Classification of Poverty Reduction Program Recipients with Neural Network Algorithm in East Kotawaringin Communities

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Nurahman
Dwi Tjahjo Seabtian

Abstract

Indonesia has a poverty rate of 24.79 million. Kotawaringin Timur is inhabited by 27.4 thousand people with an income of less than Rp. 416,777/month. The provision must be right on target, and recipients of assistance must use the assistance following the rules determined by the government.  This research is to formulate a conceptual model of the Neural Network Algorithm structure that can be used to predict the use of assistance funds.  This research applies the Knowledge Discovery Data methodology with Neural Network Algorithm for classification. The research has shown that the application of the Neural Network Algorithm with feature selection can improve performance with values AUC=0.974, CA=0.977, F1=0.977, Precision=0.977, Recall=0.977. The level of performance value for accuracy of Neural Network Algorithm in classifying is the excellent classification category. The recommended Neural Network parameter models are Neurons in hidden layers 100, Activation ReLu, Solver Adam, Regularization, α = 0.0001, and a Maximal number of iterations 200.

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Author Biography

Dwi Tjahjo Seabtian, Universitas Darwan Ali

Program Studi S1-Sistem Informasi Universitas Darwan Ali

How to Cite
Nurahman, & Seabtian, D. T. (2021). Classification of Poverty Reduction Program Recipients with Neural Network Algorithm in East Kotawaringin Communities. Jurnal E-Komtek (Elektro-Komputer-Teknik), 5(2), 190-202. https://doi.org/10.37339/e-komtek.v5i2.751

References

[1] BPS, “Profil Kemiskinan di Indonesia September 2019,” Jakarta, Jan. 2020. Accessed: Oct. 20, 2020. [Online]. Available: https://www.bps.go.id/pressrelease/2020/01/15/1743/persentase-penduduk-miskin-september-2019-turun-menjadi-9-22-persen.html.
[2] T. B. Kotim, “Garis Kemiskinan dan Penduduk Miskin 2017-2019,” Badan Pusat Statistik Kabupaten Kotawaringin Timur, 2020. https://kotimkab.bps.go.id/indicator/23/760/1/garis-kemiskinan-dan-penduduk-miskin.html (accessed Oct. 25, 2020).
[3] Gugus Tugas Percepatan Penanganan COVID-19, “Peta Sebaran Kasus COVID-19 di Indonesia,” covid19.go.id, 2020. https://covid19.go.id/peta-sebaran-covid19(accessed Oct. 23, 2020).
[4] A. Sinkov, G. Asyaev, A. Mursalimov, and K. Nikolskaya, “Neural networks in data mining,” 2016, doi: 10.1109/ICIEAM.2016.7911596.
[5] N. Hadianto, H. B. Novitasari, and A. Rahmawati, “Klasifikasi Peminjaman Nasabah Bank Menggunakan Metode Neural Network,” J. Pilar Nusa Mandiri, 2019, doi: 10.33480/pilar.v15i2.658.
[6] A. P. Windarto, “Implementasi JST Dalam Menentukan Kelayakan Nasabah Pinjaman KUR Pada Bank Mandiri Mikro Serbelawan Dengan Metode Backpropogation,” J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 1, no. 1, pp. 12–23, 2017, doi: 10.30645/j-sakti.v1i1.25.
[7] N. Nurahman and P. Prihandoko, “Perbandingan Hasil Analisis Teknik Data Mining ‘Metode Decision Tree, Naive Bayes, Smo Dan Part’ Untuk Mendiagnosa Penyakit Diabetes Mellitus,” J. Inf., vol. 4, no. 1, pp. 39–44, 2019, doi: 10.25139/inform.v4i1.1403.
[8] T. Makmur, “Teknologi Informasi :Dampak dan Implikasi Bagi Perpustakaan, Pustakawan Serta Pemustaka,” INFO Bibl. J. Perpust. dan Ilmu Inf., vol. 1, no. 1, pp. 65–74, 2019, doi: 10.24036/ib.v1i1.12.
[9] W. Setiawan, “Era Digital dan Tantangannya,” Semin. Nas. Pendidik. 2017, 2017, Accessed: Sep. 30, 2020. [Online]. Available: http://eprints.ummi.ac.id/151/2/1. Era Digital dan Tantangannya.pdf.
[10] Y. A. Prasetyo and A. Djauhar, “Mendorong Profesionalisme Pers Melalui Verifikasi Perusahaan Pers,” J. DEWAN PERS, vol. 14, pp. 1–72, Jun. 2017, Accessed: Oct. 03, 2020. [Online]. Available: https://dewanpers.or.id/assets/ebook/jurnal/715-BUKU JURNAL DEWAN PERS 14 INDONESIA.pdf.
[11] Homeland Security, “Recommended Practice: Improving Industrial Control Systems Cybersecurity with Defense-In-Depth Strategies,” Ics-Cert, 2016, Accessed: Oct. 27, 2020. [Online]. Available: https://us-cert.cisa.gov/sites/default/files/recommended_practices/NCCIC_ICS-CERT_Defense_in_Depth_2016_S508C.pdf.
[12] G. Kesavaraj and S. Sukumaran, “A study on classification techniques in data mining,” Jul. 2013, doi: 10.1109/ICCCNT.2013.6726842.
[13] M. J. Zaki and W. Meira, Jr, Data Mining and Machine Learning. Cambridge University Press, 2020.
[14] R. Sowmya and K. R. Suneetha, “Data Mining with Big Data,” in Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017, Feb. 2017, pp. 246–250, doi: 10.1109/ISCO.2017.7855990.
[15] J. Suntoro, Data Mining Algoritma dan Implementasi dengan Pemrograman PHP, 1st ed. Jakarta: Elex Media Komputindo, 2019.
[16] A. Rohman, “Komparasi Metode Klasifikasi Data Mining Untuk Prediksi Penyakit Jantung,” Neo Tek., vol. 2, no. 2, pp. 21–28, Dec. 2017, doi: 10.37760/neoteknika.v2i2.766.
[17] C. Cristina and A. Kurniawan, “Sejarah, Penerapan, dan Analisis Resiko dari Neural Network: Sebuah Tinjauan Pustaka,” J. Inform. J. Pengemb. IT, vol. 3, no. 2, pp. 259–265, 2018, doi: 10.30591/jpit.v3i2.890.
[18] S. W. Ren, X. Qi, and Y. Q. Wang, “Serum amyloid A and pairing formyl peptide receptor 2 are expressed in corneas and involved in inflammation-mediated neovascularization,” Int. J. Ophthalmol., vol. 7, no. 2, pp. 187–193, Apr. 2014, doi: 10.3980/j.issn.2222-3959.2014.02.01.
[19] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Elsevier, 2012.
[20] N. Saputra, K. Tania, and R. Heroza, “Penerapan Knowledge Management System (Kms) Menggunakan Teknik Knowledge Data Discovery (Kdd) Pada Pt Pln (Persero) Ws2Jb Rayon Kayu Agung,” J. Sist. Inf., 2016.
[21] F. Gorunescu, Data mining: Concepts, models and techniques, vol. 12. Springer, Berlin, Heidelberg, 2011.