Resilient Backpropagation Neural Network on Prediction of Poverty Levels in South Sulawesi
DOI:
https://doi.org/10.30812/matrik.v20i1.726Keywords:
Accuracy, Resilient Algorithm, Backpropagation Neural Network, Proverty Levels, PredictionAbstract
Poverty is a topic that continues and is always discussed up to this time, as a benchmark indicator of how the level of welfare and prosperity in the lives of people in a country. Several attempts have been made by the central and regional governments to reduce poverty levels, including “Bantuan Langsung Tunai†(BLT) and the “Program Keluarga Harapan†(PKH). However, poverty reduction in Indonesia is still slowing down, including in South Sulawesi. Based on this, this study aims to predict poverty levels in South Sulawesi. Factors thought to influence poverty levels are the Human Development Index (HDI), the Open Unemployment Rate (TPT), and the Gross Regional Domestic Product (GRDP). The data used are data from 2010 to 2014. The method used is a backpropagation neural network with a resilient algorithm or better known as a resilient backpropagation neural network (RBNN). The results of the prediction of poverty levels using predictors of HDI, TPT, and GRDP showed that the analysis of the RBNN reached its optimum using architecture [3- 9 - 1] and reached convergence at the 81th iteration with an accuracy rate of 95.34%.
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References
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[11] L. M. Saini, “Peak Load Forecasting Using Bayesian Regularization, Resilient and Adaptive Backpropagation Learning Based Artificial Neural Networks,†Electric Power Systems Research, vol. 78, no. 7, pp. 1302–1310, Jul. 2008.
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[14] R. Y. Fa’rifah and Z. Busrah, “Backpropagation Neural Network untuk Optimasi Akurasi pada Prediksi Financial Distress Perusahaan,†Jurnal INSTEK (Informatika Sains dan Teknologi), vol. 2, no. 2, pp. 101–110, 2017.
[15] W. Watsuntorn, R. Khanongnuch, W. Chulalaksananukul, E. R. Rene, and P. N. L. Lens, “Resilient Performance of An Anoxic Biotrickling Filter for Hydrogen Sulphide Removal From A Biogas Mimic: Steady, Transient State and Neural Network Evaluation,†Journal of Cleaner Production, vol. 249, p. 119351, Mar. 2020.
 
[16] A. Poole and A. Kotsialos, “Second Order Macroscopic Traffic Flow Model Validation Using Automatic Differentiation with Resilient Backpropagation and Particle Swarm Optimisation Algorithms,†Transportation Research Part C: Emerging Technologies, vol. 71, pp. 356–381, Oct. 2016.
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[2] Badan Pusat Statistik, Profil Kemiskinan di Indonesia September 2018. Jakarta: Badan Pusat Statistik, 2019.
[3] Badan Pusat Statistik Provinsi Sulawesi Selatan, Profil Kemiskinan Sulawesi Selatan, September 2018. Makassar: Badan Pusat Statistik Provinsi Sulawesi Selatan, 2019.
[4] N. Zuhdiyaty and D. Kaluge, “Analisis Faktor-Faktor yang Mempengaruhi Kemiskinan di Indonesia Selama Lima Tahun Terakhir (Studi Kasus pada 33 Provinsi),†Jurnal Ilmiah Bisnis dan Ekonomi Asia, vol. 11, no. 2, pp. 27–31, 2017.
[5] Y. C. Pratama, “Analisis Faktor-Faktor yang Mempengaruhi Kemiskinan di Indonesia,†Esensi: Jurnal Bisnis dan Manajemen, vol. 4, no. 2, Sep. 2014.
[6] A. N. Ulfah and S. ’Uyun, “Analisis Kinerja Algoritma Fuzzy C-Means dan K -Means pada Data Kemiskinan,†JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 1, no. 2, pp. 139–148, 2015.
[7] A. Mulyani, “Analisis Neural Network Struktur Backpropagation sebagai Metode Peramalan pada Perhitungan Tingkat Kemiskian di Indonesia,†Techno Nusa Mandiri, vol. 13, no. 1, pp. 9–15, 2016.
[8] S. Mamase and R. S. Sinukun, “Prediksi Tingkat Kemiskinan Provinsi Gorontalo dengan Metode GRNN,†in Seminar Nasional Humaniora & Aplikasi Teknologi Informasi 2018, 2018, pp. 29–32.
[9] L. M. Patnaik and K. Rajan, “Target Detection Through Image Processing and Resilient Propagation Algorithms,†Neurocomputing, vol. 35, no. 1–4, pp. 123–135, Nov. 2000.
[10] A. K. Santra, N. Chakraborty, and S. Sen, “Prediction of Heat Transfer Due to Presence of Copper–water Nanofluid Using Resilient-propagation Neural Network,†International Journal of Thermal Sciences, vol. 48, no. 7, pp. 1311–1318, Jul. 2009.
[11] L. M. Saini, “Peak Load Forecasting Using Bayesian Regularization, Resilient and Adaptive Backpropagation Learning Based Artificial Neural Networks,†Electric Power Systems Research, vol. 78, no. 7, pp. 1302–1310, Jul. 2008.
[12] S. Kumar and B. K. Tripathi, “High-Dimensional Information Processing Through Resilient Propagation in Quaternionic Domain,†Journal of Industrial Information Integration, vol. 11, pp. 41–49, Sep. 2018.
[13] R. P. Satya Hermanto, Suharjito, Diana, and A. Nugroho, “Waiting-Time Estimation in Bank Customer Queues using RPROP Neural Networks,†Procedia Computer Science, vol. 135, pp. 35–42, 2018.
[14] R. Y. Fa’rifah and Z. Busrah, “Backpropagation Neural Network untuk Optimasi Akurasi pada Prediksi Financial Distress Perusahaan,†Jurnal INSTEK (Informatika Sains dan Teknologi), vol. 2, no. 2, pp. 101–110, 2017.
[15] W. Watsuntorn, R. Khanongnuch, W. Chulalaksananukul, E. R. Rene, and P. N. L. Lens, “Resilient Performance of An Anoxic Biotrickling Filter for Hydrogen Sulphide Removal From A Biogas Mimic: Steady, Transient State and Neural Network Evaluation,†Journal of Cleaner Production, vol. 249, p. 119351, Mar. 2020.
 
[16] A. Poole and A. Kotsialos, “Second Order Macroscopic Traffic Flow Model Validation Using Automatic Differentiation with Resilient Backpropagation and Particle Swarm Optimisation Algorithms,†Transportation Research Part C: Emerging Technologies, vol. 71, pp. 356–381, Oct. 2016.
[17] M. Shiblee, B. Chandra, and P. K. Kalra, “Learning of Geometric Mean Neuron Model Using Resilient Propagation Algorithm,†Expert Systems with Applications, vol. 37, no. 12, pp. 7449–7455, Dec. 2010.
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Published
2020-09-29
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[1]
B. Poerwanto and F. Fajriani, “Resilient Backpropagation Neural Network on Prediction of Poverty Levels in South Sulawesi”, MATRIK, vol. 20, no. 1, pp. 11–18, Sep. 2020, doi: 10.30812/matrik.v20i1.726.
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