Comparative Analysis of Naïve Bayes Variants for Best-Seller Status Determination in the Air Conditioning Industry - Sasa Ani Arnomo; Zada Alzena; Heri Nuryanto; Siti Fairuz Nurr Sadikan (Prodi Teknik Informatika FIK Universitas Muslim Indonesia, 2026)
The determination of product status such as the best seller predicate for Air Conditioning products is a fundamental sales promotion strategy. Leveraging machine learning to analyze sales data is essential for maximizing business progress and market positioning within the modern electronics industry. This study aims to evaluate and compare the performance and accuracy of Naïve Bayes (NB) classification models in determining AC product status. The goal is to identify the most effective variant among BernoulliNB, GaussianNB, and MultinomialNB for this specific application. A quantitative comparative analysis was conducted using various data record sizes. The primary features analyzed included brand, inverter type, electrical power cooling capacity, address, price, and sales status. In the first scenario, testing across varying data record sizes revealed that the MultinomialNB variant achieved the highest average accuracy at 85.39 percent. In the second scenario, using the full dataset with an 80% training data split without cross-validation, the Naïve Bayes algorithm as a whole demonstrated robust classification ability, reaching a peak prediction accuracy of 99.312 percent. This peak performance significantly outperformed alternative methods such as SVM and KNeighbors Classifier within those specified parameters. This performance significantly outperformed alternative methods such as SVM and KNeighbors Classifier within the specified parameters. The study concludes that the Naïve Bayes model is highly effective for product status classification in the electronics industry. The MultinomialNB variant is identified as the most consistently reliable model for these specific datasets. Future research should consider incorporating cross-validation techniques to further validate model stability across more diverse data environments.