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Menu Sales Prediction at Kiyo Café Using Machine Learning

Authors

  • Jesi Fitriana Teknik Informatika Fakultas Ilmu Komputer Institut Informatika dan Bisnis Darmajaya
  • Joko Triloka Teknik Informatika Fakultas Ilmu Komputer Institut Informatika dan Bisnis Darmajaya

DOI:

10.47709/cnahpc.v6i2.3556

Keywords:

zxcv

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Abstract

This research evaluates the performance of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in predicting raw material stock for Café Kiyo. The study encompasses six key stages, including preparation, literature review, data collection, data mining processing, results and discussion, and conclusion with recommendations. The data mining process adheres to the Knowledge Discovery in Databases (KDD) framework, involving data selection, preprocessing, transformation, data mining, and interpretation and evaluation. The evaluation metrics reveal that KNN boasts a marginally higher accuracy of 98.71% compared to Naïve Bayes with 98.21%. KNN also demonstrates superior precision (81.25%) in identifying true positives, outperforming Naïve Bayes (72.59%). However, Naïve Bayes excels in recall, achieving 95.15% compared to KNN's 50.00%. The Area Under the Curve (AUC) analysis further confirms Naïve Bayes' superiority, with an AUC value of 0.995, indicating better performance in distinguishing between positive and negative classes.

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ARTICLE Published HISTORY

Submitted Date: 2024-02-03
Accepted Date: 2024-02-05
Published Date: 2024-04-01

How to Cite

Fitriana, J., & Triloka, J. (2024). Menu Sales Prediction at Kiyo Café Using Machine Learning. Journal of Computer Networks, Architecture and High Performance Computing, 6(2). https://doi.org/10.47709/cnahpc.v6i2.3556