Optimization of Social Assistance Recipient Determination using Gradient Boosting Algorithm

Windi Herlita Vidila; Rudi Kurniawan; Saeful Anwar

doi:10.59934/jaiea.v4i2.773

Authors

Windi Herlita Vidila STMIK IKMI Cireon
Rudi Kurniawan STMIK IKMI Cirebon
Saeful Anwar STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i2.773

Keywords:

Gradient Boosting Algorithm, Classification, Social Assistance Recipient, Optimization, Gradient Boosted Trees, RapidMiner

Abstract

This research aims to classify social assistance recipients to ensure the accuracy of aid distribution by utilizing the Gradient Boosting algorithm on RapidMiner. The data used is data on residents who are categorized as receiving and not receiving social assistance in Cicadas village with a total dataset consisting of 670 entries with 18 attributes that will be divided equally between eligible and ineligible recipients. This research uses KDD (Knowledge Discover in Database) analysis which includes the stages of data selection, pre-processing, transformation, modeling, and interpretation of results. This research uses a quantitative approach, focusing on the distribution of datasets in a ratio of 70:30 with a stratified sampling technique for training and testing purposes. The experimental results show that the selected method is effective in classifying recipients by obtaining an accuracy of 91.67%, this accuracy result can be relied upon to support decision-making in social assistance distribution. The findings underscore the potential of machine learning in optimizing social welfare initiatives by improving target accuracy and ensuring aid reaches the rightful recipients.

Downloads

Download data is not yet available.

References

M. Faid, M. Jasri, and T. Rahmawati, “Perbandingan Kinerja Tool Data Mining Weka Dan Rapidminer Dalam Algoritma Klasifikasi,” Teknika, vol. 8, no. 1, pp. 11–16, 2019, doi: 10.34148/teknika.v8i1.95.

R. Pahlevi, E. S. Negara, T. Sutabri, and M. I. Herdiansyah, “Penerapan Metode Naive Bayes Untuk Menentukan Klasifikasi Kelayakan Penerimaan Bantuan Rehabilitasi Dan Pembangunan Sekolah Pada Dinas Pendidikan Dan Kebudayaan Kabupaten Banyuasin,” J. Teknol. Inform. Dan Komput., vol. 9, no. 2, pp. 1176–1188, 2023, doi: 10.37012/jtik.v9i2.1790.

A. Aribowo, R. Kuswandhie, and Y. Primadasa, “Penerapan Dan Implementasi Algoritma CART Dalam Penentuan Kelayakan Penerima Bantuan PKH Di Desa Ngadirejo,” Cogito Smart J., vol. 7, no. 1, pp. 40–51, 2021, doi: 10.31154/cogito.v7i1.293.40-51.

P. Oosterhoff and R. M. Yunus, “The Effects of Social Assistance Interventions on Gender, Familial and Household Relations Among Refugees and Displaced Populations: A Review of the Literature on Interventions in Syria, Iraq, Jordan and Lebanon,” 2022, doi: 10.19088/basic.2022.011.

M. Kivrak, “Breast Cancer Risk Prediction With Stochastic Gradient Boosting,” Clin. Cancer Investig. J., vol. 11, no. 2, pp. 26–31, 2022, doi: 10.51847/21qrrklo4y.

D. A. Keefer and C. Blake, “The Reproducible Data Reuse (ReDaR) Framework to Capture and Assess Multiple Data Streams,” Proc. Assoc. Inf. Sci. Technol., vol. 58, no. 1, pp. 230–240, 2021, doi: 10.1002/pra2.451.

S. Silvianetri, I. Irman, Z. Zubaidah, P. Yeni, and R. Syafitri, “Student Preferences Watching Youtube and Its Implications for Wellbeing Counseling,” 2022, doi: 10.4108/eai.11-10-2021.2319487.

J. Wang, J. Xu, C. Zhao, Y. Peng, and H. Wang, “An Ensemble Feature Selection Method for High-Dimensional Data Based on Sort Aggregation,” Syst. Sci. Control Eng., vol. 7, no. 2, pp. 32–39, 2019, doi: 10.1080/21642583.2019.1620658.

F. Septian, “Optimasi Klusterisasi pada Lama Tempo Pekerjaan Berbasis Gradient Boost Algorithm,” Indones. J. Inf. Technol., 2023.

S. Hosen and R. Amin, “Significant of Gradient Boosting Algorithm in Data Management System,” Eng. Int., vol. 9, no. 2, pp. 85–100, 2021, doi: 10.18034/ei.v9i2.559.

N. Endut, W. M. A. F. W. Hamzah, I. Ismail, M. K. Yusof, Y. A. Baker, and H. Yusoff, “A Systematic Literature Review on Multi-Label Classification Based on Machine Learning Algorithms,” Tem J., pp. 658–666, 2022, doi: 10.18421/tem112-20.

S. Zulaikhah Hariyanti Rukmana, A. Aziz, and W. Harianto, “Optimasi Algoritma K-Nearest Neighbor (Knn) Dengan Normalisasi Dan Seleksi Fitur Untuk Klasifikasi Penyakit Liver,” JATI (Jurnal Mhs. Tek. Inform., vol. 6, no. 2, pp. 439–445, 2022, doi: 10.36040/jati.v6i2.4722.

D. Ananda, A. Pertiwi, and M. A. Muslim, “Journal of Numerical Optimization and Improvement accuracy of gradient boosting in app rating prediction on google playstore,” vol. 1, no. 2, 2023.