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

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Rancang Bangun Sistem Klasifikasi Kemurnian Susu Sapi dengan menggunakan Metode Naive Bayes Dimas Rizqi Firmansyah; Dahnial Syauqy; Fitri Utaminingrum
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

Cow's milk is a popular food and beverage consumed by the public. The benefits generated from cow's milk are numerous, because they contain protein, vitamins, and minerals needed in the body. Sales of cow's milk are often found in rural, urban, tourist attractions, roadside, to restaurants. Because there are so many people who need cow's milk, there are often bad sellers or sellers selling impure cow's milk. Because by falsifying cow's milk, naughty sellers benefit very much. From the falsification of the purity of cow's milk, there are a lot of losses felt by consumers, including consumers being a loss, so that the worse is consumers can be hospitalized because falsified milk is included ingredients that are not suitable for food. Therefore, to help the public not to get caught buying cow's milk which has been mixed with water by an individual, tools are needed that are able to test the purity of cow's milk directly and quickly. Because of this problem, a research was carried out to build a tool that could detect cow's milk, mixed milk or pure milk. This research requires a TCS3200 color sensor which is used to detect color in cow's milk, and also a pH sensor to obtain the acidity value in cow's milk. For the classification results using the Naive Bayes method calculation. The choice of using the Naive Bayes classification is because the method can be used to process biased data and accurate calculation results. Based on the test results, obtained an accuracy of Naive Bayes calculation of 90% taken from 20 times the test, and the test there are 2 results that are not appropriate. While the speed of calculating the device starts from the taking of the value by the sensor until the tool can issue an average classification result of 6932 ms.
Co-Authors Dahnial Syauqy Utaminingrum, Fitri