Design and Development of an IoT-Based Rain Intensity Prediction System Using LoRa - M. Arif; Mohammad Idhom; Henni Endah Wahanani (Komunitas Dosen Indonesia, 2026)
An Internet of Things (IoT)–based system for rain intensity monitoring and next-day prediction is presented by integrating low-power wide-area communication using LoRa with cloud-based processing for outdoor and rural environments. This study evaluates the feasibility of LoRa communication and the end-to-end operational reliability of an IoT–cloud pipeline, while positioning machine learning as a supporting decision-aid module. A low-cost sensing node equipped with temperature, humidity, and wind-speed sensors is connected to a LoRa-based gateway that forwards measurements to an Amazon EC2 cloud server via MQTT for centralized storage, processing, and notification delivery. The system is evaluated through a 10-day single-node real-world outdoor deployment, focusing on sensor data acquisition reliability, LoRa link quality, and end-to-end operation from data acquisition to user notifications. The classification module achieves an overall accuracy of 0.74 with a weighted F1-score of 0.71, while minority-class performance remains limited due to class imbalance. LoRa communication remains stable with RSSI values of −80.91 to −79.19 dBm, SNR values of 9.86–9.95 dB, and packet loss rates below 3%. By jointly evaluating LPWAN communication reliability and cloud-side predictive services within a single field deployment, the results demonstrate the practicality of LPWAN-based IoT sensing with cloud integration for rain intensity monitoring in resource-constrained environments, while highlighting the need for future improvements in minority-class prediction and multi-node scalability.