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All Journal RESWARA: Jurnal Riset Ilmu Teknik Journal of Community Action
Putra, Rizky Mahendra
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Load Forecasting Analysis for Electrical Distribution Systems Using Time Series Methods Putra, Rizky Mahendra; Damayanti, Rosita Mei; Lopez, Juan Camilo
RESWARA: Jurnal Riset Ilmu Teknik Vol. 3 No. 3 (2025): RESWARA: Jurnal Riset Ilmu Teknik, July 2025
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/reswara.v3i3.405

Abstract

Accurate load forecasting plays a critical role in ensuring the reliability and efficiency of electrical distribution systems. Increasing load variability, the integration of renewable energy, and changes in consumption behavior have intensified forecasting complexity. This study analyzes the effectiveness of time series methods for load forecasting in electrical distribution systems through a structured literature-based analytical approach. The study reviews and synthesizes empirical findings from peer-reviewed journal articles, conference proceedings, and patents published between 2002 and 2025. The methods analyzed include classical statistical models such as ARIMA, SARIMA, ARIMAX, and exponential smoothing, as well as hybrid and advanced approaches including LSTM, Prophet, fuzzy time series, wavelet-based models, and probabilistic forecasting frameworks. The results indicate that classical time series models remain effective for short-term forecasting with stable patterns, while hybrid and machine learning-based time series models provide superior performance under high volatility and complex load dynamics. Studies consistently report improvements in forecasting accuracy, measured using RMSE, MAE, and MAPE, when external variables and hierarchical structures are incorporated. The findings highlight the continued relevance of time series analysis as a foundational approach for load forecasting, while emphasizing the need for adaptive and hybrid models to address modern distribution system challenges. This study contributes a systematic synthesis that supports methodological selection for researchers and practitioners in electrical load forecasting.
Enhancing Digital Literacy in Local Communities Through Technology-Based Training Putra, Rizky Mahendra; Osei, Daniel K.
Journal of Community Action Vol. 2 No. 1: Journal Of Community Action, January 2026
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/joca.v2i1.153

Abstract

Digital literacy has become a critical requirement for social inclusion, economic participation, and access to public services. Local communities, particularly in rural and semi-urban areas, continue to experience significant digital skill gaps. This study aims to analyze the effectiveness of technology-based training programs in enhancing digital literacy in local communities. The research employed a systematic literature-based empirical analysis of community service and intervention studies published between 2014 and 2025. Data were drawn from national and international peer-reviewed journals focusing on digital literacy training, community empowerment, and technology adoption. The findings indicate that structured training programs significantly improve digital competencies, including basic computer skills, internet usage, digital security awareness, and e-commerce engagement. Programs that integrate participatory learning, cultural relevance, and hands-on practice demonstrate higher effectiveness and sustainability. The study also identifies key success factors, such as local facilitator involvement, contextualized content, and continuous mentoring. These findings contribute to the development of an evidence-based framework for designing effective digital literacy interventions at the community level. The study concludes that technology-based training is a practical and scalable approach to reducing the digital divide and strengthening community resilience in the digital era.
Co-Authors Damayanti, Rosita Mei Lopez, Juan Camilo Osei, Daniel K.