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All Journal IJISTECH (International Journal Of Information System & Technology) Journal of Computer System and Informatics (JoSYC) IJISTECH
Robiansyah, Wendi
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

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The Conjugate Gradient Backpropagation Algorithm in Predicting Inmate Rates in Pematangsiantar City Based on Gender Darma, Surya; Robiansyah, Wendi; Firzada, Fahmi; Irawan, Eka; Saputra, Widodo
IJISTECH (International Journal of Information System and Technology) Vol 8, No 2 (2024): The August edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i2.353

Abstract

The increase in the number of inmates in Indonesia, particularly in Pematangsiantar City, is a significant social issue. In this context, it is important to predict inmate levels based on demographic factors, including gender. One promising approach is the use of Artificial Neural Networks (ANN) with the Backpropagation Conjugate Gradient (BPCG) algorithm. ANN is a computational model that mimics the way the human brain processes information and has been used in various applications, including crime prediction. The BPCG algorithm is a variant of the backpropagation algorithm that efficiently accelerates the convergence of ANN training. This study aims to implement ANN with the BPCG algorithm to predict inmate levels in Pematangsiantar City based on gender and to evaluate the performance of this model in the context of available crime data. MATLAB (version 7.13 R2011b) was used as a tool, employing five model architectures (7-3-1, 7-5-1, 7-11-1, 7-12-1, and 7-15-1) to test data for estimation/prediction. The best model, 7-12-1, achieved 100% accuracy with 16 iterations in less than 1 second and an MSE of 0.1477446359. With 100% accuracy, this model will be used to predict the number of inmates in Pematangsiantar City by gender in 2023. This study can make a significant contribution to the fields of criminology and data analysis and serve as a reference for future research on the use of artificial intelligence in legal and criminal contexts. 
The Conjugate Gradient Backpropagation Algorithm in Predicting Inmate Rates in Pematangsiantar City Based on Gender Darma, Surya; Robiansyah, Wendi; Firzada, Fahmi; Irawan, Eka; Saputra, Widodo
IJISTECH (International Journal of Information System and Technology) Vol 8, No 2 (2024): The August edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i2.353

Abstract

The increase in the number of inmates in Indonesia, particularly in Pematangsiantar City, is a significant social issue. In this context, it is important to predict inmate levels based on demographic factors, including gender. One promising approach is the use of Artificial Neural Networks (ANN) with the Backpropagation Conjugate Gradient (BPCG) algorithm. ANN is a computational model that mimics the way the human brain processes information and has been used in various applications, including crime prediction. The BPCG algorithm is a variant of the backpropagation algorithm that efficiently accelerates the convergence of ANN training. This study aims to implement ANN with the BPCG algorithm to predict inmate levels in Pematangsiantar City based on gender and to evaluate the performance of this model in the context of available crime data. MATLAB (version 7.13 R2011b) was used as a tool, employing five model architectures (7-3-1, 7-5-1, 7-11-1, 7-12-1, and 7-15-1) to test data for estimation/prediction. The best model, 7-12-1, achieved 100% accuracy with 16 iterations in less than 1 second and an MSE of 0.1477446359. With 100% accuracy, this model will be used to predict the number of inmates in Pematangsiantar City by gender in 2023. This study can make a significant contribution to the fields of criminology and data analysis and serve as a reference for future research on the use of artificial intelligence in legal and criminal contexts. 
Analisis Algoritma JST untuk Prediksi Perkembangan PDRB Menurut Lapangan Usaha Atas Dasar Harga Berlaku Robiansyah, Wendi; Okprana, Harly
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.5994

Abstract

Gross Regional Domestic Product (GRDP) data plays a vital role as a reference in regional development planning. However, the main challenge faced is the inaccuracy of GRDP growth predictions due to complex and fluctuating economic dynamics, especially in areas such as Simalungun Regency. Therefore, this study aims to analyze the development of Gross Regional Domestic Product (GRDP) by business field based on current prices in Simalungun Regency using three Artificial Neural Network (ANN) algorithms, namely Backpropagation, Bayesian Regulation, and Levenberg-Marquardt. The research data is GRDP times-series data for 2015-2023 obtained from the Central Statistics Agency of Simalungun Regency. The analysis used five models of the same architecture, namely 7-5-1, 7-10-1, and 7-15-1, with a target error of 0.01 and a maximum epoch of 1000 iterations. The results of the study indicate that the Levenberg-Marquardt algorithm with the 7-10-1 architecture model provides the best performance with an accuracy rate of 100% and the smallest Mean Squared Error (MSE) value of 0.0000214320 compared to other algorithms and architecture models. This finding indicates that the Levenberg-Marquardt algorithm is superior in predicting the development of GRDP in Simalungun Regency. The implementation of the results of this study is expected to help local governments and related agencies provide information on the development of GRDP in Simalungun Regency so that they can design more accurate and effective economic policies. In addition, this study also contributes to the development of artificial intelligence-based economic prediction methods, especially in the application of JST for the analysis of complex and dynamic regional economic data.
Co-Authors Eka Irawan Firzada, Fahmi Okprana, Harly Surya Darma Widodo Saputra