Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Journal of Applied Data Sciences ADI Pengabdian kepada Masyarakat Jurnal (ADIMAS Jurnal) International Transactions on Artificial Intelligence (ITALIC)
Ignatius Agus Supriyono
Satya Wacana Christian University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Empowering MSMEs Through Student-Led Digital Retail Strategies for Sustainable Development Goals Kursih Sulastriningsih; Ignatius Agus Supriyono; Mardiana Mardiana; Ardivan Avandi; Kgomotso Moyo
ADI Pengabdian Kepada Masyarakat Vol 6 No 2 (2026): ADI Pengabdian Kepada Masyarakat
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/adimas.v6i2.1464

Abstract

Digital transformation in the retail sector is progressing rapidly, yet many micro, small, and medium enterprises (MSMEs) continue to face challenges in utilizing technology effectively. This study presents a student-led community empowerment model aimed at optimizing digital retail strategies while supporting the Sustainable Development Goals (SDGs). The study employed a descriptive qualitative approach complemented by quantitative observation and survey data collected from 100 respondents. Students implemented various digital initiatives, including digital branding, content management, marketplace integration, and digital payment systems in participating MSMEs. The findings indicate significant improvements in market reach, customer interaction, operational efficiency, and customer loyalty. The respondent data further support the positive impact of digital strategy implementation and highlight the role of students as mediators of knowledge transfer between academic theory and business practice. This model demonstrates that student involvement in digital retail initiatives can effectively enhance MSME performance while promoting inclusive economic growth, industrial innovation, and responsible consumption aligned with SDGs. The findings also suggest a scalable framework for sustainable community-based economic empowerment.
Reliable Machine Learning Models for Energy Optimization in Smart Green Cities Ignatius Agus Supriyono; Mochamad Heru Riza Chakim; Henry Zainarthur; Dimas Aditya Prabowo
International Transactions on Artificial Intelligence Vol. 4 No. 2 (2026): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i2.1092

Abstract

Rapid urbanization and increasing energy consumption have intensified the need for intelligent approaches that support sustainable and efficient energy management in smart green cities. This study investigates the effectiveness of machine learning models in improving energy demand forecasting and energy optimization through a reliability-oriented evaluation framework. The research utilizes a real-world smart city energy consumption dataset comprising 17,520 hourly observations collected between January 2022 and December 2023. Three machine learning models, namely Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM), were developed and evaluated using 30 independent execution runs. Model performance was assessed through Mean Absolute Error (MAE), Root Mean Square Error (RMSE), coefficient of determination (R2), reliability analysis, and interpretability consistency measurements. The results demonstrate that LSTM achieved the best predictive performance with an MAE of 0.31, RMSE of 0.45, and R2 of 0.93, outperforming Random Forest and SVM across all evaluation metrics. Furthermore, LSTM exhibited the highest reliability score of 0.912 and superior explanation stability, indicating robust and consistent performance under repeated executions. The forecasting outputs were integrated into an energy optimization framework, resulting in reductions in peak energy loads and overall electricity consumption. These findings confirm that reliable and explainable machine learning models can support adaptive, data-driven energy management strategies capable of enhancing operational efficiency and sustainability in urban environments. The proposed framework contributes to the development of trustworthy intelligent systems for smart green cities and supports the achievement of sustainable development objectives related to clean energy, sustainable communities, and climate action.
A Voltage-Controlled Model Machine Learning Framework for State-of-Charge Estimation in Battery Management System: A Performance Evaluation Ignatius Agus Supriyono; Eko Sediyono; Iwan Setyawan; Ivanna Kristianti Timotius; Untung Rahardja
Journal of Applied Data Sciences Vol 7, No 4: December 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i4.1511

Abstract

Accurate State-of-Charge (SoC) estimation is essential for ensuring the reliability, safety, and efficient operation of Battery Energy Storage Systems (BESS), particularly in photovoltaic (PV) applications characterized by intermittent generation. Conventional SoC estimation methods may have limitations in capturing nonlinear and time-dependent voltage behavior under practical operating conditions. This study proposes a data-driven SoC estimation framework that integrates a Voltage-Controlled Model (VCM) with sliding-window temporal feature extraction and multiple machine learning (ML) algorithms. The sliding-window approach transforms sequential voltage measurements into structured lag-based features to represent battery discharge dynamics more effectively. Real-world voltage data were collected from four 45-Ah lead-acid batteries operating in a PV-BESS system from February to December 2024. More than 140,000 charge-discharge records were acquired, from which 6,226 discharge records were extracted for model development and evaluation. Five ML models, namely Support Vector Regression (SVR), Artificial Neural Network (ANN), Random Forest (RF), Long Short-Term Memory (LSTM), and eXtreme Gradient Boosting (XGBoost), were compared using mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), and computation time. The results show that VCM-LSTM achieves the lowest prediction errors, whereas VCM-XGBoost provides the most favorable balance between predictive accuracy and computational efficiency. VCM-XGBoost achieves MSE = 0.0849, MAE = 0.1401, RMSE = 0.2913, and a computation time of 0.94 seconds. These findings indicate that combining sliding-window temporal representation with VCM and ML can support accurate and computationally efficient SoC estimation, highlighting the potential of VCM-XGBoost for resource-constrained BMS applications, particularly in real-world residential PV-BESS environments with limited computational resources.
Co-Authors Ardivan Avandi Dimas Aditya Prabowo Eko Sediyono Henry Zainarthur Ivanna Kristianti Timotius Iwan Setyawan Kgomotso Moyo Kursih Sulastriningsih Mardiana Mardiana Mochamad Heru Riza Chakim Untung Rahardja