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Gentha Wijaya
Andalas University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Prototipe Pengukuran Kadar Gula dalam Tubuh Manusia Melalui Urin Mohammad Hafiz Hersyah; Budi Rahmadya; Gentha Wijaya
Jurnal Rekayasa Elektrika Vol 15, No 2 (2019)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1109.218 KB) | DOI: 10.17529/jre.v15i2.13034

Abstract

In this research, we propose a prototype to measure glucose index in human body after applying benedict reagen into urine samples. This system divides into two main components that are identification device and android smartphone. The identification device consists of TCS3200 colour sensor and a raspberry pi. The TCS3200 colour sensor's function is to predict the alteration of urine sample and determine the colour category according to the benedict rule and to measure the glucose in the sample. The Raspberry pi function is to process the data that acquired from the colour sensor. By optimizing with Tsukamoto Fuzzy Logic Control, the research successfully identifies the glucose by achieving 100% and the result of fuzzy logic control on Raspberry Pi as decision making by urine in 90% and by conflicting minimum error in 5.6%.
Prototipe Pengukuran Kadar Gula dalam Tubuh Manusia Melalui Urin Mohammad Hafiz Hersyah; Budi Rahmadya; Gentha Wijaya
Jurnal Rekayasa Elektrika Vol 15, No 2 (2019)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v15i2.13034

Abstract

In this research, we propose a prototype to measure glucose index in human body after applying benedict reagen into urine samples. This system divides into two main components that are identification device and android smartphone. The identification device consists of TCS3200 colour sensor and a raspberry pi. The TCS3200 colour sensor's function is to predict the alteration of urine sample and determine the colour category according to the benedict rule and to measure the glucose in the sample. The Raspberry pi function is to process the data that acquired from the colour sensor. By optimizing with Tsukamoto Fuzzy Logic Control, the research successfully identifies the glucose by achieving 100% and the result of fuzzy logic control on Raspberry Pi as decision making by urine in 90% and by conflicting minimum error in 5.6%.
Co-Authors Budi Rahmadya Hersyah, Mohammad Hafiz