Smartphone Rating Classification Based on Technical Specifications Using Naive Bayes and Feature Importance

Eka Yunizar Zaimatus Sa'diyah

doi:10.59934/jaiea.v5i2.2022

Authors

Eka Yunizar Zaimatus Sa'diyah Universitas Negeri Surabaya

DOI:

https://doi.org/10.59934/jaiea.v5i2.2022

Keywords:

Naive Bayes, Feature Importance, Rating Classification, Mutual Information, Smartphone, Machine Learning

Abstract

Classifying smartphone ratings based on technical specifications is crucial for market analysis and consumer electronics marketing strategies. This study applies the Naive Bayes algorithm to categorize smartphone ratings into low, medium, and high levels, while identifying influential features via Mutual Information scores. The dataset includes 1,159 smartphones with eight categorical predictors: screen size, RAM capacity, pixel density (PPI), battery capacity, water resistance, display type, IP rating, and Android OS version. Data preprocessing involved handling missing values with mode imputation, Label Encoding for categoricals, and stratified 80:20 train-test split to preserve class balance. The model achieved 77.16% accuracy and a weighted F1-score of 0.7724 on test data, with 5-fold cross-validation yielding a mean accuracy of 76.53%.Mutual Information analysis ranked RAM capacity (32.51%), PPI (25.78%), and display type (21.09%) as top features. These findings highlight key specs for differentiating rating groups, aiding product analysis, market positioning, and smartphone marketing in Indonesia.

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