Perbandingan Metode C4.5 dan K-Nearest Neighbor dalam Klasifikasi Status Penerima Bantuan Program Keluarga Harapan di Desa Selat
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Kemiskinan merupakan permasalahan sosial yang kompleks dan menjadi tantangan utama bagi pemerintah Indonesia. Salah satu upaya penanggulangan kemiskinan adalah melalui Program Keluarga Harapan yang bertujuan untuk meningkatkan kesejahteraan masyarakat penerima program, yang dipilih melalui proses seleksi. Penelitian ini bertujuan untuk membangun model klasifikasi calon penerima bantuan dengan membandingkan efektivitas algoritma C4.5 dan K-Nearest Neighbor. Penelitian ini menggunakan 538 entri data dengan enam atribut, yaitu pekerjaan, kesehatan, pendidikan, kesejahteraan sosial, kepemilikan rumah, dan kepemilikan aset berupa lahan. Hasil pengujian menunjukkan bahwa algoritma C4.5 menghasilkan nilak akurasi, presisi, dan recall sebesar 100%, serta berhasil mereduksi jumlah atribut menjadi empat atribut. Sementara itu, untuk algoritma K-Nearest Neighbor dengan nilai k = 21 memperoleh nilai akurasi sebesar 99,07% dan presisi sebesar 98,46%. Temuan ini menunjukkan bahwa algoritma C4.5 lebih efektif daripada algoritma K-Nearest Neighbor dalam klasifikasi penerima bantuan Program Keluarga Harapan, sehingga dapat dijadikan alternatif dalam proses pengambilan keputusan.
Comparison of the C4.5 and K-Nearest Neighbor Methods in Classifying the Status of Recipients of the Family Hope Program Assistance in Selat Village
Abstract
Poverty is a complex social issue and remains a major challenge for the Indonesian government. One of the efforts to alleviate poverty is the Family Hope Program, which aims to improve the welfare of beneficiary households selected through a screening process. This study aims to develop a classification model for prospective beneficiaries by comparing the effectiveness of the C4.5 and K-Nearest Neighbor algorithms. The study utilizes 538 data entries with six attributes: employment, health, education, social welfare, home ownership, and land asset ownership. The evaluation results show that the C4.5 algorithm achieved an accuracy, precision, and recall of 100%, while also reducing the number of attributes to four. Meanwhile, the K-Nearest Neighbor algorithm with k = 21 achieved an accuracy of 99,07% and a precision of 98,46%. These findings indicate that the C4.5 algorithm is more effective than the K-Nearest Neighbor algorithm in classifying beneficiaries of the Family Hope Program, and thus can serve as an alternative for decision-making processes.
Anggraeni, A. P., & Nugroho, A. A. (2022). Evaluasi Kebijakan PKH (Program Keluarga Harapan) Di Indonesia. Journal of Public Policy and Applied Administration, 4(2), 39–54.
Atma, Y. D., & Setyanto, A. (2018). Perbandingan Algoritma C4.5 dan K-NN dalam Identifikasi Mahasiswa Berpotensi Drop Out. Metik Jurnal, 2(2), 31–37.
Bhatia, N. (2010). Survey of Nearest Neighbor Techniques. International Journal of Advanced Computer Science and Applications, 8(2).
Bhinadi, A. (2017). Penanggulangan Kemiskinan dan Pemberdayaan Masyarakat?: Studi Kasus Daerah Istimewa Yogyakarta. In DEEPUBLISH Publisher CV Budi Utama (hal. 89–92).
Dananjaya, D., Werdiningsih, I., & Semiati, R. (2019). Decision support system for classification of early childhood diseases using principal component analysis and k-nearest neighbors classifier. Journal of Information Systems Engineering and Business Intelligence, 5(1), 13. https://doi.org/10.20473/jisebi.5.1.13-22
Hadianto, N., Novitasari, H. B., & Rahmawati, A. (2019). Klasifikasi Peminjaman Nasabah Bank Menggunakan Metode Neural Network. Jurnal Pilar Nusa Mandiri, 15(2), 163–170.
Kementerian Sosial Republik Indonesia. (2021). Pedoman Pelaksanaan Program Keluarga Harapan (PKH).
Khoiruzzaman, N., Ramadhani, R., & Junaidi, A. (2021). Hasil klasifikasi algoritma backpropagation dan k-nearest neighbor pada cardiovascular disease. Journal of Dinda Data Science Information Technology and Data Analytics, 1(1), 17-27. https://doi.org/10.20895/dinda.v1i1.141
Kusrini, & Luthfi, E. T. (2009). Algoritma Data Mining. Buku Algoritma Data Mining (T. A. Prabawati (ed.)). CV Andi Offset (Penerbit Andi).
Melati, A. M., Sudrajat, & Burhany, D. I. (2021). Pengaruh Belanja Pendidikan, Belanja Kesehatan dan Belanja Bantuan Sosial terhadap Kemiskinan pada Kabupaten dan Kota di Provinsi Jawa Barat. Indonesian Accounting Research Journal, 1(3), 422–430.
Nofriani, N. (2020). Machine learning application for classification prediction of household’s welfare status. Jitce (Journal of Information Technology and Computer Engineering), 4(02), 72-82. https://doi.org/10.25077/jitce.4.02.72-82.2020
Puspita, D., Aminah, S., & Arif, A. (2022). Prediction System for Credit Eligibility Using C4.5 Algorithm. Journal of Informatics and Telecommunication Engineering, 6(1), 148–156.
Siska, F., & Heni, S. (2021). Analisis Data Hasil Diagnosa Untuk Klasifikasi Gangguan Kepribadian Menggunakan Algoritma C4.5. Jurnal Teknologi dan Sistem Informasi (JTSI), 2(4), 89–95.
Suryahadi, A., Al Izzati, R., & Suryadarma, D. (2020). The Impact of COVID-19 Outbreak on Poverty: An Estimation for Indonesia. In SMERU Working Paper: Vol. April.
Ulya, S., Soeleman, M., & Budiman, F. (2021). Optimasi parameter k pada algoritma k-nn untuk klasifikasi prioritas bantuan pembangunan desa. Techno Com, 20(1), 83-96. https://doi.org/10.33633/tc.v20i1.4215
Wang, Y., Guo, C., Xiao, C., & Yang, W. (2022). Combining imputation method and feature weighting algorithms to improve the classification accuracy of incomplete data. Journal of Physics Conference Series, 2171(1), 012038. https://doi.org/10.1088/1742-6596/2171/1/012038
Wibowo, A., Kasih, P., & Farida, I. (2024). Sistem bantu penentuan konsentrasi mahasiswa menggunakan metode k-nearest neighbor classification. Stains, 3(1), 370-379. https://doi.org/10.29407/stains.v3i1.4343
Yandi Saputra, A., & Primadasa, Y. (2018). Penerapan Teknik Klasifikasi Untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritma K-Nearest Neighbour Implementation of Classification Method to Predict Student Graduation Using K-Nearest Neighbor Algorithm. Techno.Com, 17(4), 9.
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