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All Journal Jurnal Buana Informatika Telematika : Jurnal Informatika dan Teknologi Informasi Wikrama Parahita : Jurnal Pengabdian Masyarakat Indonesian Journal of Artificial Intelligence and Data Mining Applied Technology and Computing Science Journal International Journal of Informatics and Computation Prosiding Seminar Nasional Multidisiplin Ilmu Respati Jurnal Computer Science and Information Technology (CoSciTech) Journal of Computer, Electronic, and Telecommunication (COMPLETE) Jurnal Ilmu Komputer dan Teknologi (IKOMTI)
Wijaya, Nurhadi
Universitas Respati Yogyakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences Other

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Journal : Jurnal Buana Informatika

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Machine Learning for Environmental Health: Optimizing ConcaveLSTM for Air Quality Prediction Diqi, Mohammad; Hamzah; Ordiyasa, I Wayan; Wijaya, Nurhadi; Martin, Benedicto Reynaka Filio
Jurnal Buana Informatika Vol. 15 No. 01 (2024): Jurnal Buana Informatika, Volume 15, Nomor 01, April 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i1.8707

Abstract

This study investigates the optimization of the ConcaveLSTM model for air quality prediction, focusing on the interplay between input sequence lengths and the number of LSTM units to enhance forecasting accuracy. Through the evaluation of various model configurations against performance metrics such as RMSE, MAE, MAPE, and R-squared, an optimal setup featuring 50 input steps and 300 neurons was identified, demonstrating superior predictive capabilities. The findings underscore the critical role of model parameter tuning in capturing temporal dependencies within environmental data. Despite limitations related to dataset representativeness and environmental variability, the research provides a solid foundation for future advancements in predictive environmental modeling. Recommendations include expanding dataset diversity, exploring hybrid models, and implementing real-time data integration to improve model generalizability and applicability in real-world scenarios.
Co-Authors Agus Qomaruddin Munir Ahmad Wakhid Anang Faktchur Rachman Evrita Lusiana Utari, Evrita Lusiana Hamzah Hamzah Hamzah I Wayan Ordiyasa Ikhwan Mustiadi Imam Mahfudz I'tisyam Irawadi Buyung, Irawadi Joko Aryanto kasmawaru kasmawaru Listiawan, Indra Listyalina, Latifah Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Mohammad Diqi Muliani, Sri Hasta Nurain, Maisarah Ordiyasa, I Wayan Putra Wanda, Putra Rike Pradila Sri Hasta Mulyani Trinidya, Fike