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All Journal Jurnal Ilmu Pendidikan Harmonia: Journal of Research and Education International Journal of Advances in Intelligent Informatics Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah Journal of Applied Data Sciences Science in Information Technology Letters International Journal of Robotics and Control Systems International journal of education and learning Applied Engineering and Technology Bulletin of Social Informatics Theory and Application
Utama, Agung Bella Putra
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Arts Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Social Sciences

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Exploring LSTM-based Attention Mechanisms with PSO and Grid Search under Different Normalization Techniques for Energy demands Time Series Forecasting Pranolo, Andri; Zhou, Xiaofeng; Mao, Yingchi; Pratolo, Bambang Widi; Wibawa, Aji Prasetya; Utama, Agung Bella Putra; Ba, Abdoul Fatakhou; Muhammad, Abdullahi Uwaisu
Knowledge Engineering and Data Science Vol 7, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i12024p1-12

Abstract

Advanced analytical approaches are required to accurately forecast the energy sector's rising complexity and volume of time series data.  This research aims to forecast the energy demand utilizing sophisticated Long Short-Term Memory (LSTM) configurations with Attention mechanisms (Att), Grid search, and Particle Swarm Optimization (PSO). In addition, the study also examines the influence of Min-Max and Z-Score normalization approaches in the preprocessing stage on the accuracy performances of the baselines and the proposed models. PSO and Grid Search techniques are used to select the best hyperparameters for LSTM models, while the attention mechanism selects the important input for the LSTM. The research compares the performance of baselines (LSTM, Grid-search-LSTM, and PSO-LSTM) and proposes models (Att-LSTM, Att-Grid-search-LSTM, and Att-PSO-LSTM) based on MAPE, RMSE, and R2 metrics into two scenarios normalization: Min-Max, and Z-Score. The results show that all models with Min-Max normalization have better MAPE, RMSE, and R2 than those with Z-Score. The best model performance is shown in Att-PSO-LSTM MAPE 3.1135, RMSE 0.0551, and R2 0.9233, followed by Att-Grid-search-LSTM, Att-LSTM, PSO-LSTM, Grid-search-LSTM, and LSTM. These findings emphasize the effectiveness of attention mechanisms in improving model predictions and the influence of normalization methods on model performance. This study's novel approach provides valuable insights into time series forecasting in energy demands.
Restricted Boltzmann Machine Approach for Diagnosing Respiratory Diseases Haviluddin, -; Nurhalifah, Siti; Trahutomo, Dinnuhoni; Wibawa, Aji Prasetya; Utama, Agung Bella Putra
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3427

Abstract

Respiratory diseases remain a significant global health challenge, particularly in developing countries where high morbidity and mortality rates persist. This study aims to establish a diagnostic approach for respiratory diseases using the Restricted Boltzmann Machine (RBM) method to support early detection and improve clinical decision-making. The research utilizes 180 medical records from patients at I. A Moeis Samarinda Hospital, East Kalimantan, Indonesia, includes 22 symptom variables associated with six respiratory disease types: sinusitis, pharyngitis, bronchitis, pneumonia, tuberculosis, and asthma. The collected data were preprocessed into binary formats to represent symptomatic and asymptomatic conditions, facilitating practical training in the RBM model. Data splitting was conducted with 70:30, 80:20, and 90:10 ratios for training and testing sets. The RBM architecture was optimized to enhance model performance by tuning key parameters, including the number of epochs, learning rate, and hidden neurons. Experimental results demonstrate that the RBM model achieved high diagnostic accuracy, with an accuracy of 98%, sensitivity of 98%, and specificity of 99% under the configuration of 5000 epochs, a learning rate of 0.1, and 53 hidden neurons. These findings indicate the model’s capability to recognize patterns and accurately classify respiratory diseases based on clinical symptoms. The study highlights the potential of integrating AI-based diagnostic systems like RBM into healthcare services, particularly in resource-limited settings. Future research should explore larger, more diverse datasets and consider environmental and socioeconomic factors to improve the model’s generalizability and practical applicability.
Social informatics and CDIO: revolutionizing technological education Wibawa, Aji Prasetya; Nabila, Khurin; Utama, Agung Bella Putra; Purnomo, Purnomo; Dwiyanto, Felix Andika
International Journal of Education and Learning Vol 5, No 2: August 2023
Publisher : Association for Scientific Computing Electrical and Engineering(ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijele.v5i2.1175

Abstract

Social informatics is an interdisciplinary area that examines how information and communication technologies (ICT) and the complex web of social and cultural contexts interact and change over time. This study not only helps with the design and use of ICT but also shows how these technologies significantly affect society and culture. It encourages new ideas, collaborations between different fields, and policymaking insights, which drives technological innovation and a better knowledge of how ICT affects society. The Conceive, Design, Implement, operate (CDIO) educational system stands out as a new and innovative teaching method. It emphasizes active learning and gives engineering students both technical and social skills. Its use in social informatics ushers in a new era of education that combines innovation and technology to help students become strong and independent. Future study on CDIO programs in social informatics education has the potential to augment the technical proficiency and social consciousness of graduates, thereby rendering them significant contributors to the field.
Journal Unique Visitors Forecasting Based on Multivariate Attributes Using CNN Dewandra, Aderyan Reynaldi Fahrezza; Wibawa, Aji Prasetya; Pujianto, Utomo; Utama, Agung Bella Putra; Nafalski, Andrew
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (379.839 KB) | DOI: 10.29099/ijair.v6i1.274

Abstract

Forecasting is needed in various problems, one of which is forecasting electronic journals unique visitors. Although forecasting cannot produce very accurate predictions, using the proper method can reduce forecasting errors. In this research, forecasting is done using the Deep Learning method, which is often used to process two-dimensional data, namely convolutional neural network (CNN). One-dimensional CNN comes with 1D feature extraction suitable for forecasting 1D time-series problems. This study aims to determine the best architecture and increase the number of hidden layers and neurons on CNN forecasting results. In various architectural scenarios, CNN performance was measured using the root mean squared error (RMSE). Based on the study results, the best results were obtained with an RMSE value of 2.314 using an architecture of 2 hidden layers and 64 neurons in Model 1. Meanwhile, the significant effect of increasing the number of hidden layers on the RMSE value was only found in Model 1 using 64 or 256 neurons.
Fixed sherwood duel optimization for time series imputation Utama, Agung Bella Putra; Wibawa, Aji Prasetya; Handayani, Anik Nur; Nafalski, Andrew
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2396

Abstract

Missing values remain a persistent challenge in time-series data, particularly within large-scale monitoring systems where reliable forecasting and evaluation are essential. Incomplete records often arise from irregular reporting, infrastructure limitations, or system failures, leading to biased analyses and inaccurate predictions. Traditional imputation methods, such as mean, median, and mode substitution, provide computational efficiency but oversimplify temporal structures. At the same time, more advanced approaches, including Multiple Imputation by Chained Equations (MICE) and K-Nearest Neighbors (KNN), offer improvements yet remain sensitive to data distribution and model configuration. To address this gap, this study introduces Sherwood Duel Optimization (SDO). This socio-inspired framework reconceptualizes imputation as a deterministic duel-based optimization problem. In its fixed form, SDO generates multiple candidate imputations and selects the most robust replacement value using a composite multi-metric scoring mechanism that integrates forecasting accuracy and explanatory power. The framework was evaluated using multivariate educational time-series data and further validated across heterogeneous SDG-related domains, and compared against classical and advanced baselines across three forecasting models. Experimental results demonstrate that SDO consistently outperforms existing methods, reducing forecasting error (MAPE) by more than 40%, achieving the lowest RMSE, and producing R² values exceeding 0.95. Statistical testing confirms that these improvements are significant across experimental configurations. These findings highlight the potential of SDO as a reliable, interpretable, and computationally efficient optimization-based imputation framework. By strengthening data reliability at the reconstruction stage, SDO enhances the credibility of downstream forecasting and decision-making in institutional and sustainability-oriented monitoring systems.
Evaluating Student Learning Outcomes in Virtual Reality Adaptive Chemistry Machfudin, Mohammad Farid; Setiawan, Esther Irawati; Halim, Kevin Jonathan; Santoso, Joan; Utama, Agung Bella Putra; Gunawan, Gunawan; Kusuma, Samuel Budi Wardana; Singh, Vrijraj; Tuan Vu, Tong Nam
Jurnal Ilmu Pendidikan Vol 31, No 2 (2025): December
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um048v31i2p%p

Abstract

This study evaluates the effectiveness of a Virtual Reality (VR)–based adaptive learning application in enhancing high school students’ understanding of chemical compounds. The primary objective was to quantitatively assess the impact of the VR intervention on student learning outcomes across two distinct cohorts (N = 78). A pretest–posttest control-group design was employed, with two parallel groups (Group A and Group B) to ensure internal validity and comparability of results. The findings consistently indicate a marked contrast between the experimental and control conditions. Students in the control groups showed declines in performance, with negative learning gains of −8.32 and −15.20, suggesting learning loss when conventional instructional methods were used. In contrast, students exposed to the VR-based adaptive learning application demonstrated positive learning gains of +2.90 and +9.70, reflecting meaningful improvements in conceptual understanding. Further analysis of the intervention’s impact revealed effect sizes ranging from medium (Cohen’s d = 0.722) to very large (d = 2.182). These results indicate not only statistical significance but also substantial practical significance. Overall, the findings provide strong empirical evidence that the VR-based adaptive learning application is effective in preventing learning loss and significantly enhancing students’ understanding of chemical compounds when compared to traditional instructional approaches.
Co-Authors Aji Prasetya Wibawa Akbari, Ade Kurnia Ganesh Andri Pranolo Anik Nur Handayani Atmaja, Nimas Hadi Ba, Abdoul Fatakhou Bambang Widi Pratolo Chuttur, Mohammad Yasser Citra, Hana Rachma Dewandra, Aderyan Reynaldi Fahrezza Dwiyanto, Felix Andika Eddy Sutadji Esther Irawati Setiawan Fadhilla, Akhmad Fanny Ghosh , Urvi Gunawan Gunawan Halim, Kevin Jonathan Hariyono Hariyono Haviluddin Haviluddin Haviluddin, - Hery Widijanto Izdihar, Zahra Nabila Joan Santoso Kurniawati, Fitriana Lauretta, Giovanny Cyntia Leonel Hernandez, Leonel Machfudin, Mohammad Farid Mao, Yingchi Muhammad, Abdullahi Uwaisu Nabila, Khurin Nafalski, Andrew Naufal, Ayyub Ningtyas, Yana Noviani, Erina Fika Nurhalifah, Siti Paramarta, Andien Khansa’a Iffat Purnomo Purnomo Putri , Nastiti Susetyo Fanany Putri, Nastiti Susetyo Fanany Ramadhan, Sany Putra Rizqini, Fajriwati Qoyyum Samuel Budi Wardana Kusuma, Samuel Budi Wardana Saputra, Irzan Tri Setyaputri, Faradini Usha Shakti, Maharsa Caraka Shiddiqy, Jabar Ash Singh, Vrijraj Sucahyo, Cornaldo Beliarding Trahutomo, Dinnuhoni Triono, Alfiansyah Putra Pertama Tuan Vu, Tong Nam Uriu, Wako Utomo Pujianto Widiharso , Prasetya Yandratama, Hengky Zaeni, Ilham Ari Elbaith Zhou, Xiaofeng