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All Journal Indonesian Journal of Multidisciplinary on Social and Technology
Akbar Ainurrofik
Universitas Muhammadiyah Jember
Computer Science & IT Economics, Econometrics & Finance Engineering Social Sciences

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Klasifikasi Smartphone Berdasarkan Spesifikasi Menggunakan Decision Tree C4.5 dan Random Forest dengan Hyperparameter Tuning Akbar Ainurrofik; Ilham Saifudin; Rosita Yanuarti
Indonesian Journal of Multidisciplinary on Social and Technology Vol. 4 No. 3 (2026): Juli - Oktober
Publisher : PT Ilmu Data Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijmst.v4i3.13271

Abstract

The rapid growth of smartphone products has produced substantial variation in technical specifications and price segments, making manual grouping increasingly complex. This study identifies the most informative specification using the C4.5 Decision Tree and develops a smartphone price-segment classification model using Random Forest optimized through hyperparameter tuning. The dataset contains 3,260 smartphone records and six input features: RAM capacity, internal storage, number of processor cores, battery capacity, main-camera resolution, and screen size. Prices in Indian rupees were converted into Indonesian rupiah and used only to generate three class labels: Entry-Level, Mid-Range, and Flagship; price was excluded from model inputs to prevent data leakage. The dataset was divided using a stratified 80:20 split. C4.5 analysis identified internal storage as the root node at a threshold of 192 GB with a Gain Ratio of 0.3804. The baseline Random Forest achieved 85.12% accuracy, 82.92% macro precision, 80.81% macro recall, and 81.77% macro F1-score. GridSearchCV with five-fold StratifiedKFold selected 100 trees, max_depth 20, min_samples_split 2, min_samples_leaf 1, max_features sqrt, and bootstrap=True. The tuned model maintained 85.12% accuracy while improving macro precision to 83.44%, macro recall to 81.03%, and macro F1-score to 82.15%. The tuned model was selected because it produced a better balance across classes and was subsequently implemented in a web-based classification system.
Co-Authors Saifudin, Ilham Yanuarti, Rosita