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Purwanto, Sofi Dwi
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Computer Science & IT

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Analisis Perbandingan Algoritma Naive Bayes Classifier dan Learning Vector Quantization dalam Sistem Identifikasi Boraks pada Bakso Daging Sapi Fauzan, Abd. Charis; Purwanto, Sofi Dwi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 7, No 2 (2021): Desember 2021
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (474.449 KB) | DOI: 10.24014/coreit.v7i2.11564

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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