Implementation of Random Forest Algorithm for Classification of Eligibility For Social Assistance Recipients In Information Systems
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Dec 26, 2024
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Abstract
This study aims to develop a web-based information system for classifying the eligibility of social assistance (BANSOS) recipients using the Random Forest algorithm in the Bagan Batu Kota Subdistrict. The system is designed to assist local authorities in identifying BANSOS recipients more accurately and efficiently, minimize errors, and enhance distribution fairness. A quantitative research method was employed, with data collection techniques including observation, interviews, literature review, and document analysis. The dataset consists of 1,100 samples with features such as income, family size, and housing conditions. The Random Forest algorithm was implemented by building a classification model based on training and testing data. The evaluation showed a system accuracy rate of 97%, with a classification error of only 3%. The system provides features for recipient data management, field validation, and automated reporting, supporting more precise decision-making. The results of this study are expected to offer a solution for more effective and transparent social assistance distribution.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Mita Trianda, & Triase Triase. (2024). Implementation of Random Forest Algorithm for Classification of Eligibility For Social Assistance Recipients In Information Systems . Jurnal E-Komtek (Elektro-Komputer-Teknik), 8(2), 275-286. https://doi.org/10.37339/e-komtek.v8i2.2197
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[2] Haris Kurniawan, Sarjon Defit, and Sumijan, “Data Mining Menggunakan Metode K-Means Clustering Untuk Menentukan Besaran Uang Kuliah Tunggal,” J. Appl. Comput. Sci. Technol., vol. 1, no. 2, pp. 80–89, 2020, doi: 10.52158/jacost.v1i2.102.
[3] B. F. Supit and E. Lumingkewas, “Implementasi Kebijakan Bantuan Sosial Tunai Di Kelurahan Talikuran Utara Kecamatan Kawangkoan Utara Minahasa,” Acad. Educ. J., vol. 14, no. 2, pp. 1059–1068, 2023, doi: 10.47200/aoej.v14i2.1981.
[4] D. Librado, T. Prabawa, and H. A. Triyanto, “Klasterisasi Penerima Bantuan Sosial Menggunakan Metode Simple Additive Weighting,” JIKO (Jurnal Inform. dan Komputer), vol. 7, no. 1, p. 30, 2023, doi: 10.26798/jiko.v7i1.677.
[5] A. Aldiyansyah, A. Irma Purnamasari, and I. Ali, “Perbandingan Tingkat Akurasi Algoritma Decision Tree Dan Random Forest Dalam Mengklasifikasi Penerima Bantuan Sosial Bpnt Di Desa Slangit,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 127–132, 2024, doi: 10.36040/jati.v8i1.8290.
[6] Ardi Ramdani, Christian Dwi Sofyan, Fauzi Ramdani, Muhamad Fauzi Arya Tama, and Muhammad Angga Rachmatsyah, “Algoritma Klasifikasi Data Mining Untuk Memprediksi Masyarakat Dalam Menerima Bantuan Sosial,” J. Ilm. Sist. Inf., vol. 1, no. 2, pp. 39–47, 2022, doi: 10.51903/juisi.v1i2.363.
[7] M. Fardi, Inka Nusamuda Pratama, Darmansyah, and Amil, “Transparansi Pendataan Program Bantuan Sosial Tunai dalam Penanggulangan Kemiskinan di Lingkungan Bebidas Kelurahan Pegesangan Kota Mataram,” J. Soc. Policy Issues, vol. 3, pp. 150–155, 2023, doi: 10.58835/jspi.v3i3.209.
[8] S. Lestari and S. Agustiansyah, “Implementasi Data Mining Clustering Data Penduduk Miskin Menggunakan Metode Algoritma C4.5 Untuk Merekomendasikan Bantuan Sosial Pada RT. 05/01 Kelurahan Jati Mekar Kecamatan Jati Asih Kota Bekasi,” J. Tek. Elektro dan Komputasi, vol. 5, no. 1, pp. 95–104, 2023.
[9] M. Yazid, D. A. Sani, and N. M. Anggadimas, “Sistem Rekomendasi Penerima Bantuan Sosial APBD Menggunakan Metode Support Vector Machine,” Ilk. J. Comput. Sci. Appl. Informatics, vol. 6, no. 1, pp. 54–61, 2024, doi: 10.28926/ilkomnika.v6i1.619.
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[12] D. P. Sinambela, H. Naparin, M. Zulfadhilah, and N. Hidayah, “Implementasi Algoritma Decision Tree dan Random Forest dalam Prediksi Perdarahan Pascasalin,” J. Inf. dan Teknol., vol. 5, no. 3, pp. 58–64, 2023, doi: 10.60083/jidt.v5i3.393.
[13] M. I. Baihaqi, A. Syaripudin, and F. A. Nugroho, “Implementation Of The Random Forest Algorithm In Stock Price Predictions Based On Historical Data Implementasi Algoritma Random Forest Pada Prediksi Harga Saham Berdasarkan Data Historis,” Jubitek J. BIG DATA DAN Teknol. Inf., vol. 1, pp. 42–51, 2023.
[14] Yoga Religia, Agung Nugroho, and Wahyu Hadikristanto, “Klasifikasi Analisis Perbandingan Algoritma Optimasi pada Random Forest untuk Klasifikasi Data Bank Marketing,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 187–192, 2021, doi: 10.29207/resti.v5i1.2813.
[15] E. R. B. Sebayang, Y. H. Chrisnanto, and Melina, “Klasifikasi Data Kesehatan Mental di Industri Teknologi Menggunakan Algoritma Random Forest,” IJESPG J., vol. 1, no. 3, pp. 237–253, 2023.
[16] A. Rustamana, P. Wahyuningsih, M. F. Azka, and P. Wahyu, “Penelitian Metode Kuantitatif,” Sindoro Cendikia Pendidik., vol. 5, no. 6, pp. 1–10, 2024.
[17] B. Setiawan and T. Triase, “Implementasi Desain Ui/Ux Aplikasi Ourticle Ke Dalam Aplikasi Berbasis Android,” SIBATIK J. J. Ilm. Bid. Sos. Ekon. Budaya, Teknol. dan Pendidik., vol. 2, no. 3, pp. 805–818, 2023, doi: 10.54443/sibatik.v2i3.665.
[18] S. Suendri, T. Triase, and S. Afzalena, “Implementasi Metode Job Order Costing Pada Sistem Informasi Produksi Berbasis Web,” JS (Jurnal Sekolah), vol. 4, no. 2, pp. 97–106, 2020.
[19] M. Prabowo, METODOLOGI PENGEMBANGAN SISTEM INFORMASI. LP2M IAIN Salatiga, 2020.