, doi: 10.1186/s40537-022-00639-7

M. R. Putri, G. S. Nugraha, and R. Dwiyansaputra, “Pengelompokan Provinsi di Indonesia Berdasarkan Indikator Pendidikan Menggunakan Metode K-Means Clustering,” Journal of Computer Science and Informatics Engineering (J-Cosine), vol. 7, no. 1, pp. 76–83, 2023, doi: 10.29303/jcosine.v7i1.509

Z. Mustakim and R. Kamal, “K-means clustering for classifying the quality management of secondary education in Indonesia,” Cakrawala Pendidikan, vol. 40, no. 3, pp. 723–735, 2021, doi: 10.21831/cp.v40i3.40150

C. E. Widiantoro, S. P. Putri, T. Purwandari, and others, “Pengelompokkan Provinsi di Indonesia Berdasarkan Indikator Pendidikan Jenjang SMA/Sederajat dengan Analisis Klaster Non Hierarki,” BIAStatistics Journal Of Statistics Theory And Aplication Special Issue, vol. 4, no. 1, pp. 1–9, 2024, doi: 10.1234/biassi.v2024i.273

U. Arfan and Y. Pekei, “Evaluasi Distribusi Guru-Siswa dan Ketersediaan Sekolah untuk Mendukung Pembangunan Pendidikan Menggunakan K-Means Clustering: An Evaluation of Teacher–Student Distribution and School Availability in Supporting Educational Development Using the K-Means Clustering Algorithm,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 5, no. 4, pp. 1508–1516, 2025, doi: 10.3390/app13053267.

N. I. Mohd Talib, N. A. Abd Majid, and S. Sahran, “Identification of student behavioral patterns in higher education using K-means clustering and support vector machine,” Applied Sciences, vol. 13, no. 5, p. 3267, 2023, doi: 10.3390/app13053267.

L. N. Rani, S. Defit, and L. J. Muhammad, “Determination of student subjects in higher education using hybrid data mining method with the k-means algorithm and fp growth,” International Journal Of Artificial Intelegence Research, vol. 5, no. 1, 2021,

A. F. Mohamed Nafuri, N. S. Sani, N. F. A. Zainudin, A. H. A. Rahman, and M. Aliff, “Clustering analysis for classifying student academic performance in higher education,” Applied Sciences, vol. 12, no. 19, p. 9467, 2022, doi: 10.3390/app12199467.

A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, “K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data,” Inf. Sci. (N. Y)., vol. 622, pp. 178–210, 2023, doi: 10.1016/j.ins.2022.11.139.

F. Corradini, L. Mozzoni, M. Piangerelli, B. Re, and L. Rossi, “A Framework for Rapidly Prototyping Data Mining Pipelines,” Big Data and Cognitive Computing, vol. 9, no. 6, p. 150, 2025, doi: 10.3390/bdcc9060150.

R. Liu, “Data analysis of educational evaluation using K-means clustering method,” Comput. Intell. Neurosci., vol. 2022, no. 1, p. 3762431, 2022, doi: 10.1155/2022/3762431.

J. Newling and F. Fleuret, “Fast k-means with accurate bounds,” in International Conference on Machine Learning, 2016, pp. 936–944.

R. Mussabayev, N. Mladenovic, B. Jarboui, and R. Mussabayev, “How to use K-means for big data clustering?,” Pattern Recognit., vol. 137, p. 109269, 2023, doi: 10.1016/j.patcog.2022.109269.

P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math., vol. 20, pp. 53–65, 1987, doi: 10.1016/0377-0427(87)90125-7.

B. Albreiki, N. Zaki, and H. Alashwal, “A systematic literature review of student’performance prediction using machine learning techniques,” Educ. Sci. (Basel)., vol. 11, no. 9, p. 552, 2021, doi: 10.3390/educsci11090552.

M. A. Al-Barrak and M. Al-Razgan, “Predicting students final GPA using decision trees: a case study,” International journal of information and education technology, vol. 6, no. 7, p. 528, 2016, doi: 10.7763/IJIET.2016.V6.745.

S. M. Dol and P. M. Jawandhiya, “Classification technique and its combination with clustering and association rule mining in educational data mining A survey,” Eng. Appl. Artif. Intell., vol. 122, p. 106071, 2023, doi: 10.1016/j.engappai.2023.106071.

S. Kim, S. Cho, J. Y. Kim, and D.-J. Kim, “Statistical assessment on student engagement in asynchronous online learning using the k-means clustering algorithm,” Sustainability, vol. 15, no. 3, p. 2049, 2023, doi: 10.3390/su15032049.

M. Orsoni et al., “Preliminary evidence on machine learning approaches for clusterizing students’ cognitive profile,” Heliyon, vol. 9, no. 3, 2023, doi: 10.1016/j.heliyon.2023.e14506.

B. Peng and X. Pei, “A decision support system model for middle school education management based on sparse clustering algorithm,” Mobile Information Systems, vol. 2022, no. 1, p. 4807395, 2022, doi: 10.1155/2022/4807395.

Z. Wang, “Higher education management and student achievement assessment method based on clustering algorithm,” Comput. Intell. Neurosci., vol. 2022, no. 1, p. 4703975, 2022, doi: 10.1155/2022/4703975.

C. G. Lechuga and S. Doroudi, “Three algorithms for grouping students: A bridge between personalized tutoring system data and classroom pedagogy,” Int. J. Artif. Intell. Educ., vol. 33, no. 4, pp. 843–884, 2023, doi: 10.1007/s40593-022-00309-y.