Analisis Kinerja Sistem Penyortiran Sayuran Otomatis Menggunakan Sensor Warna Dan Berat

This research aims to design and implement an Internet of Things (IoT)-based automatic vegetable sorting system that can classify vegetables based on color and weight. The system is designed using an ESP32 microcontroller as the control center, a TCS34725 color sensor to detect the color type of vegetables, and a loadcell weight sensor with HX711 module to measure the weight of vegetables. Both sensors work in an integrated manner, with the sorting results sent and displayed in real-time through the Blynk application. The system is designed as a trainer kit-based learning media for the greenhouse environment at Politeknik Negeri Jakarta.
The test results show that the weight sensor has high accuracy with an average value of 97.6%, and the relative error is within the range of 1.4% to 2.7%. The color sensor is also able to classify objects correctly, with 100% accuracy and an average response time of 3.5 milliseconds. The automatic sorting process successfully directs all vegetable samples to the appropriate container based on predefined weight and color classification parameters. Sensor calibration was performed to ensure the readings were close to the actual values, and the results showed the stability of the system throughout the process.
With high accuracy and fast processing time, the system shows reliable performance and is worth further development as a simple automation solution in agriculture, especially for horticultural products such as tomatoes and peppers.