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All Journal International Journal of Electrical and Computer Engineering JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Andalasian International Journal of Applied Science, Engineering, and Technology Abditek Nusantara TEKNOLOGI NUSANTARA Media Elektrik Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Agung Muhamad Toha
Universitas Islam Nusantara, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences

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Tuning feature selection to enhance machine learning predictions of bandgap and efficiency in chalcogenide perovskites Primadianti, Osphanie Mentari; Iman, Ryan Nur; Adli, Muhammad Zimamul; Toha, Agung Muhamad; Wibowo, Agung Surya
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1508-1517

Abstract

Solar cell technology has advanced rapidly in efficiency and material innovation. As a renewable energy source, solar cells help mitigate the global energy crisis. Perovskite-based solar cells have recently achieved efficiencies above 25%, surpassing conventional silicon cells. Among emerging materials, chalcogenide perovskites show great promise due to their superior stability compared to halide perovskites. However, they remain in the exploration stage, making accurate predictions of their electrical properties, especially bandgap, essential for assessing potential in solar cell applications. This study predicts bandgap values using computational methods, emphasizing efficiency and cost reduction compared to experimental approaches. Key features derived from collected data include oxidation state, electronegativity, coordination number, ionic radius, and density. Several machine learning (ML) algorithms: AdaBoost Regressor, gradient boosting regressor, support vector regressor, CatBoost Regressor, and k-neighbor regressor, were implemented using Python. The research process involved data collection, preprocessing (feature scaling, fusion, reduction, and selection), model training and testing with 5-fold cross-validation, and hyperparameter optimization to achieve optimal results. Among the tested models, CatBoost Regressor yielded the best performance, achieving a coefficient of determination (R2) of 69.34%, a mean absolute error (MAE) of 23.1%, and root-mean-square error (RMSE) of 29.49%, demonstrating its effectiveness in predicting chalcogenide perovskite bandgaps.
Multi-Trajectory Performance and Run-to-Run Consistency of GWO-Tuned PID Control for a Differential-Drive Mobile Robot Auliya Nabila; Agung Muhamad Toha
Jurnal Media Elektrik Vol. 23 No. 3 (2026): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i3.13911

Abstract

Accurate trajectory tracking of differential-drive mobile robots is strongly influenced by proportional–integral–derivative (PID) controller parameters, while a single fixed parameter set may exhibit different performance across trajectories with distinct geometric characteristics. This study investigates the multi-trajectory performance and run-to-run consistency of Grey Wolf Optimizer (GWO)-based PID tuning for a differential-drive mobile robot. A kinematic robot model with linear and angular-velocity PID control is evaluated on four reference trajectories: circle, lemniscate, square, and S-curve. A systematically tuned fixed PID controller is employed as the common baseline, while GWO independently optimizes six PID gains for each trajectory using an objective function combining the integral of time-weighted absolute error (ITAE) and control effort. To account for the stochastic nature of GWO, 30 independent optimization runs are performed for each trajectory and performance is assessed using root mean square error (RMSE), ITAE, standard deviation, median, interquartile range, and coefficient of variation. The simulation results show that GWO-PID reduces the mean RMSE from 0.07767 to 0.04979 m for the circle, from 0.08113 to 0.06393 m for the lemniscate, and from 0.09047 to 0.05095 m for the S-curve, corresponding to improvements of 35.90%, 21.20%, and 43.68%, respectively. The square trajectory exhibits an increase in mean RMSE from 0.07728 to 0.08154 m, indicating that optimization does not provide uniform improvement across all trajectory geometries. Run-to-run analysis further reveals substantial differences in optimization consistency with RMSE coefficients of variation of 54.17% for the circle, 85.33% for the lemniscate, and 12.03% for the square, and 0.75% for S-curve. These findings indicate that the effectiveness and repeatability of GWO-based PID tuning are trajectory-dependent, highlighting the importance of multi-run statistical evaluation when assessing metaheuristic controller tuning for mobile robot trajectory tracking.
Co-Authors Adli, Muhammad Zimamul Agung Surya Wibowo Asshidiq, Barkah Auliya Nabila Bahtiar, Deri Deri Bahtiar, Deri Bahtiar Elsa Halvianisa Fairuz Antoni Putri Ganis Sanhaji, Ganis Ganis Sanhaji, Ganis Sanhaji Iman, Ryan Nur Iwan Satriyo Nugroho Moh. Sahal Bahari Mokhamad Ridho Hanafi Muhammad Sobari Muhammad Zimamul Adli Norma Rohman Nurul Rahmi Khotimah osphanie mentari Primadianti, Osphanie Mentari Putri, Fairuz Antoni Ramdan Robi Al Hamimi Rindi Wulandari Rofin Afril Irawan Sovia Woza Wulandari, Rindi Yussa Saripuddin