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

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Sistem Identifikasi Jenis Makanan dan Perhitungan Kalori berdasarkan Warna HSV dan Sensor Loadcell menggunakan Metode K-NN berbasis Raspberry Pi Muhammad Rizqi Zamzami; Dahnial Syauqy; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 3 (2021): Maret 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Overweight and obesity are still common diseases in this world, which is caused by an unhealthy lifestyle, one of which is consuming excessive food. This excessive food consumption is caused by several factors, namely emotional problems, environmental and social conditions and certain physical conditions. If food consumption is not controlled and is not balanced with a lot of body activity, it will cause calorie accumulation in the body, resulting in obesity and obesity and a risk of disease. One way to overcome this is by controlling yourself in consuming food by measuring the number of calories that will be consumed. From these problems, a system is made to measure food calories by identifying the type of food and measuring the weight of the food. The identification of these foods uses the k-Nearest Neighbor method and the Loadcell sensor to read the weight of the food being measured. The system will capture images and read the weight of the food measured through the camera module and loadcell sensor. The image is then processed on the Raspberry Pi 3 B to extract the color value from the mean HSV. Furthermore, the extraction results are used as a feature to identify the type of food which is used to measure food calories based on the identification and measurement results of the Loadcell sensor. The results of the system will be displayed on the 16 × 2 LCD screen. The system test uses 5 samples for each type of food. From the test results, the accuracy at k = 3 is 96%, at k = 5 is 92% and at k = 7 is 92%.
Co-Authors Dahnial Syauqy Fitriyah, Hurriyatul