Mohammad Andy Purwanto
Fakultas Ilmu Komputer, Universitas Brawijaya

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Implementasi Fuzzy Logic pada Sistem Monitoring Kualitas Air Kolam Renang dan Aplikasi Android Mohammad Andy Purwanto; Mochammad Hannats Hanafi Ichsan; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 2 (2022): Februari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Every living thing need water to life. One of the uses of water is to fill swimming pools. Swimming pool cleanliness becomes a problem, such as pollution of swimming pool water with urine. Not only that, swimming pool water must also pay attention to the temperature, pH and turbidity of the water so that the quality is maintained. However, the quality monitoring process seems slow and is not carried out regularly and continuously. Sometimes, the monitoring process carried out gets inaccurate results and impratical in practice. Therefore, a swimming pool water quality monitoring system was created with a fuzzy logic algorithm, assisted by the eFLL (embedded Fuzzy Logic Library) library which is used to determine whether the water quality is good or bad by processing the data obtained by the DS18B20 temperature sensor, pH -4502C and also the dfrobot turbidity sensor. Arduino Nano microcontroller is used with Atmega328 as a data processor obtained from the sensor. Then, with serial communication via bluetooth, users can see the monitoring results on an android smart phone. The test is carried out by measuring the quality of the swimming pool water in the morning and evening with an interval of 8 hours to get 40 test data. The results obtained are in the form of monitoring and storing data on smart phones. The accuracy of the DS18B20 temperature sensor is 98.75% with an average error of 1.25%, the pH-4502C sensor gets an accuracy of 97.52% with an average error of 2.48% and also the turbidity sensor test results that when the water is getting cloudy, the voltage value will be smaller . In addition, the accuracy of the fuzzy logic algorithm compared to MATLAB is quite high that is 98.49% with an average error of 1.51%..