Integrasi Sistem IoT untuk Pemantauan Cuaca Real-Time dan Prediksi Curah Hujan berbasis LSTM

Dimas Yudistira; Muhammad Ikhsan

doi:10.29408/edumatic.v9i3.32425

Authors

Dimas Yudistira Program Studi Ilmu Komputer, Universitas Islam Negeri Sumatera Utara https://orcid.org/0009-0007-4142-7587
Muhammad Ikhsan Program Studi Ilmu Komputer, Universitas Islam Negeri Sumatera Utara https://orcid.org/0000-0001-5313-0923

DOI:

https://doi.org/10.29408/edumatic.v9i3.32425

Keywords:

esp32, iot, lstm, weather monitoring, rainfall prediction

Abstract

The inaccuracy of non-real-time macro-regional weather forecasts creates an information gap for MSME actors in the agricultural sector who need updates on actual weather conditions at their business locations. This study aims to develop a real-time weather monitoring system based on ESP32 and an LSTM rainfall prediction model, the results of which can be accessed via a web interface by MSME actors such as farmers. The system was developed using an ESP32 microcontroller, DHT22 sensor, and Hall-effect 3144, with an LSTM model trained using historical BMKG data and field data. Our findings show that the system is capable of monitoring weather conditions in real time while generating microspatial rainfall predictions through full integration between IoT devices and the LSTM model. Test results show stable system performance with a transmission delay of 5 seconds, an initial fetch latency of 48 ms, and prediction performance RMSE of 17.05 mm, MAE of 12.40 mm, and F1-score of 0.175, which is adequate for detecting extreme rainfall intensity. These findings demonstrate the potential for developing an affordable, easily replicable, and adaptable microspatial weather forecasting system to support early warning systems or smart irrigation in agricultural areas.

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