Analyzing Compost Fermentation Accuracy Through Fuzzy Logic and R-Square Techniques - Putranto, Reza Firmansyah; Ningrum, Novita Kurnia (Politeknik Negeri Batam, 2026)
The accumulation of unmanaged organic waste remains a critical environmental issue, highlighting the need for technological support to improve composting efficiency and monitoring. This study proposes an Internet of Things (IoT)-based system for monitoring compost fermentation conditions using temperature and humidity sensors, combined with Fuzzy Logic and R-square (R²) analysis to evaluate fermentation quality. The system employs a DHT11 sensor integrated with an ESP8266 microcontroller to collect temperature and humidity data in real time over a 20-day observation period, resulting in 1,008 data points. Fuzzy Logic is applied through fuzzification, rule-based inference, and defuzzification to classify compost conditions into four categories: poor, good, very good, and cooling needed. The model’s performance is further validated using multiple linear regression, with temperature and humidity as independent variables and average temperature as the dependent variable. The results show that compost temperature ranged between 28–32°C and humidity between 50–87%, indicating that the fermentation process was predominantly in the mesophilic or early composting phase. The fuzzy inference results demonstrate that most conditions fell within the “good” category, while the R² value of 0.87 indicates a strong relationship between the observed variables. These findings confirm that the integration of IoT, Fuzzy Logic, and statistical analysis is effective as a real-time monitoring and decision support system for compost management, while also highlighting the need for additional parameters to achieve a more comprehensive compost quality assessment.