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All Journal Jurnal Pengabdian UNDIKMA Journal of Electrical Engineering and Computer (JEECOM)
Muhammad Akbar Barrinaya
Universitas Pertamina
Religion Agriculture, Biological Sciences & Forestry Humanities Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Empowering the Community through Household Waste Source Separation Education in Barengkok Village: A Pretest–Posttest Evaluation Adhitya Ryan Ramadhani; Purwo Kadarno; Sylvia Ayu Pradanawati; Byan Wahyu Riyandwita; Muhammad Akbar Barrinaya; Waskito Pranowo; Herminarto Nugroho
Jurnal Pengabdian UNDIKMA Vol. 7 No. 2 (2026): May
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v7i2.17517

Abstract

This community service program aims to enhance the knowledge and skills of residents in Barengkok Village regarding household waste segregation at the source, particularly the separation of organic and inorganic waste, as well as basic on-site processing of organic waste at the household level. The implementation method employed a participatory educational approach through training sessions involving adult residents as the primary participants. The program integrated participatory instruction, hands-on demonstrations, including the establishment of a two-bin waste separation system and basic composting techniques, and pre- and post-assessments consisting of seven waste literacy items. Descriptive statistics indicated consistent post-session improvement across all items, with the most substantial gains observed in waste categorization and organic waste management skills. Participant surveys revealed very high perceived relevance and satisfaction, suggesting that the program was well received by the community. This initiative provides a practical and cost-effective model that combines clear procedures with simple supporting tools, including designated two-bin kits and a laminated kitchen guide, enabling households to operate more independently despite downstream collection limitations. The findings also suggest the need for a brief 4–6-week follow-up period, including bin placement monitoring, contamination assessment, and initial compost trials, as well as a strategy for village-level institutionalization through community advocates and a regular buy-back schedule for clean recyclables.
Physics-Informed Deep Sequential Learning and Non-Parametric Adaptive Thresholding for Integrated Microgrid Fault Detection Muhammad Akbar Barrinaya; Herminarto Akbar Nugroho
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.17123

Abstract

The rapid proliferation of clean energy microgrids—integrating solar photovoltaic (PV) generation, bidirectional power converters, and battery energy storage systems (BESS)—requires robust fault detection and isolation (FDI) to guarantee continuous operational stability. However, non-Gaussian residual profiles caused by severe irradiance intermittency and dynamic switching transients compromise traditional fixed-threshold strategies, resulting in elevated false alarm and missed detection rates. To address this challenge, this study presents a hybrid FDI framework combining physics-guided deep sequential forecasting with non-parametric adaptive thresholding. A Temporal Convolutional Network integrated with Long Short-Term Memory (TCN-LSTM), constrained by equivalent circuit and DC bus power balance equations, is developed to forecast multi-modal nominal trajectories and extract reliable diagnostic residuals. Raw electrical streams sampled at 10 kHz are down sampled via moving-window averaging to 10-second intervals to accommodate prognostic forecasting horizons. Non-parametric Kernel Density Estimation (KDE) is subsequently implemented to dynamically compute adaptive threshold boundaries from empirical non-Gaussian residual distributions. Simulation experiments under stochastic irradiance profiles and dynamic load cycles confirm that the proposed TCN-LSTM architecture achieves nominal forecasting RMSE values of 0.007 V for cell voltage and 0.306 °C for temperature, reducing the missed detection rate of PV shading mismatches and critical sensor biases compared to static thresholding. Furthermore, integration with a Local Outlier Factor (LOF) anomaly detector provides early warning margins of 83.5 minutes for micro-short circuit voltage dips and 24.6 minutes for thermal runaway precursors prior to hard-limit BMS alarms.
Co-Authors Adhitya Ryan Ramadhani Byan Wahyu Riyandwita Herminarto Akbar Nugroho Herminarto Nugroho Purwo Kadarno Sylvia Ayu Pradanawati Waskito Pranowo