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

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Implementasi Naive Bayes pada Embedded System untuk Menentukan Status Gizi Bayi Fauzi Rivani; Dahnial Syauqy; Mochammad Hannats Hanafi Ichsan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

A baby is a stage of human development from 0-12 months where brain development and physical development are the main focus interest to see the baby growth. Nutrition status is one of the essential components that is always under supervision for baby growht. Baby's nutrition system is not only measured in terms of whether they are good or bad nutrition, but also being measurd through baby's height and baby's weight. Based on these problems, needed research on the classification system to detecting nutrition of baby and baby's parents can monitor the development nutrition from their baby. Furthermore, there are other parameters such as gender and age that serve as contributing factor when measuring baby's nutrition, A censor with microcontroller has already been developed to provide specific number of the baby's gender and age which then will be input to a system using 4x4 keypad. On the other hand, ultrasonic censor and load cell are used as a measurement system for baby's height and weight. Then all these data will be input and compared to arduino uno classification that will determine the baby's nutrition. The method use for this classification is called Naive Bayes. All the development stages will be put on 16x2 LCD to provide an assessment and analysis regarding the accuracy of this system. Based on the reading the censor provides on 10 objects, the heihgt (lenght) censor has 98.28% accuracy and weight with 71.02% accuracy. The classification test with 30 data that is picked randomly has 100% accuracy for the weight and height. Then average of time classification test are 0.026 second and 0.032 second respectively. The test and analysis of the overall system has 96.66% for ist accuracy for the height and 60% accuracy for the weight system.
Co-Authors Dahnial Syauqy Mochammad Hannats Hanafi Ichsan