Sistem Monitoring Suhu dan Kontrol Otomatis Kelembapan Penyimpanan Benih Berbasis Internet of Things Secara Real-time

Abstract

Penelitian ini dilatarbelakangi oleh pentingnya menjaga kualitas benih padi selama penyimpanan agar viabilitas tetap terjaga. Tujuan penelitian ini adalah merancang dan mengimplementasikan sistem monitoring suhu dan kontrol otomatis Kelembapan berbasis Internet of Things (IoT) secara real-time dengan memanfaatkan mikrokontroler ESP32, sensor DHT22, aplikasi Blynk, serta pencatatan data otomatis pada Google Sheets. Metode penelitian mencakup pengujian sensor menggunakan analisis Mean Absolute Error (MAE), analisis deskriptif kuantitatif terhadap data suhu dan Kelembapan, serta analisis respon sistem terhadap perubahan kondisi lingkungan. Hasil penelitian menunjukkan bahwa sistem mampu menampilkan data real-time, menyimpan data secara otomatis, serta merespon perubahan suhu dan Kelembapan sesuai ambang batas dengan tingkat keberhasilan 100%. Uji akurasi sensor menghasilkan nilai MAE sebesar 0,325 °C untuk suhu dan 2,283% untuk Kelembapan, keduanya masih dalam toleransi pabrikan. Namun, penelitian ini terbatas pada parameter suhu dan Kelembapan, ruang lingkup pengujian yang singkat, serta kondisi ruangan yang belum sepenuhnya stabil, sehingga sistem masih perlu dikembangkan dan diuji lebih lanjut agar dapat digunakan secara lebih luas.

This research was motivated by the importance of maintaining the quality of rice seeds during storage to preserve their viability. The purpose of this study was to design and implement a real-time temperature monitoring and automated humidity control system based on the Internet of Things (IoT) using an ESP32 microcontroller, DHT22 sensor, Blynk application, and automatic data recording on Google Sheets. The research methods included sensor testing using the Mean Absolute Error (MAE) analysis, quantitative descriptive analysis of temperature and humidity data, and system respone analysis to environmental changes. The results showed that the system successfully displayed real-time data, stored data automatically, and responded to changes in temperature and humidity according to the threshold with 100% reliability. Sensor accuracy testing yielded an MAE value of 0.325 °C for temperature and 2.283% for humidity, both within the manufacturer’s tolerance limits. However, this study was limited to temperature and humidity parameters, a relatively short testing duration, and an environment that was not fully stable. Therefore, the system still requires further development and testing before it can be widely applied.