Open AccessNaive Bayes Algorithm Implementation Based on Particle Swarm Optimization in Analyzing the Defect Product
Author(s) -
Ikhsan Romli,
Toga Pardamean,
Sufajar Butsianto,
Tri Ngudi Wiyatno,
Effendi Mohamad
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1845/1/012020
Subject(s) - particle swarm optimization , naive bayes classifier , bayes' theorem , product (mathematics) , algorithm , computer science , productivity , quality (philosophy) , machine learning , mathematical optimization , artificial intelligence , mathematics , bayesian probability , economics , support vector machine , geometry , philosophy , epistemology , macroeconomics
In the era of progressively more competitive industrial competition, especially in the manufacturing world, it is always required to develop the quality or quality of products and productivity. Each company is compete to win market share. One of the strategies carried out by the company is improving the quality of products and the production process conducted by the company. In the industrial world, product quality and productivity are the keys for success of the production process. Therefore, the purpose of this study is to analyze data for defective products at PT Mane Indonesia with the Particle Swarm Optimization (PSO) and Naïve Bayes Classifier method. The accuracy results using the Naïve Bayes algorithm get a value of 84.38% and an AUC value of 0.953. The results of the PSO-based Naïve Bayes algorithm get a value of 88.62% and AUC value of 0.945. Based on the research which has been performed by using Naïve Bayes based on PSO, it developed a contribution rate of 5,02% in predicting the defected products.