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Rint Zata Amani
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Sistem Pendeteksi Dehidrasi Berdasarkan Warna dan Kadar Amonia pada Urin Berbasis Sensor TCS3200 Dan MQ135 dengan Metode Naive Bayes Rint Zata Amani; Rizal Maulana; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 5 (2017): Mei 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Dehydration is a condition when human body tissue's loss of fluid abnormally and this condition often underestimated by common people, so in case on serious level illness of dehydration will causes death. However nowadays to detect dehydration still according to analysis from health team by some clinical sign that cause dehydration. From this problem, it is needed a research about automatic system to detect dehydration level that can use by common people, so can decrease amount of dehydration's patient that untreated since begining. On this research, the parameters were used to compare dehydration's level are color and level of ammonia in human urine. The reason of using urine as research object is because urine condition reflect fluid condition in human body. Process to determine dehydration's level from color and level of ammonia in human urine is perform with read data color sensor TCS3200 and gas sensor MQ135 by Arduino Uno Microcontroler with Naive Bayes method. Naive Bayes method is selected as a technique to make a decision of dehydrations level because this method was one of classification method that good, which the classification classes of dehdydration level were already known since begining. From some testing result, it has known that the error percentage of color sensor TCS3200 on read the color object was 2,70% and the corelation value of gas sensor MQ135 reading with out voltage of sensor was 99,81%. And then on system testing by Naive Bayes method with amount of training data was 46 data and testing data as many as 23 data, the accuration reached 95,65% with average computation time was 0,69 second.
Co-Authors Dahnial Syauqy Rizal Maulana