Perbandingan Algoritma K-Nearest Neighbor dan Naive Bayes Untuk Klasifikasi Status Penjualan Furniture dengan Data Historis

Bangkit Dwi Sucahyo; Sampurna Dadi Riskiono

doi:10.47065/josh.v7i2.8675

Bangkit Dwi Sucahyo * Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
Sampurna Dadi Riskiono Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/josh.v7i2.8675

Keywords: K-Nearest Neighbor; Naïve Bayes; Sales Prediction; Data Mining; Machine Learning

Abstract

This study aims to evaluate the performance of two machine learning classification methods, K-Nearest Neighbor (KNN) and Naive Bayes, in predicting the sales status of furniture products at CV. Surya Gemilang. The data used comes from previous sales records and includes details such as product category, product name, price, sales amount, revenue, and sales status, which are labeled "Best Selling" and "Not Selling". This study follows several steps, including data collection, data cleaning and organization, labeling, model training, and performance assessment using accuracy, precision, recall, and F1-score. The research process includes data preprocessing, handling missing values, encoding categorical features, normalizing numeric features, separating training and testing data, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The results show that the KNN algorithm achieves 97% accuracy, 100% precision, 95% recall, and 0.97 F1-score. Meanwhile, the Naïve Bayes algorithm achieved 85% accuracy, 92% precision, 81% recall, and an F1-score of 0.86. These findings indicate that KNN is better able to recognize complex patterns in sales data than Naïve Bayes. The contribution of this research is to provide a machine learning-based classification model that can be used to support production planning and marketing strategies by predicting furniture product sales levels. The results show that KNN achieved 97% accuracy, while Naïve Bayes only achieved 85%. This indicates that KNN is better at identifying complex relationships between features in sales data, while Naïve Bayes is less effective because it assumes all variables are independent. In summary, KNN is more effective in classifying furniture product sales status and can be the basis for making informed business decisions based on data. This research makes a significant contribution to the application of machine learning in small and medium-sized enterprises, helping to improve sales forecasting and develop more effective marketing strategies.

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