Article Per Year (5 Year)
p-Index From 2021 - 2026
3.846
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Jurnal Edukasi Elektro JTEV (Jurnal Teknik Elektro dan Vokasional ELKHA : Jurnal Teknik Elektro Jurnal Momentum SITEKIN: Jurnal Sains, Teknologi dan Industri JURNAL NASIONAL TEKNIK ELEKTRO PROtek : Jurnal Ilmiah Teknik Elektro Journal of Energy, Mechanical, Material and Manufacturing Engineering Seminar Nasional Teknologi Informasi Komunikasi dan Industri Rang Teknik Journal Multitek Indonesia : Jurnal Ilmiah JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI JOMLAI: Journal of Machine Learning and Artificial Intelligence Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics SNTE Jurnal Teknik Elektro
Zulfatri Aini
Universitas Islam Negeri Sultan Syarif Kasim Riau
Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

Published : 32 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 32 Documents
Search

 1   2   3   4 
Performance Evaluation of NARX-CG Model for Electricity Forecasting: Bali Blackout Case Study Alaqsa, Tengku Reza Suka; Aini, Zulfatri
Jurnal Teknik Elektro Vol. 17 No. 2 (2025)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v17i2.35519

Abstract

Bali experienced a widespread blackout in May 2025 that disrupted economic and social activities across the island, revealing weaknesses in electricity demand forecasting and system resilience. This study evaluates the performance of a Hybrid Nonlinear Autoregressive with Exogenous Inputs-Conjugate Gradient (NARX-CG) model as an advanced electricity forecast. The dataset covers the 2018-2023 period and includes six variables: electricity energy, connected capacity, number of customers, tariffs, Gross Regional Domestic Product (GRDP), and population, aligned with the national electricity planning framework. The NARX-CG model was developed using a 6-12-6-1 network architecture and trained with tansig transfer function. Forecasting performance was evaluated using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) metrics. Results show that the NARX-CG model achieved an MSE of 0.09853 and an average MAPE of 8.12%, outperforming conventional projections with a MAPE of 28.48%. Yearly evaluations show consistent model stability, with the lowest MAPE values of 1.93% and 5.86% in 2023 and 2022, respectively. The NARX-CG model effectively captures nonlinear temporal dependencies, enhances predictive accuracy, and contributes to improved power system reliability and resilience, providing valuable insights for adaptive energy planning following the 2025 Bali blackout.
Entropy-Regularized Nonlinear Auto-Regressive Network with eXogenous Inputs (ER-NARX): A Mathematical Framework for Scalable and Robust Big Data Forecasting Using ITL and Fractional Dynamics Zulfatri Aini; Tengku Reza Suka Alaqsa
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 4 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i4.6689

Abstract

This study proposes the Entropy-Regularized NARX (ER-NARX) model, which integrates nonlinear autoregressive modeling, entropy-based regularization, and information-theoretic learning for big data forecasting. The NARX model captures temporal dependencies between past outputs and exogenous inputs, while entropy regularization is incorporated to control the uncertainty of model predictions and prevent overfitting. The innovation of this model is its ability to control information flow through entropy regularization, which helps balance predictive accuracy with uncertainty, preventing the model from becoming overly deterministic. By combining these components, the ER-NARX model enhances the stability and robustness of the forecasts and improves its generalization to complex, high-dimensional data. Additionally, fractional dynamics are employed to model long-range memory effects in temporal data to enhancing the model's ability to handle datasets with extended dependencies. The resulting ER-NARX framework provides a mathematically grounded approach to big data forecasting improved performance in a computationally efficient manner. Future research may explore advanced entropy regularization techniques and apply the model to more diverse real-world data with intricate dependencies.
Co-Authors Achiruddin Lubis Ahmad Faizal Ahmad Fudholi Alaqsa, Tengku Reza Suka Andika, Rizki Aprialdi, Rahmat Asmi, M. Fariz Dwi Candra, Oriza David Setiawan Dian Mursyitah Diar, Abdurahman Dila Marta Putri Efryansah, Randra Agustio Eng, Ewe Win Esa Mutari Guido, Muhammad Guido Randa Febiant Hanif Naufal Qasthari Harda Arya, Engla Liliana Liliana Liliana liliana liliana Lubis, Achiruddin Lubis, Arul Anwar Maharmi, Benriwati Nanda Putri Miefthawati Nanda Putri Miefthawati Putra, Nanda Okta Raditya Saputra Ramadhan, Muhammad Fiqri Rika Susanti Riswan Efendi Sepannur Bandri Sri Basriati Sudarmadi, Ardiansyah Suka Alaqsa, Tengku Reza Sumarta, Prima Sutoyo Sutoyo Tengku Reza Suka Alaqsa Tengku, Tengku Reza Suka Alaqsa Ullah, Aulia Zainal Ahnur