Algoritma Backpropagation dalam Memprediksi Jumlah Angka Kemiskinan di Provinsi Sumatera Utara


  • Roimal Hafizi Purba * Mail STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Muhammad Zarlis Universitas Sumatera Utara, Medan, Indonesia
  • Indra Gunawan STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • (*) Corresponding Author
Keywords: Backpropagation; Poverty; ANN; Province; North Sumatra

Abstract

Poverty is one of the phenomenal problems that Indonesia faces every year. Therefore, this study was conducted with the aim to predict the number of poverty figures by district/city in the province of North Sumatra. The algorithm used to conduct this research is the backpropagation algorithm. This algorithm is one algorithm that is often used to make data predictions. The data used is the data of the poor population in North Sumatra in 2013-2017, which was sourced from the Central Statistics Agency of North Sumatra. Based on this data will be formed and determined the network architecture model used with the Backpropagation algorithm, including 3-9-1, 3-16-1, 3-18-1, 3-23-1, and 3-40-1. From these 5 models after training and testing, it was found that the best architectural model was 3-23-1. The accuracy rate of this architectural model is 97% with an MSE test value of 0.00359. The results of this study are in the form of predictions of the number of poverty in North Sumatra for the next 5 years. The results of this study are expected to be a reference for the regional government of North Sumatra to see the level of development of poverty in North Sumatra for the coming year.

Downloads

Download data is not yet available.

References

Afriliansyah, T., Parulian, P., Ulva, A. F., Simanjuntak, M. Y., Wanto, A., Sihombing, D., … Ginantra, N. (2019). Implementation of Bayesian Regulation Algorithm for Estimation of Production Index Level Micro and Small Industry. Journal of Physics: Conference Series, 1255(1), 1–6.

Atalay, R. (2015). The Education and the Human Capital to Get Rid of the Middle-income Trap and to Provide the Economic Development. Procedia - Social and Behavioral Sciences, 174, 969–976. https://doi.org/10.1016/j.sbspro.2015.01.720

Atika, D. (2018). Implementasi Algoritma Spritz dan Algoritma RC4A Dalam Skema Three-Pass Protocol Untuk Pengamanan Data.

Bhawika, G. W., Purwantoro, P., GS, A. D., Sudrajat, D., Rahman, A., Makmur, M., … Wanto, A. (2019). Implementation of ANN for Predicting the Percentage of Illiteracy in Indonesia by Age Group. Journal of Physics: Conference Series, 1255(1), 1–6.

Binti, M. T. (2016). Analisa Pengaruh Pertumbuhan Ekonomi Terhadap Penurunan Tingkat Kemiskinan di Kalimantan Tengah. Jurnal Komunikasi Bisnis Dan Manajemen, 3(6), 69–78.

Lubis, M. R., Saputra, W., Wanto, A., Andani, S. R., & Poningsih, P. (2019). Analysis of Artificial Neural Networks Method Backpropagation to Improve the Understanding Student in Algorithm and Programming. Journal of Physics: Conference Series, 1255(1), 1–6. https://doi.org/10.1088/1742-6596/1255/1/012032

Parlina, I., Wanto, A., & Windarto, A. P. (2019). Artificial Neural Network Pada Industri Non Migas Sebagai Langkah Menuju Revolusi Industri 4.0. InfoTekJar : Jurnal Nasional Informatika Dan Teknologi Jaringan, 4(1), 155–160.

Parulian, P., Tinambunan, M. H., Ginting, S., Gibran, M. K., Wanto, A., Muharram, L. O., … Bhawika, G. W. (2019). Analysis of Sequential Order Incremental Methods in Predicting the Number of Victims Affected by Disasters. Journal of Physics: Conference Series, 1255(1), 1–6.

Purba, I. S., Wanto, A., Riansah, R. M., Ahmad, Y., Siregar, S. P., Winanjaya, R., … Silitonga, H. (2019). Accuracy Level of Backpropagation Algorithm to Predict Livestock Population of Simalungun Regency in Indonesia Accuracy Level of Backpropagation Algorithm to Predict Livestock Population of Simalungun Regency in Indonesia. Journal of Physics: Conference Series, 1255(1), 1–6.

Purba, N. Z., Wanto, A., & Kirana, I. O. (2019). Implementation of ANN for Prediction of Unemployment Rate Based on Urban Village in 3 Sub-Districts of Pematangsiantar. International Journal of Information System & Technology, 3(1), 107–116.

Rubiyanah, Maria Magdalena Minarsih, L. B. H. (2016). Implementasi Program Nasional Pemberdayaan Masyarakat Mandiri Perkotaan Dalam Penanggulangan Kemiskinan. Journal Of Management, 2(2), 1–18.

Saputra, W., Hardinata, J. T., & Wanto, A. (2020). Resilient method in determining the best architectural model for predicting open unemployment in Indonesia. IOP Conference Series: Materials Science and Engineering, 725(1), 1–7. https://doi.org/10.1088/1757-899X/725/1/012115

Saputra, W., Poningsih, P., Lubis, M. R., Andani, S. R., Damanik, I. S., & Wanto, A. (2019). Analysis of Artificial Neural Network in Predicting the Fuel Consumption by Type of Power Plant. Journal of Physics: Conference Series, 1255(1), 1–5. https://doi.org/10.1088/1742-6596/1255/1/012069

Saragih, J. R., Hartama, D., & Wanto, A. (2020). Prediksi Produksi Susu Segar Di Indonesia Menggunakan Algoritma Backpropagation. Jurnal Ilmiah Informatika, 08(01), 58–65.

Setti, S., Wanto, A., Syafiq, M., Andriano, A., & Sihotang, B. K. (2019). Analysis of Backpropagation Algorithms in Predicting World Internet Users. Journal of Physics: Conference Series, 1255(1), 1–6. https://doi.org/10.1088/1742-6596/1255/1/012018

Sinaga, S. P., Wanto, A., & Solikhun, S. (2019). Implementasi Jaringan Syaraf Tiruan Resilient Backpropagation dalam Memprediksi Angka Harapan Hidup Masyarakat Sumatera Utara. Infomedia, 4(2), 81–88.

Siregar, E., Mawengkang, H., Nababan, E. B., & Wanto, A. (2019). Analysis of Backpropagation Method with Sigmoid Bipolar and Linear Function in Prediction of Population Growth. Journal of Physics: Conference Series, 1255(1), 1–6.

Siregar, S. P., Wanto, A., & Nasution, Z. M. (2018). Analisis Akurasi Arsitektur JST Berdasarkan Jumlah Penduduk Pada Kabupaten / Kota di Sumatera Utara. Seminar Nasional Sains & Teknologi Informasi (SENSASI), 526–536.

Situmorang, M., Wanto, A., & Nasution, Z. M. (2019). Architectural Model of Backpropagation ANN for Prediction of Population-Based on Sub-Districts in Pematangsiantar City. International Journal of Information System & Technology, 3(1), 98–106.

Sormin, M. K. Z., Sihombing, P., Amalia, A., Wanto, A., Hartama, D., & Chan, D. M. (2019). Predictions of World Population Life Expectancy Using Cyclical Order Weight / Bias. Journal of Physics: Conference Series, 1255(1), 1–6.

Sudiar, S. (2015). Konsolidasi Potensi Pembangunan: Studi Tentang Penanganan Kemiskinan di Kecamatan Muara Muntai-Kutai Kartanegara. Jurnal Paradigma, 4(2), 69–79.

Syahza, A. (2014). Model Pengembangan Daerah Tertinggal Dalam Upaya Percepatan Pembangunan Ekonomi Pedesaan. Ekuitas : Jurnal Ekonomi Dan Keuangan, 18(3), 365–386.

Wanto, A. (2018). Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Jumlah Kemiskinan Pada Kabupaten/Kota Di Provinsi Riau. Kumpulan JurnaL Ilmu Komputer (KLIK), 05(01), 61–74.

Wanto, A., Ginantra, N., Nurmawati, N., Bhawika, G. W., GS, A. D., Purwantoro, P., … Taufiqurrahman, T. (2019). Analysis of the Backpropagation Algorithm in Viewing Import Value Development Levels Based on Main Country of Origin. Journal of Physics: Conference Series, 1255(1), 1–6.

Wanto, A., & Hardinata, J. T. (2019). Estimasi Penduduk Miskin di Indonesia Sebagai Upaya Pengentasan Kemiskinan dalam Menghadapi Revolusi Industri 4.0. CESS (Journal of Computer Engineering System and Science), 4(2), 198–207.

Wanto, A., & Hardinata, J. T. (2020). Estimations of Indonesian poor people as poverty reduction efforts facing industrial revolution 4 . 0. IOP Conference Series: Materials Science and Engineering, 725(1), 1–8. https://doi.org/10.1088/1757-899X/725/1/012114

Wanto, A., Hartama, D., Bhawika, G. W., Chikmawati, Z., Hutauruk, D. S., Siregar, P. H., … Windarto, A. P. (2019). Model of Artificial Neural Networks in Predictions of Corn Productivity in an Effort to Overcome Imports in Indonesia. Journal of Physics: Conference Series, 1339(1), 1–6. https://doi.org/10.1088/1742-6596/1339/1/012057

Wanto, A., Parulian, P., Siahaan, H., Windarto, A. P., Afriliansyah, T., Saputra, W., … Irfan Sudahri Damanik. (2019). Analysis of the Accuracy Batch Training Method in Viewing Indonesian Fisheries Cultivation Company Development. Journal of Physics: Conference Series, 1255(1), 1–6. https://doi.org/10.1088/1742-6596/1255/1/012003

Windarto, A. P., Nasution, D., Wanto, A., Tambunan, F., Hasibuan, M. S., Siregar, M. N. H., … Nofriansyah, D. (2020). Jaringan Saraf Tiruan: Algoritma Prediksi dan Implementasi.

Zuhdiyaty, N., & Kaluge, D. (2017). Analisis Faktor-faktor yang Mempengaruhi Kemiskinan di Indonesia Selama Lima Tahun Terakhir (Studi Kasus Pada 33 Provinsi). Jurnal Jibeka, 11(2), 27–31.


Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Algoritma Backpropagation dalam Memprediksi Jumlah Angka Kemiskinan di Provinsi Sumatera Utara

Dimensions Badge
Article History
Submitted: 2020-06-25
Published: 2020-06-30
Abstract View: 149 times
PDF Download: 117 times
Section
Articles

Most read articles by the same author(s)

1 2 > >>