Sistem Monitoring Hidroponik Menggunakan Metode K- Nearest Neighbor Dalam Aplikasi Mobile

Abstract

Hydroponics is a soil-less farming method utilizing a controlled nutrient solution, providing optimal conditions for plant growth. This study aims to develop an IoT-based monitoring system to classify pH, temperature, and TDS parameters in the hydroponic cultivation of pakcoy using the K-Nearest Neighbor (KNN) method. The system employs pH and temperature sensors to measure parameters in real-time, transmit data to a server, and display analytical results through a mobile application. The KNN method is applied to determine whether water conditions are within the optimal range, achieving a classification accuracy of 90.46%. At the selected value of k = 7, the system recorded an accuracy of 92.30% on the first fold, 95.00% on the second fold, 85.00% on the third fold, 85.00% on the fourth fold, and 95.00% on the fifth fold, resulting in an average accuracy of 90.46%. Additionally, the system supports automatic notifications if parameters fall outside the optimal range. Designed with a focus on IoT connectivity, user-friendliness, and energy efficiency, this system is expected to assist farmers in enhancing productivity and monitoring efficiency in hydroponic cultivation.