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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 Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Agung Muhamad Toha
Electrical Engineering, Universitas Islam Nusantara
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.
Co-Authors Adli, Muhammad Zimamul Agung Surya Wibowo Asshidiq, Barkah 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