Literature Review: Implementation of the Naive Bayes Algorithm for Classification in Various Fields of Data Mining
DOI:
https://doi.org/10.61098/ijiretm.v3i1.269Keywords:
Naive Bayes, Classification, Data Mining, Literature Review, Classification AlgorithmsAbstract
The significant increase in data volume across various sectors demands efficient, accurate, and adaptive classification methods. The Naive Bayes algorithm is one of the probabilistic classification techniques widely used in data mining due to its model simplicity and its capability to handle high-dimensional data. This study aims to systematically review the application of the Naive Bayes algorithm for data classification in various sectors in Indonesia through a Systematic Literature Review (SLR) approach. Data were obtained from scientific journals published in the last five years (2019–2024) relevant to the topic and analyzed using qualitative descriptive methods. The review results show that Naive Bayes is widely applied in the fields of health, education, social sciences, economics, and technology. Most studies report high accuracy rates, particularly in text classification and imbalanced dataset cases. However, the limitation of this algorithm lies in the assumption of attribute independence, which is often not met in real-world cases. Therefore, several studies combine Naive Bayes with other methods to improve performance. This study provides a comprehensive overview of the strengths and weaknesses of Naive Bayes and serves as a reference for selecting appropriate classification methods in future data mining applications.
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