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All Journal Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Journal of Educational Technology and Learning Creativity
Nguyen Anh Tuan
Ministry of Science and Tehcnology
Aerospace Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Mechanical Engineering

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Implementation of Information Technology Algorithms Based on ICT Media to Make Teachers Have 21st Century Skills Yolviansyah, Fauziah; Amin, Nor Fadila; Retutas, Mildin; Tuan, Nguyen Anh; Peiling, Liu
Journal of Educational Technology and Learning Creativity Vol. 1 No. 2 (2023): December
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v1i2.1398

Abstract

Purpose of the study: This study aims to improve technological literacy and 21st century skills of teachers through the implementation of algorithms based on ICT media, relevant to the demands of the Industrial Revolution 5.0 era. Methodology: The research method uses an experimental approach with a pretest-posttest design and data collection through questionnaires and observations. Training was given to 30 high school teachers using algorithmic technology and ICT media. The data analysis used was parametric statistics using the t-test.. Main Findings: The results of the study showed a significant increase in technological literacy and 21st century skills, with an average increase of 26.9%, especially in the ICT Media aspect. Novelty/Originality of this study: The novelty of this research lies in the futuristic algorithmic integration in ICT media for learning, which provides an innovative solution for teacher competency development. This finding is expected to be a guide in designing technology-based training programs for adaptive and innovative education.
Improving Short-Term Electricity Load Forecasting Accuracy Using the Ghost Convolutional Neural Network Model Tuan, Nguyen Anh; Toan, Nguyen Duc
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26562

Abstract

Short-Term Load Forecasting (STLF) is essential for maintaining grid stability and optimizing operational efficiency in modern energy systems. While traditional Convolutional Neural Networks (CNNs) can extract local temporal features, they often struggle with capturing long-term dependencies and demand high computational resources. This study proposes a novel application of the Ghost Convolutional Neural Network (GhostCNN)—initially designed for image processing—to time-series electricity load forecasting. GhostCNN significantly reduces model complexity while preserving forecasting accuracy by generating redundant temporal features through lightweight linear operations. The model is trained and evaluated on a real-world electricity load dataset from Ho Chi Minh City, containing 13,440 hourly observations (~1.5 years). A comprehensive hyperparameter tuning strategy is applied, covering kernel size, Ghost ratio, sequence length, batch size, and learning rate. The model's performance is benchmarked against MLP, CNN, and LSTM architectures. GhostCNN achieves the lowest Mean Absolute Percentage Error (MAPE) of 1.15%, outperforming CNN (1.27%), MLP (1.67%), and LSTM (7.3%). Furthermore, GhostCNN reduces inference time by approximately 40% and decreases parameter count by ~45% compared to standard CNNs, affirming its suitability for real-time smart grid deployment. These results demonstrate that GhostCNN provides a robust, scalable, and efficient solution for accurate short-term electricity load forecasting in dynamic and resource-constrained environments.
Bayesian-Optimized MLP-LSTM-CNN for Multi-Year Day-Ahead Electric Load Forecasting Tuan, Nguyen Anh; Nguyen , Trung Dung
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.14345

Abstract

Accurate long-term electric load forecasting—multi-year, day-ahead peak-load prediction—is critical for planning, operations, and policy. While traditional statistical and shallow machine-learning methods often struggle with nonlinear and multi-scale temporal patterns, deep learning offers promising alternatives. This study conducts a systematic, controlled comparison of three architectures—Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—within a unified Bayesian hyperparameter optimization protocol using daily peak-load data from the New South Wales (NSW) electricity market, 2015–2021, with a 365-day look-back window. Under identical data splits, objective, and search procedures, CNN delivers the best accuracy across all metrics (MAE = 699, MSE = 791,838, RMSE = 890, MAPE = 7.53%), MLP performs slightly worse, and LSTM yields the most significant errors alongside the most extended runtime. These results indicate that, under consistent tuning and a one-year context window, CNN captures local variations more effectively than the recurrent alternative in this setting. The research contribution of this study is a fair, empirical benchmark of widely used deep models (MLP, CNN, LSTM) for multi-year, day-ahead peak-load forecasting under a single Bayesian optimization framework, offering practical guidance for model selection. Reproducibility is facilitated by fixed random seeds and comprehensive configuration/trace logging. Limitations include an intentionally univariate design (no exogenous variables), a focus on learned architectures rather than naïve baselines, and the absence of uncertainty quantification; future work will extend to multivariate inputs (e.g., weather and calendar effects), hybrid CNN–LSTM and Transformer-based models, and broader baseline and robustness evaluations.
Hybrid Stacking of Multilayer Perceptron, Convolutional Neural Network, and Light Gradient Boosting Machine for Short-Term Load Forecasting Nguyen, Trung Dung; Tuan, Nguyen Anh
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14410

Abstract

Short-term load forecasting (STLF) is essential for scheduling, dispatch, and demand-side management. Real-world load series exhibit rapid local fluctuations and calendar or exogenous influences that challenge single-model approaches. This study proposes a hybrid stacking framework combining a Multilayer Perceptron (MLP), a 1-D Convolutional Neural Network (CNN), and a Light Gradient Boosting Machine (LightGBM), integrated through a ridge-regression meta-learner. The CNN extracts local temporal patterns from sliding windows of the load series, and the MLP processes tabular features such as lags, rolling statistics, and calendar/holiday indicators. At the same time, LightGBM captures nonlinear interactions in the same feature space. Base learners are trained using a rolling TimeSeriesSplit to avoid temporal leakage, and their out-of-fold predictions are used as inputs for the meta-learner. Early stopping regularizes the neural models. Experimental backtests on Queensland electricity demand data (89,136 half-hourly samples) demonstrate that the stacked model achieves markedly lower forecasting errors, with MAPE ≈ 0.81%, corresponding to a 24% reduction compared to CNN (MAPE ≈ 1.07%) and a 32% reduction compared to MLP (MAPE ≈ 1.19%). Regarding runtime, LightGBM is the fastest (25s) but least accurate, while the stacked model requires longer computation (2488s) yet delivers the most reliable forecasts. Overall, the proposed framework balances accuracy and robustness, and it is modular, reproducible, and extensible to additional exogenous inputs or base learners.
Co-Authors Amin, Nor Fadila Nguyen , Trung Dung Nguyen, Trung Dung Peiling, Liu Retutas, Mildin Toan, Nguyen Duc Yolviansyah, Fauziah