Open AccessMeasure the effectiveness of information systems with the naïve bayes classifier method
Author(s) -
Agung Triayudi,
Sumiati Sumiati,
Saleh Dwiyatno,
Dentik Karyaningsih,
Susilawati Susilawati
Publication year - 2021
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 7
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v10.i2.pp414-420
Subject(s) - computer science , naive bayes classifier , classifier (uml) , bayes classifier , bayes' theorem , machine learning , artificial intelligence , data mining , bayesian probability , support vector machine
Technological advances at this time are developing very fast, information systems became the frontline in technological advancements, the need for information systems to support jobs is increasingly high. However, its implementation for users does not have a significant impact, so that it needs to be reviewed and re-evaluated in the use of the information system built. The naive bayes classifier method can provide "effective" and "ineffective" conclusions and is used as material for evaluation and improvement. The purpose of this study is to contribute to measuring the effectiveness of the information system, to solve problems with the naïve bayes classifier method approach which has advantages in the process of classifying data and predicting data. From the test results three times, training has been conducted using 100 data, accuracy value of 84.82% and error 15.18%.