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

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Sistem Tertanam Pendeteksi Kondisi Ideal Fermentasi Susu Kefir berdasarkan Kadar Alkohol dan pH menggunakan Metode Naive Bayes Izza Febria Nurhayati; Dahnial Syauqy; Rizal Maulana
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

Kefir is a fermented milk product that contains probiotics which is very useful for body health. Kefir is fermented milk that contains alcohol and has a low pH than milk. At this time in the process of kefir milk fermentation is done manually so as to allow failure and a decrease in the quality of kefir milk. From these problems, a study was conducted called the Embedded Detection System for Ideal Fermentation of Kefir Milk based on Alcohol Levels and pH using the Naive Bayes Method, so kefir milk producers can improve the quality of kefir milk and reduce the potential for failure during the kefir manufacturing process. In this study the parameters used in determining the condition of kefir milk are pH and alcohol content. PH and alcohol parameters play a role in determining how long the fermentation takes place so that the condition of the kefir milk is finally known. The pH was detected using a SKU SEN pH sensor and the alcohol content in kefir milk was detected using an MQ-3 sensor and processed by the Arduino Uno microcontroller using the Naive Bayes method. The use of the Naive Bayes method was chosen for the classification of kefir milk conditions, because this method is one classification method that is quite effective and fast in its calculations. From the results of several tests conducted it is known that the error percentage of the SKU-SEN pH color sensor reading is 10.087% and the error percentage value of the MQ-3 gas sensor is 12.65%. In testing the accuracy of Naive Bayes classification obtained 70% with 10 test data from 60 training data with a system computing time of 3,0781 seconds..
Co-Authors Dahnial Syauqy Rizal Maulana