SISTEM PEMANTAUAN KUALITAS AIR IKAN NILA MEDIA BIOFLOK MENGGUNAKAN ALGORITMA LSTM DAN SVM - Nugroho, T. Arief; Angela, Dina; Yoel Wijaya, Yoel Wijaya (LPPM Universitas Putera Batam, 2025)
Tilapia farming with biofloc systems faces challenges in maintaining water quality stability due to fluctuations in temperature and pH that can affect fish health. To overcome this, this research designs an Internet of Things (IoT)-based water quality monitoring system integrated with machine learning-based prediction and classification methods. The system is built using NodeMCU ESP8266, DS18B20 (temperature), and PH-4502C (pH) sensors. Historical data from the sensors is processed using the Long Short-Term Memory (LSTM) algorithm to predict the values of water quality parameters. Next, the predicted results are classified using a Support Vector Machine (SVM) into three categories: “Air Terlalu Asam”, “Kualitas Air Baik”, dan “Air Terlalu Basa”. The classification information is then automatically sent to the farmer via the Telegram application. Model testing showed very high performance, where the LSTM prediction model achieved an R-squared value of 0.8871 with a very low error rate (MAE: 0.0115; MSE: 0.0019; RMSE: 0.0436), while the SVM classification model managed to achieve an accuracy of 99.86%. The implementation of this system is expected to assist farmers in making quick and precise decisions, reduce the risk of fish mortality, and increase time and labor efficiency in biofloc pond operations..