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All Journal International Journal of Engineering Continuity
Miftahul Fikri
Department of Electrical Engineering, Institut Teknologi Perusahaan Listrik Negara, Jakarta, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Comparative Study of PSO, GA, and ACO for Optimizing Dielectric Performance in Fly Ash Filled Silicone Rubber Andi Amar Thahara; Christiono Christiono; Miftahul Fikri; Iwa Garniwa M. K.; Mohammad Wirandi
International Journal of Engineering Continuity Vol. 4 No. 2 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i2.439

Abstract

This study investigates the optimization of coal fly ash composition as a filler in Silicone Rubber (SiR) insulator materials, aiming to enhance their dielectric characteristics. Compositional optimization was achieved by evaluating and comparing three advanced meta-heuristic algorithms Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO), using the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as performance metrics. The utilized fly ash, containing dominant silica, alumina, and iron oxides, was directly incorporated into the SiR matrix. Results indicate that, compared to PSO, GA and ACO exhibited superior performance and consistency. Specifically, for Relative Permittivity, the optimal composition of 80% yielded the lowest errors with GA and ACO (RMSE = 0.0751; MAPE = 0.9044). For Hydrophobicity, these two algorithms showed superior accuracy in the RMSE metric (RMSE = 0.8883) at 15.39% loading. These findings underscore the scientific contribution of this study by establishing the superior reliability of GA and ACO for optimizing fly ash composition in SiR, thus providing a robust analytical methodology to advance the use of industrial waste for high-performance dielectric materials.
Integrating ISO 50001 and PDCA Cycle for Continuous Energy Performance Improvement in Higher Education Buildings Dwi Listiawati; Christiono Christiono; Ishvandono Yunaini A; Miftahul Fikri; Andi Amar Thahara
International Journal of Engineering Continuity Vol. 5 No. 1 (2026): IJEC
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v5i1.472

Abstract

This study proposes a systematic framework for energy performance improvement in institutional facilities by integrating technical auditing with the ISO 50001:2018 standard. Utilizing the Plan-Do-Check-Act (PDCA) cycle, a comprehensive energy baseline for the ITPLN Building was established based on 2024 data, revealing an annual consumption of 1,405,600.80 kWh. In the Check phase, the calculated Energy Consumption Intensity (IKE) of 104.78 kWh/m²/year classified the building as Efficient under ESDM Regulation No. 3/2025. Quantitative analysis identified HVAC (57%) and Lighting (18%) as primary drivers, necessitated by an average ambient temperature of 30°C. To address inefficiencies, the Act phase formulated strategic Energy Saving Opportunities (ESO) such as LED retrofitting and AC standardization. These interventions are projected to reduce consumption by 42,168.02 kWh/year, lowering the IKE to 101.6 kWh/m²/year—a 3% efficiency gain. The study concludes that integrating ISO 50001 with physical audit data provides a replicable and economically measurable strategy for optimizing energy performance, with systematic maintenance recommended to ensure long-term operational sustainability.
Predictive Modeling of Nonlinear Breakdown Voltage in Silicone Rubber Polymer Insulators with Fly Ash Filler Using the GLME and BPNN Methods Davina Salmah An’nafri; Christiono Christiono; Miftahul Fikri; Nurmiati Pasra; Andi Dyah Harum
International Journal of Engineering Continuity Vol. 5 No. 2 (2026): IJEC
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v5i2.607

Abstract

The expansion of Indonesia's transmission and distribution network increases the demand for high-voltage insulator materials with reliable dielectric performance and sustainable manufacturing. Silicone rubber (SiR) filled with coal fly ash is a promising alternative; however, its breakdown voltage varies nonlinearly with filler composition, temperature, and fly ash source. This study proposes a complementary statistical machine learning framework using Generalized Linear Mixed Effects (GLME) for interpretable statistical analysis and a Backpropagation Neural Network (BPNN) for flexible nonlinear prediction. The analysis used 512 secondary observations from four Indonesian fly ash sources, with filler compositions of 10–80% and temperatures of 35–50°C. Breakdown voltage increased with fly ash content up to an optimum of approximately 60–65% before declining at higher loadings, while increasing temperature consistently reduced dielectric strength. GLME achieved R² = 0.7457, RMSE = 0.0354, and MAPE = 1.57%, whereas the five-fold cross-validated BPNN showed slightly better average predictive performance, with R² = 0.7617, RMSE = 0.0343, and MAPE = 1.56%. GLME provides interpretable and statistically testable coefficients, whereas BPNN captures additional complex nonlinear patterns without requiring a predefined functional form. SEM and XRF characterization supported the observed nonlinear trends through particle dispersion and fly ash oxide composition, predominantly SiO₂ and Al₂O₃. The proposed complementary framework combines statistical interpretability with nonlinear predictive capability, supporting fly ash composition selection, efficient material screening, and breakdown voltage prediction for high voltage silicone rubber insulator applications.
Co-Authors Andi Amar Thahara Andi Dyah Harum Christiono Christiono Christiono Christiono Davina Salmah An’nafri Dwi Listiawati Ishvandono Yunaini A Iwa Garniwa M. K. Mohammad Wirandi Nurmiati Pasra