Article Per Year (5 Year)
p-Index From 2021 - 2026
1.671
P-Index
This Author published in this journals
All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Buana Informatika SemanTIK : Teknik Informasi IT JOURNAL RESEARCH AND DEVELOPMENT Techne : Jurnal Ilmiah Elektroteknika Jurnal Sistem Cerdas PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Applied Technology and Computing Science Journal Letters in Information Technology Education (LITE) International Journal of Informatics Engineering and Computing
Ordiyasa, I Wayan
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
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 Dian Rhesa Rahmayanti Elisabeth Deta Lustiyati Evrita Lusiana Utari, Evrita Lusiana Fathinah, Azzah HAFIZAH, IDA Hamzah Hamzah Hamzah Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Meliala, Dyan Avando Mohammad Diqi Noverianus, Noverianus Putra Wanda, Putra Sahal, Ahmad Salsabela, Marcella Sri Hasta Mulyani Sriwidodo Sriwidodo, Sriwidodo Sugeng Winardi Sugiarto, Raden Bagus Nurhadi Wijaya Syihab, Namira Anjani Rahmadina Wandani, Aulia Fadillah Wani Wijaya, Nurhadi Wiratma, Harits Dwi