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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Hurriyatul Fitriani
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Sistem Kendali Kualitas Air Kolam Ikan Nila dengan metode Jaringan Syaraf Tiruan berdasarkan PH dan Turbidity berbasis Arduino Uno Satya Pradhana; Hurriyatul Fitriani; Mochammad Hannats Hanafi Ichsan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 10 (2021): Oktober 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In freshwater fish farming, it is important to pay attention to the quality of the air for fish to live. Tilapia still has high economic value due to high market demand. Excessive feeding of fish will affect the rest of the feed and can cause a decrease in the quality of fish pond water, so that it can directly affect fish productivity. In addition, the optimal air pH for tilapia habitat is between 6.5 - 8.5. For more optimal air turbidity is 50 NTU. One solution that can be used is to build a system that can be made to control the water quality of a tilapia. The system can be made based on a control system based on Artificial Intelligence (AI). One of the application branches of AI is forecasting algorithms, one of which is the Artificial Neural Network algorithm to determine the estimated run time of water filter pumps. In this study, the tools used by the author are a pH sensor to determine the pond water, a turbidity sensor to determine the cleanliness of the pond and a Real Time Clock (RTC) to determine the time of fish water. The data will be used by Arduino UNO to perform computations on existing data using the artificial neural network method. Then use the relay as a regulator on or off the fish water filter pump. The relay will turn the fish filter on and off. Then the I2C-based 16x2 LCD will display the water conditions in text form. If the water conditions are not optimal, the buzzer will sound. For the installation of this tool will be placed on the photo of tilapia. The results of the analysis carried out by the artificial network method obtained an average accuracy of 87.4% and a computation time of 3.29 seconds.
Co-Authors Mochammad Hannats Hanafi Ichsan Satya Pradhana