ANALISIS FAKTOR SOSIAL EKONOMI YANG MEMPENGARUHI RENDAHNYA CAPAIAN PENDIDIKAN DI INDONESIA MENGGUNAKAN KOMBINASI METODE DATA MINING
Abstract
Educational inequality remains a persistent issue in Indonesia, particularly in regions with challenging socio-economic conditions. This study aims to analyze how various socio-economic factors influence the average years of schooling across Indonesian provinces using a combination of K-Means Clustering and Decision Tree algorithms. The dataset includes indicators such as poverty rate, gross regional domestic product (GRDP), per capita expenditure, and life expectancy, obtained from official national statistics.
K-Means Clustering was employed to segment provinces into three distinct groups based on socio-economic similarities. The clustering revealed clear disparities among regions, where the most disadvantaged cluster showed significantly lower education levels. Subsequently, the Decision Tree algorithm was used to classify the average years of schooling, identifying per capita expenditure, life expectancy, and socio-economic cluster as the most influential variables.
The combined approach allows for both segmentation and interpretation, providing data-driven insights that are accessible and actionable for policymakers. The findings highlight the importance of targeting socio-economic improvements as a strategy to enhance educational outcomes. Ultimately, this study underscores the value of integrating unsupervised and supervised machine learning models in addressing complex social issues in education.
References
Nurwati, R.N. and Listari, Z.P. (2021) ‘Pengaruh Status Sosial Ekonomi Keluarga Terhadap Pemenuhan Kebutuhan Pendidikan Anak’, Share : Social Work Journal, 11(1), p. 74. Available at: https://doi.org/10.24198/share.v11i1.33642.
Purwanti, Y. (2022) ‘Disparitas fasilitas pendidikan dan tenaga pengajar sekolah menengah atas di Indonesia menggunakan Metode Spatial Fuzzy C-Means’, Jurnal Pendidikan Dompet Dhuafa, 12(02), pp. 15–22.
Rahim, A., Haryadi, W. and Muliawansyah, D. (2024) ‘ANALISIS FAKTOR RATA-RATA LAMA SEKOLAH DAN PENGANGGURAN TERBUKA DALAM MEMPENGARUHI TINGKAT KEMISKINAN DI KABUPATEN SUMBAWA’, Jurnal Ekonomi & Bisnis, 12(1), pp. 14–25.
Royani, I. and Pertiwi, T.B. (2022) ‘Pengaruh Status Sosial Ekonomi Terhadap Minat Melanjutkan Pendidikan Anak Usia 11–21 Tahun: Pengaruh Status Sosial Ekonomi Terhadap Minat Melanjutkan Pendidikan Anak Usia 11–21 Tahun’, Journal Of Lifelong Learning, 5(2), pp. 28–36.
Yusuf, D., Sestri, E. and Razi, F. (2024) ‘PENGELOMPOKKAN DATA MAHASISWA MENGGUNAKAN CLUSTERING UNTUK OPTIMALISASI PENERIMAAN MAHASISWA BARU’, JIKA (Jurnal Informatika), 8(4), pp. 484–490.
Yusuf, D., Sestri, E. and Razi, F. (2023) ‘Implementasi Teknik Clustering Untuk Pengelompokan Mobil Bekas Berdasarkan Grade Pada Mobi Auto’, J-SISKO TECH (Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD), 6(2), pp. 307-313.
F. H. Kuwil, F. Shaar, A. E. Topcu, and F. Murtagh, “A new data clustering algorithm based on critical distance methodology,” Expert Syst. Appl., vol. 129, pp. 296–310, 2019, doi: 10.1016/j.eswa.2019.03.051
N. T. Luchia, H. Handayani, F. S. Hamdi, D. Erlangga, and S. Fitri Octavia, “Perbandingan K-Means dan K-Medoids Pada Pengelompokan Data Miskin di Indonesia,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 2, no. 2, pp. 35–41, 2022.
D. S. Maylawati, T. Priatna, H. Sugilar, and M. A. Ramdhani, “Data science for digital culture improvement in higher education using K-means clustering and text analytics,” Int. J. Electr. Comput. Eng., vol. 10, no. 5, pp. 4569–4580, 2020, doi: 10.11591/IJECE.V10I5.PP4569-4580.
A. Cahyadi, Hendryadi, S. Widyastuti, V. N. Mufidah, and Achmadi, “Emergency remote teaching evaluation of the higher education in Indonesia,†Heliyon, vol. 7, no. 8, 2021, doi: 10.1016/j.heliyon.2021.e07788.
H. Annur, “Klasifikasi MasyarakatMiskin Menggunakan Metode Naïve Bayes,” Ilk. J. Ilm., vol. 10, no. 2, pp.160–165, 2018.
F. Aris and Benyamin, “Penerapan Data Mining untuk Identifikasi Penyakit Diabetes Melitus dengan Menggunakan Metode Klasifikasi,” J. Sist. Komput. dan Sist. Inf., vol. 1, no. 1, pp. 1–6, 2019.
Asroni, B. M. Respati, and S. Riyadi, “Penerapan Algoritma C4.5 untuk Klasifikasi Jenis Pekerjaan Alumni di Universitas Muhammadiyah Yogyakarta,” J. Ilm. Fak. Tek. Univ. Muhammadiyah, vol. 21, no. 2, pp. 158–165, 2018.
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