Implementasi CRISP-DM Pada Analisis Pembangunan Pendidikan Prasekolah Menurut Kabupaten/Kota di Indonesia
- Putri Chandra Iranti Universitas Sriwijaya, Palembang, Indonesia
-
Dedy Kurniawan
*
Universitas Sriwijaya, Palembang,
Indonesia
- M Rudi Sanjaya Universitas Sriwijaya, Palembang, Indonesia
- Ahmad Rifai Universitas Sriwijaya, Palembang, Indonesia
- M Husni Syahbani Universitas Sriwijaya, Palembang, Indonesia
- Gabriel Ekoputra Hartono Cahyadi Universitas Sriwijaya, Palembang, Indonesia
- Purwita Sari Universitas Sriwijaya, Palembang, Indonesia (*) Corresponding Author
Abstract
Preschool education through Kindergarten (TK) plays a crucial role in child development in Indonesia, yet unequal access remains a significant issue. This study evaluates the need for preschool infrastructure development using the K-Means clustering algorithm implemented through RapidMiner. Regional clustering is based on the number of students, number of TK schools, Human Development Index (HDI), poverty rate, population size, and unemployment rate. The CRISP-DM methodology is applied, involving stages of understanding, preparation, modeling, evaluation, and deployment. Data from the Central Bureau of Statistics (BPS) and the Ministry of Education's Dapodik system are utilized, incorporating Z-transformation normalization and data cleansing. The clustering results reveal three main clusters with the lowest Davies-Bouldin Index (DBI) at K=3, scoring 0.205. With a total of 514 districts/cities in Indonesia, the results of the needs of each cluster were obtained, namely Cluster 0 consisting of 402 districts/cities requiring increased participation, Cluster 1 covering 49 districts/cities requiring educational facilities, Cluster 2 covering 63 districts/cities requiring the construction of new schools. This study provides valuable insights into addressing disparities in preschool education access and offers guidance for better resource allocation and policy decisions aimed at improving early childhood education infrastructure.
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References
R. Adhitama, A. Burhanuddin, and R. Ananda, “Penentuan Jumlah Cluster Ideal Smk Di Jawa Tengah Dengan Metode X-Means Clustering Dan K-Means Clustering Determining Vocational Ideal Cluster Number in Central Java With X-Means Clustering and K-Means Clustering Methods,” J. Inform. dan Komputer) Akreditasi KEMENRISTEKDIKTI, vol. 3, no. 1, pp. 1–5, 2020, doi: 10.33387/jiko.
Abdussalam Amrullah, Intam Purnamasari, Betha Nurina Sari, Garno, and Apriade Voutama, “Analisis Cluster Faktor Penunjang Pendidikan Menggunakan Algoritma K-Means (Studi Kasus: Kabupaten Karawang),” J. Inform. dan Rekayasa Elektron., vol. 5, no. 2, pp. 244–252, 2022, doi: 10.36595/jire.v5i2.701.
N. D. Wahyuni, “Pengaruh Tingkat Pendidikan, Pekerjaan, Jumlah Tanggungan Anak, dan Pendapatan Orangtua terhadap Kemampuan Memenuhi Kebutuhan Pendidikan Anak di Indonesia,” J. Pendidik. dan Ekon., vol. 9, no. 3, pp. 204–212, 2020.
E. Yanty, T. Putri, and I. Kamila, “Pendukung Pendidikan Dengan Jumlah Sekolah Dan Jumlah Guru Menggunakan Algoritma K-Means,” J. Ilm. Mat., vol. 2, no. 1, pp. 1–12, 2022.
M. M. Astriani and M. A. Alfahnum, “Peningkatan Kompetensi Guru PAUD dalam Mengembangkan Media Pembelajaran Inovatif,” J. PkM Pengabdi. Kpd. Masy., vol. 3, no. 4, p. 366, 2020, doi: 10.30998/jurnalpkm.v3i4.8151.
E. Sugian, F. Fahruddin, and A. H. Witono, “Implementasi Program Pengembangan PAUD “Holistik Integratif’di PAUD LSM Ampenan Kota Mataram,” J. Ilm. Mandala Educ., vol. 7, no. 3, pp. 675–685, 2021, doi: 10.58258/jime.v7i3.2342.
Ramadhana, Islamiyah, and A. P. A. Masa, “Penerapan Data Mining Menggunakan Metode K-Means Clustering Pada Data Ekspor Batubara,” Adopsi Teknol. dan Sist. Inf., vol. 2, no. 1, pp. 35–42, 2023, doi: 10.30872/atasi.v2i1.595.
N. Hendrastuty, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa,” J. Ilm. Inform. Dan Ilmu Komput., vol. 3, no. 1, pp. 46–56, 2024, [Online]. Available: https://doi.org/10.58602/jima-ilkom.v3i1.26
M. Rachman Mulyandi et al., “Implementasi Algoritma K-Means Clustering Dalam,” J. Student Res., vol. 1, no. 3, pp. 101–114, 2023.
K. S. Purba, D. Hartama, and S. Suhada, “Analisis Data Mining Pesebaran Siswa Smp Di Pematangsiantar Dengan Metode Algoritma K-Means Clustering,” Kesatria J. Penerapan Sist. Inf. (Komputer dan Manajemen), vol. 3, no. 1, pp. 1–8, 2022, doi: 10.30645/kesatria.v3i1.91.
F. Martha and D. Anggraini, “Data Mining Untuk Pemeliharaan Prediktif Mesin Produksi berdasarkan Database Kerusakan Mesin menggunakan Naïve Bayes Classifier,” J. Ilm. Komputasi, vol. 20, no. 2, pp. 143–154, 2021, doi: 10.32409/jikstik.20.2.368.
Z. Muttaqin, “Implementasi Unsupervised Learning Pada Nilai Jasmani Kesamaptaan Sekolah Polisi Negara Dengan Metode Clustering Analysis,” PROSISKO J. Pengemb. Ris. dan Obs. Sist. Komput., vol. 10, no. 1, pp. 18–23, 2023, doi: 10.30656/prosisko.v10i1.6269.
M. Rafi Nahjan, Nono Heryana, and Apriade Voutama, “Implementasi Rapidminer Dengan Metode Clustering K-Means Untuk Analisa Penjualan Pada Toko Oj Cell,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 101–104, 2023, doi: 10.36040/jati.v7i1.6094.
M. Yamin Nurzaman and B. Nurina Sari, “Implementasi K-Means Clustering Dalam Pengelompokkan Banyaknya Jumlah Petani Berdasarkan Kecamatan Di Provinsi Jawa Barat,” J. Tek. Inform. dan Sist. Inf., vol. 10, no. 3, pp. 131–144, 2023, [Online]. Available: http://jurnal.mdp.ac.id
I. Ramadhani and M. Megawati, “Implementasi Algoritma K-Means Untuk Klustering Data Produktivitas Kelapa Sawit,” Indones. J. Inform. Res. Softw. Eng., vol. 3, no. 1, pp. 56–64, 2023, doi: 10.57152/ijirse.v3i1.488.
D. Cahya, M. Hidayat, and F. Asnawi, “Journal of Engineering and Informatic Implementasi Algoritma K-Means Clustering untuk Menentukan Calon,” vol. 1, no. 1, pp. 28–34, 2022, doi: 10.56854/jei.v1i1.16.
E. Muningsih, N. Hasan, and G. B. Sulistyo, “Penerapan Metode Principle Component Analysis (PCA) untuk Clustering Data Kunjungan Wisatawan Mancanegara ke Indonesia,” Bianglala Inform., vol. 8, no. 1, pp. 58–62, 2020, doi: 10.31294/bi.v8i1.8470.
J. Nasir, “Penerapan Data Mining Clustering Dalam Mengelompokan Buku Dengan Metode K-Means,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 11, no. 2, pp. 690–703, 2021, doi: 10.24176/simet.v11i2.5482.
A. T. Basalamah and R. Setyadi, “Penerapan Algoritma K-Means Clustering Pada Tingkat Penyelesaian Pendidikan Di Provinsi Indonesia,” J. Inform. dan Teknol. Komput., vol. 4, no. 2, pp. 114–121, 2023, [Online]. Available: https://ejurnalunsam.id/index.php/jicom/
Sutisna and N. M. Yuniar, “Klasifikasi Kualitas Air Bersih Menggunakan Metode Naïve baiyes,” J. Sains dan Teknol., vol. 5, no. 1, pp. 243–246, 2023, [Online]. Available: https://doi.org/10.55338/saintek.v5i1.1383
Y. Suhanda, I. Kurniati, and S. Norma, “Penerapan Metode Crisp-DM Dengan Algoritma K-Means Clustering Untuk Segmentasi Mahasiswa Berdasarkan Kualitas Akademik,” J. Teknol. Inform. dan Komput., vol. 6, no. 2, pp. 12–20, 2020, doi: 10.37012/jtik.v6i2.299.
E. N. R. Khakim, A. Hermawan, and D. Avianto, “Implementasi Correlation Matrix Pada Klasifikasi Dataset Wine,” JIKO (Jurnal Inform. dan Komputer), vol. 7, no. 1, p. 158, 2023, doi: 10.26798/jiko.v7i1.771.
S. R. Hani, “Clustering Data Pencari Kerja Menurut Tingkat Pendidikan Menggunakan Algoritma K-Means,” J. Minfo Polgan, vol. 12, no. 1, pp. 1–14, 2023, doi: 10.33395/jmp.v12i1.12217.
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Copyright (c) 2024 Putri Chandra Iranti, Dedy Kurniawan, M Rudi Sanjaya, Ahmad Rifai, M Husni Syahbani, Gabriel Ekoputra Hartono Cahyadi, Purwita Sari
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