IMPLEMENTASI SISTEM PEMANTAUAN TANAMAN HIDROPONIK BERBASIS IOT MENGGUNAKAN METODE KLASIFIKASI KNN - Aditya Satriyo Wibisono; Norma Ningsih; Afifah Dwi Ramadhani (Prosiding Seminar Nasional Terapan Riset Inovatif (SENTRINOV), 2025)
This project displays a hydroponic plant monitoring system that uses an ESP32 microcontroller with a range of sensors—including TDS, pH, light, temperature, humidity, and water level—to keep an eye on environmental conditions. The data collected is processed and shared through platforms like Blynk, Telegram, and a simple 16x2 I2C LCD display. To predict how healthy the plants are, the system uses the K-Nearest Neighbors (KNN) algorithm based on the real-time sensor data, helping users keep conditions just right. Tests with five different data setups showed that the sensors are pretty accurate: HC-SR04 at 90.9%, DHT11 temperature at 99.29%, DHT11 humidity at 90.32%, and BH1750 at 98.84%. The TDS sensor needs calibration using a 1382 ppm solution before use. When connected to a 46 Mbps network, the data takes about 1.4 to 2.1 seconds to travel from the system to the user interface. Overall, this setup provides dependable, real-time environmental monitoring combined with smart analysis, making it a helpful tool for hydroponic farming.