Utilizing Machine Learning For Identifying Potential Beneficiaries of Family Hope Program

Muhammad Abdurrohim, Lena Magdalena, Muhammad Hatta

Abstract


In identifying families who are entitled to PKH assistance there are often obstacles such as RTSM identification errors, this is caused by the negligence of officials so that they are not accurate in making confirmations in large numbers. An automated system that can predict RTSM can be a solution to this problem, a system based on a machine learning model. This study aims to analyze the machine learning model Decision Tree C45 (DT C45), K-Nearest Neighbor (KNN), and Naive Bayes (NB). The results showed that Decision Tree C45 was the optimal model to implement with an accuracy value of 70%.

Keywords


PKH; Artificial Intelligence; Decision Tree C4.5; K-Nearest Neighbor; Naïve Bayes.

Full Text:

PDF

References


I. A. Sobari and R. A. Zuama, “Pendekatan Machine Learning dalam Memprediksi Keluarga Penerima Program PKH,” J. Tek. Komput. AMIK BSI, vol. 9, no. 1, pp. 61–64, 2023, doi: 10.31294/jtk.v4i2.

A. Ahmadi and S. Hartati, “Penerapan Fuzzy C-Means dalam Sistem Pendukung Keputusan untuk Penentuan Penerima Bantuan Langsung Masyarakat ( BLM ) PNPM- MPd ( Studi Kasus PNPM-MPd Kec . Ngadirojo Kab . Pacitan ) 2007 Pemerintah Indonesia mencanangkan Program Nasional Pemberdayaan Mas,” Berk. MIPA, vol. 23, no. 3, pp. 264–273, 2013.

R. Puspita and A. Widodo, “Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS,” J. Inform. Univ. Pamulang, vol. 5, no. 4, p. 646, 2021, doi: 10.32493/informatika.v5i4.7622.

S. Lestari, A. Akmaludin, and M. Badrul, “Implementasi Klasifikasi Naive Bayes Untuk Prediksi Kelayakan Pemberian Pinjaman Pada Koperasi Anugerah Bintang Cemerlang,” PROSISKO J. Pengemb. Ris. dan Obs. Sist. Komput., vol. 7, no. 1, pp. 8–16, 2020, doi: 10.30656/prosisko.v7i1.2129.

B. Padilah, A. R. Pratama, and A. R. Juwita, “Analysis of Sentiment Adiraku App Reviews on Google Play Store Using Vector Machine Support Algorithm and Naïve Bayes,” J. Sisfotek Glob., vol. 13, no. 1, p. 8, 2023, doi: 10.38101/sisfotek.v13i1.2943.

N. Iriadi and N. Nuraeni, “Kajian Penerapan Metode Klasifikasi Data Kelayakan Kredit Pada Bank,” J. Tek. Komput. AMIK BSI, vol. II, no. 1, pp. 132–137, 2016, doi: https://doi.org/10.31294/jtk.v2i1.371.

S. R. Cholil, T. Handayani, R. Prathivi, and T. Ardianita, “IJCIT (Indonesian Journal on Computer and Information Technology) Implementasi Algoritma Klasifikasi K-Nearest Neighbor (KNN) Untuk Klasifikasi Seleksi Penerima Beasiswa,” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 6, no. 2, pp. 118–127, 2021, doi: https://doi.org/10.31294/ijcit.v6i2.10438.

Y. Yahya and W. Puspita Hidayanti, “Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Efektivitas Penjualan Vape (Rokok Elektrik) pada ‘Lombok Vape On,’” Infotek J. Inform. dan Teknol., vol. 3, no. 2, pp. 104–114, 2020, doi: 10.29408/jit.v3i2.2279.

R. Tullah, S. Maisaroh, and I. Sumpena, “Implementasi Machine Learning pada Aplikasi Pendeteksi Konten Pornografi,” vol. 2, no. 2, 2020, doi: http://dx.doi.org/10.38101/ajcsr.v2i2.283.

R. Retnosari, “Analisa Kelayakan Kredit Usaha Mikro Berjalan pada Perbankan dengan Metode Naive Bayes,” PROSISKO J. Pengemb. Ris. dan Obs. Sist. Komput., vol. 8, no. 1, pp. 53–59, 2021, [Online]. Available: https://e-jurnal.lppmunsera.org/index.php/PROSISKO/article/view/2848

M. R. Noviansyah, T. Rismawan, and D. M. Midyanti, “Penerapan Data Mining Menggunakan Metode K-Nearest Neighbor Untuk Klasifikasi Indeks Cuaca Kebakaran Berdasarkan Data Aws (Automatic Weather Station) (Studi Kasus: Kabupaten Kubu Raya),” J. Coding, Sist. Komput. Untan, vol. 06, no. 2, pp. 48–56, 2018, doi: http://dx.doi.org/10.26418/coding.v6i2.26672.

I. Istiadi et al., “Classification of Tempeh Maturity Using Decision Tree and Three Texture Features,” Int. J. Informatics Vis., vol. 6, no. 4, pp. 883–889, 2022, doi: 10.30630/joiv.6.4.983.

M. B. Ryando, A. R. Mariana, and R. A. Hakim, “Sistem Pendukung Keputusan Pemilihan Sepeda Motor Second Terbaik di Kelas Matic 150cc Menggunakan Metode AHP dan TOPSIS,” Acad. J. Comput. Sci. Res., vol. 5, no. 1, p. 47, 2023, doi: 10.38101/ajcsr.v5i1.611.

F. Wiza and B. Febriadi, “Classification Analysis Using C4.5 Algorithm To Predict The Level of Graduation of Nurul Falah Pekanbaru High School Students,” IJISTECH (International J. Inf. Syst. Technol., vol. 2, no. 2, p. 43, 2019, doi: 10.30645/ijistech.v2i2.21.

D. Maulana and Y. Religia, “Genetic Algorithm Optimization on Nave Bayes for Airline Customer Satisfaction Classification,” JISA(Jurnal Inform. dan Sains), vol. 4, no. 2, pp. 121–126, 2021, doi: 10.31326/jisa.v4i2.925.




DOI: http://dx.doi.org/10.38101/sisfotek.v13i2.9718

Refbacks

  • There are currently no refbacks.


 

JURNAL SISFOTEK GLOBAL

Organized by: Research Center and Community Development
Published by: Institut Teknologi dan Bisnis Bina Sarana Global
Jl. Aria Santika No.43A, Margasari, Kec. Karawaci, Kota Tangerang, Banten 15114
Phone. +62 552 2727
Email: lppm@global.ac.id

INDEXED BY:

   


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License..
Based on a work at https://journal.global.ac.id/index.php/sisfotek/index.