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All Journal Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Pramudia, Henrikus
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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A Fuzzy Logic-Based Temperature Control System for Baby Incubators Pramudia, Henrikus; Syaifudin, Syaifudin; Kholiq, Abd.; Lawal, Kamilu O.
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 5 No. 4 (2023): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v5i4.190

Abstract

A The purpose of a baby incubator is to help preterm infants whose bodies cannot adapt to their new surroundings by providing them with artificial heat. The goal of this research was to develop a method of applying fuzzy control in conjunction with the DS18B20 sensor for analyzing the response points involved in the construction of a baby incubator. For this experiment, researchers employed conditions of 32 ˚C, 35 ˚C, and 36 ˚C. The Incu analyzer is utilized as the industry standard reference instrument. Fuzzy control on a microcontroller involves a few steps, including fuzzification, which involves inputting the value of the membership function, where this member is a collection of error and feedback values, in this case 0.5; this member is then processed further in fuzzification, which involves transforming raw crisp calculations into membership values via the function membership. A rule base is a set of rules developed to achieve a goal by specifying the appropriate control action in response to a particular input value using linguistic rules. Defuzzification performs calculations of changing fuzzy quantities presented in the form of variable values from the rule base with output values to set an output value that we need in the system. This fuzzy system produces an average rise point of 200 seconds and an overshoot value in the range of +0.50 C. Stability can be achieved within 8 to 10 minutes.
Co-Authors KHOLIQ, ABD. Lawal, Kamilu O. Syaifudin Syaifudin, Syaifudin