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Dimas Dwi Saputra
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Sistem Klasifikasi Bakso yang Mengandung Boraks dengan Sensor Warna Menggunakan Metode K-Nearest Neighbor Berbasis Arduino Dimas Dwi Saputra; Hurriyatul Fitriyah; Eko Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Meatballs are a favorite meal of Indonesian people from various backgrounds, there are various types of meatballs based on the use of meat such as chicken meatballs, beef meatballs, shrimp meatballs or rabbit meatballs. Meatballs are also foods that have a delicious taste and nutritional content, protein, vitamins in meatballs can benefit the body. But many meatballs have been circulating with the content of dangerous chemicals in this case is borax. Borax is a chemical compound for use as a wood preservative, an insect repellent and detergent-making material. The use of borax material in meatballs is intended so that meatballs sold by unscrupulous traders can be more durable and have a good texture and shape, so that people can be interested in buying the meatballs. To overcome these acts of cheating, it is necessary to design a system to classify meatballs containing borax with meatballs that do not contain borkas. In order for the system to be implemented, it requires an Arduino Mega microcontroller to process data as well as classification calculations, a color sensor to distinguish colors from the tested meatball object and a pH sensor to detect pH levels on the meatballs. In order for this system to classify, this system will use the K-Nearest Neighbor classification method by using the K value 3,5,7,9,11,13,15,17. The results of the difference in K values ​​will be compared with other K values ​​to find out which K value has the highest accuracy. From testing on the system, the highest accuracy is obtained at 93.33% by applying the K 5 value to the K-Nearest Neighbor method.
Co-Authors Eko Setiawan Fitriyah, Hurriyatul