Sistem Monitoring dan Kontrol Kadar Air pada Tempat Penyimpanan Tembakau Menggunakan Wemos D1 Berbasis Smartphone - Fatahilla, Aqilla Bayu; Abidin, Rahmad Zainul (Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh, 2025)
Moisture content is a critical factor that determines the quality of tobacco. Inappropriate moisture levels, whether too high or too low, can degrade quality and trigger damage during storage. Previous studies have mostly focused on soil moisture monitoring or other agricultural products, while research on integrated monitoring and automatic control systems for tobacco moisture content in small-scale storage is still limited. This study aims to design an Internet of Things (IoT)-based system for monitoring and controlling tobacco moisture content using a Wemos D1 microcontroller, soil moisture sensor, Firebase, and an Android application developed with MIT App Inventor. The system monitors moisture levels in real time, sends automatic notifications to a smartphone, and controls a storage roof mechanism that opens when the moisture exceeds 14% and closes when it falls below 10%, in accordance with the Indonesian National Standard (SNI). The system was tested on a small scale under several scenarios, including measurements of dry and wet tobacco, notification delay testing, and internet connection range testing. Experimental results show that the system can detect tobacco moisture content with satisfactory accuracy, with an average notification delay of about 2.8–3 seconds. The roof control mechanism driven by a servo motor successfully operated in 100% of trials. The system also worked effectively within a Wi-Fi connection range of up to 20 meters without obstacles and 15 meters with obstacles. In conclusion, this study demonstrates that an IoT-based solution using Wemos D1 provides a practical approach to maintaining tobacco moisture content within the required quality standards for small-scale storage. Future work may include employing more precise capacitive sensors, integrating historical data storage, and applying predictive models to enhance the reliability of automated tobacco storage systems.