Implementation of the Naïve Bayes Method to determine the Level of Consumer Satisfaction

Authors

  • Fitri Febriyani Hasibuan Universitas Labuhanbatu
  • Muhammad Halmi Dar Universitas Labuhanbatu
  • Gomal Juni Yanris Universitas Labuhanbatu

DOI:

10.33395/sinkron.v8i2.12349

Keywords:

Classification, Confusion Matrix, Data Mining, Naïve Bayes, Satisfaction

Abstract

Satisfaction is a feeling of pleasure at something you like, you get it from goods and services. Satisfaction becomes an important assessment when someone sells goods or services. This is because satisfaction will be an assessment of the goods purchased by consumers or services that will be received by consumers. Therefore the authors make research about the level of consumer satisfaction in shopping. This research was made using the Naïve Bayes method and used consumer data as sample data which used 49 consumer data. By using the Naïve Bayes method, this study aims to see the level of consumer shopping satisfaction, it is made to see the results of a consumer's satisfaction, sometimes there are some consumers who are dissatisfied with the reason the product is not good and some are satisfied with the reason the product is still new and good. Therefore this research was made. This research was conducted using the naïve Bayes method with the first stage being data analysis, then data preprocessing, then naïve Bayes algorithm and finally system testing. After system testing is carried out, classification results will be obtained using the naïve Bayes method. Classification results stated that as many as 47 consumers were satisfied shopping and as many as 2 consumers were not satisfied shopping. The conclusion is that a lot of consumers are satisfied with shopping, meaning that the place is very good and liked by many consumers.

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How to Cite

Hasibuan, F. F., Dar, M. H. ., & Yanris, G. J. . (2023). Implementation of the Naïve Bayes Method to determine the Level of Consumer Satisfaction. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 1000-1011. https://doi.org/10.33395/sinkron.v8i2.12349

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