Analisis Perbandingan Algoritma Naive Bayes Classifier dan Learning Vector Quantization dalam Sistem Identifikasi Boraks pada Bakso Daging Sapi

Abd. Charis Fauzan, Sofi Dwi Purwanto

Abstract


Meatballs are processed meat products that are prone to be mixed with harmful substances as processed ingredients. This study aims to analyze the performance of the Naive Bayes Classifier method and the Learning Vector Quantization neural network as an object of comparison in order to find the best approach to detecting the borax content in beef noodles. The data used in the study consisted of 2 populations, namely data from processed meatballs independently with different levels of borax and data from surveys in the field. Based on the results of data testing using the instrument, the best accuracy level is the Naive Bayes Classifier approach, which is 93.33% for the population of meatballs containing borax. While the test for data without using the best performance tool also obtained the Naive Bayes Classifier approach with an accuracy of 79.34%.

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DOI: http://dx.doi.org/10.24014/coreit.v7i2.11564

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