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%.
Downloads
Download data is not yet available.
References
[1] Badan Pusat Statistik, Profil Kemiskinan di Indonesia Maret 2019. Jakarta: Badan Pusat Statistik, 2019.
[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.
[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.
Downloads
Published
2020-09-29
Issue
Section
Articles
How to Cite
[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.
Similar Articles
- Bambang Suprihatin, Yuli Andriani, Fauziah Nuraini Kurdi, Anita Desiani, Ibra Giovani Dwi Putra, Muhammad Akmal Shidqi, Lungs X-Ray Image Segmentation and Classification of Lung Disease using Convolutional Neural Network Architectures , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 1 (2023)
- Fatur Rahman Harahap, Anggun Fitrian Isnawati, Khoirun Ni'amah, Variation of Distributed Power Control Algorithm in Co-Tier Femtocell Network , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 1 (2024)
- Wahyu Styo Pratama, Didik Dwi Prasetya, Triyanna Widyaningtyas, Muhammad Zaki Wiryawan, Lalu Ganda Rady Putra, Tsukasa Hirashima, Performance Evaluation of Artificial Intelligence Models for Classification in Concept Map Quality Assessment , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 3 (2025)
- Ahmad Ashril Rizal, Siti Soraya, Multi Time Steps Prediction dengan Recurrent Neural Network Long Short Term Memory , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 18 No. 1 (2018)
- Fitra Ahya Mubarok, Mohammad Reza Faisal, Dwi Kartini, Dodon Turianto Nugrahadi, Triando Hamonangan Saragih, Gender Classification of Twitter Users Using Convolutional Neural Network , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 1 (2023)
- Bambang Krismono Triwijoyo, Ahmat Adil, Anthony Anggrawan, Convolutional Neural Network With Batch Normalization for Classification of Emotional Expressions Based on Facial Images , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 1 (2021)
- Taufik Hidayat, Mohammad Ridwan, Muhamad Fajrul Iqbal, Sukisno Sukisno, Robby Rizky, William Eric Manongga, Determining Toddler's Nutritional Status with Machine Learning Classification Analysis Approach , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 2 (2025)
- Miftahuddin Fahmi, Anton Yudhana, Sunardi Sunardi, Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 3 (2023)
- Muhammad Amirul Mukminin, Tio Dharmawan, Muhamad Arief Hidayat, Gender Classification Using Viola Jones, Orthogonal Difference Local Binary Pattern and Principal Component Analysis , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 3 (2024)
- Aini Suri Talita, Aristiawan Wiguna, Implementasi Algoritma Long Short-Term Memory (LSTM) Untuk Mendeteksi Ujaran Kebencian (Hate Speech) Pada Kasus Pilpres 2019 , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 19 No. 1 (2019)
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- Suwardi Annas, Bobby Poerwanto, Sapriani Sapriani, Muhammad Fahmuddin S, Implementation of K-Means Clustering on Poverty Indicators in Indonesia , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 2 (2022)
- Bobby Poerwanto, Baso Ali, Implementasi Algoritma Fuzzy C-Means dalam Mengelompokkan Kecamatan di Tana Luwu Berdasarkan Produktifitas Hasil Perkebunan , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 19 No. 1 (2019)
.png)
