The digital poultry sector faces challenges in maintaining stable incubation temperatures. Comparative evaluation between Sugeno and Tsukamoto methods has not been conducted in real-world IoT systems. This research is essential to identify the most effective fuzzy logic control approach for smart incubators. This study aims to compare the effectiveness of Sugeno and Tsukamoto fuzzy logic methods through implementation in the SCAMIS platform. A comparative experimental design was employed using Sugeno and Tsukamoto models in SCAMIS. Temperature data were collected via DHT22 sensors and analyzed quantitatively. Code validation and sensor accuracy tests ensured data reliability and the credibility of fuzzy decision-making processes. The accuracy level of the DHT22 sensor when detecting temperature is 98.31%. The comparison of response time from initial temperature to target temperature (30°C - 38°C) between the Tsukamoto and Sugeno fuzzy logic methods is 1 : 3. Tsukamoto takes 1 hour 52 seconds (3,652 seconds), while Sugeno takes 3 hours 1 second (10,801 seconds). The response time required by Tsukamoto for each 1°C temperature increase is 1–7.5 minutes, while Sugeno is 1–10.3 minutes. However, in terms of temperature stability, Sugeno is more stable with an accuracy rate of 98.40% approaching the target temperature, while Tsukamoto has an accuracy rate of 96.93%. These results indicate a significant trade-off between the speed of reaching the target temperature and stability at the target temperature. Control method selection should align with specific operational priorities in smart incubation systems. These findings recommend choosing fuzzy methods based on system priorities. Future studies should evaluate energy consumption and hatching success efficiency.