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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.
Monitoring System for Sugar Storage using DHT22, Ultrasonic, and Light Sensors Izzurohman, Moh.; Mulyani, Sri Hasta; Ordiyasa, I Wayan
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/c3d6kr84

Abstract

This study develops an Internet of Things (IoT)-based monitoring system designed to maintain stable environmental conditions in palm sugar storage warehouses. The system integrates a NodeMCU ESP8266 microcontroller, a DHT22 temperature and humidity sensor, an OLED display, and a relay-controlled exhaust fan to monitor and regulate environmental parameters. Experimental evaluation was conducted using 30 measurement samples collected at 15-minute intervals in a simulated warehouse environment. The accuracy of the DHT22 sensor was assessed by comparing its readings with calibrated digital instruments. The results show that the average temperature measurement error was 0.3923°C, while the humidity error reached approximately 2.1%. The monitoring system successfully displayed real-time environmental conditions and automatically activated the exhaust fan when the temperature exceeded 30°C or the humidity surpassed 67.89%. Telegram notifications were delivered with an average latency of approximately 1–2 seconds after threshold detection, demonstrating near real-time system responsiveness. Overall, the proposed IoT-based monitoring system demonstrates reliable performance in monitoring and managing environmental conditions in palm sugar storage facilities. The integration of automated control, remote notification, and web-based data visualization provides a practical and cost-effective solution for warehouse monitoring.