PENERAPAN ALGORITMA NAIVE BAYES UNTUK KLASIFIKASI PRODUK SELF DECLARE (STUDI KASUS : BADAN PENYELENGGARA JAMINAN PRODUK HALAL)

The Halal Product Guarantee Administering Body (BPJPH) is a work unit under the Ministry of Religion which was formed in accordance with the mandate of Law Number 33 of 2014 concerning Halal Product Guarantee. In order to succeed in the target of 10 million halal certified products, BPJPH, Ministrie...

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Main Author: Gustian Abrary Shidqi, (Author)
Format: Book
Published: 2023-12-15.
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520 |a The Halal Product Guarantee Administering Body (BPJPH) is a work unit under the Ministry of Religion which was formed in accordance with the mandate of Law Number 33 of 2014 concerning Halal Product Guarantee. In order to succeed in the target of 10 million halal certified products, BPJPH, Ministries/Agencies, Regional Governments, and other BPJPH partners are here to provide strengthening for micro and small business actors through the Free Halal Certification (SEHATI) program. A total of 1 million MSE registrant quotas are prepared to obtain free halal certificates using a self-declaration mechanism. Self Declare is a statement of the halal status of Micro and Small Business (UMK) products by the business actor himself. At the product verification stage carried out by BPJPH, the Self Declare product classification process is still carried out by BPJPH staff. This method still takes a long time and is susceptible to errors such as inspection errors by BPJPH staff. The aim of this research is to classify Self Declare products or not using a dataset containing information data regarding the 2022 BPJPH Self Declare products from BPJPH. In this research, the data is divided into training data and test data with a ratio of 80% training data and 20% test data. The method used in this research is the Naive Bayes algorithm. The results of the research are that the Naive Bayes algorithm model can be used to classify Self Declare products. The results of the evaluation of the Naive Bayes Algorithm model with a comparison of training data and test data of 80%:20% produce an accuracy of 0,94 (94%), precision of 0,99 (99%), recall of 0,94 (94%), and F1-Score of 0,96 (96%). 
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