Deteksi Anomali Konduktivitas Air Menggunakan Kalman Filter

Abstract

Water quality is an essential part of shrimp farming. Data integrity is one of the challenges in building a water conductivity monitoring system. Data read by the sensor should represent the physical conditions that occur. However, some factors can cause abnormal data changes. This abnormal data change can occur due to sensor damage or an attempt to sabotage the pool. In this study, a data anomaly detection algorithm was built using the Kalman filter and standard deviation to solve the problem of determining the normal range of data. The designed algorithm was then tested and evaluated using Arduino nano, Arduino mega, and Wemos D1 Microcontrollers to determine the algorithm's performance on limited computing devices. Based on the data analysis that has been carried out, it is found that the anomaly detection algorithm based on the Kalman filter has an accuracy of 92.5% and can detect anomaly data that occurs with TPF = 1 and FNR = 0 values. The implementation of the detection algorithm on the microcontroller shows that WEMOS D1 (ESP8266) has an excellent average computational speed of 27.99 us. As for the stability of the Arduino Nano (ATMEGA328) and Arduino Mega 2560 (ATMEGA 2560) microcontrollers, the computation time deviation is about 2.8 us.