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

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Implementasi Prototype Kapal sebagai Sistem Monitoring Kualitas Air menggunakan Algoritme Naive Bayes Axel Elcana Duncan; Rizal Maulana; Eko Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 7 (2020): Juli 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Living things really need water as a source of life. Lots of people use water in the waters for various things. Water quality standards using three characteristics, physics, chemistry, and biology are prerequisites for water quality. Through its very important role for all living things, it is very much needed an embedded system that is equipped with an algorithm for monitoring water quality, given the complexity, and conventionality of existing methods for measuring water quality. A ship prototype system using Arduino Nano, WEMOS D1 Mini, Genuine Analog pH Meter sensor, DS18B20 sensor, Turbidity sensor, JSN-SR04T sensor, L298N motor driver, and 6V DC motor can help from existing problems. This system is in the form of a ship so that it can carry out comprehensive monitoring. This system also uses the Naive Bayes algorithm in classifying through features such as pH, temperature, turbidity, and depth as inputs. Good, medium, and bad results will be the class will be output. In getting these results, this system uses as many as 40 training data and 20 test data. The average error reading of the Genuine Analog pH Meter sensor from 10 tests is 8,233%. The average error of DS18B20 sensor readings from 10 tests is 0.859%. Turbidity sensors have a linear graph, the more turbid the water the smaller the voltage value. The average error of sensor reading JSN-SR04T from 10 times the test is 2.492%. The accuracy of sending data from 10 tests is 100%. The accuracy of the classification using the Naive Bayes algorithm is 90%. The average computational time performed by the system from 10 tests is 3.7055 ms. The accuracy rate on the drive system from 3 times of testing is 100%.
Co-Authors Eko Setiawan Rizal Maulana