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Penerapan Teknik Soft Voting Ensemble pada Klasifikasi Rating Film
Abstract
Pertumbuhan jumlah penonton film yang begitu pesat mendorong industri perfilman untuk terus berinovasi, sehingga menghasilkan beragam judul baru dengan genre dan karakteristik yang semakin bervariasi. Kondisi ini menyebabkan kompleksitas data yang tinggi, sehingga dibutuhkan metode klasifikasi yang efektif dan akurat untuk mengelompokkan rating film berdasarkan karakteristiknya. Penelitian ini bertujuan untuk meningkatkan kinerja klasifikasi rating film dengan menggunakan metode Ensemble Soft Voting, yang menggabungkan tiga algoritma klasifikasi, yaitu K-Nearest Neighbor (KNN), Decision Tree (DT), dan Support Vector Machine (SVM). Evaluasi dilakukan dengan membandingkan kinerja metode Soft Voting terhadap masing-masing metode individu berdasarkan metrik akurasi, presisi, sensitivitas, dan F1-score. Hasil penelitian menunjukkan bahwa metode Soft Voting memberikan kinerja klasifikasi yang lebih baik dibandingkan metode KNN, Decision Tree, dan SVM secara terpisah, dengan capaian akurasi sebesar 89,64%, presisi 85,63%, sensitivitas 89,64%, dan nilai F1-score sebesar 86,52%.
Kata kunci: klasifikasi, Ensemble Learning, KNN, Decision Tree, SVM
The rapid growth in the number of movie viewers has driven the film industry to continuously innovate, resulting in a diverse range of new titles with increasingly varied genres and characteristics. This has led to significant data complexity, necessitating an effective and accurate classification method to categorize movie ratings based on their characteristics. This study aims to evaluate the performance of the Soft Voting ensemble method in classifying movie ratings. The classification results from Soft Voting are compared to those of individual models, namely K-Nearest Neighbor (KNN), Decision Tree (DT), and Support Vector Machine (SVM). The evaluation process was carried out by training and testing the models five times using different random splits of the dataset. Based on the results obtained, Soft Voting consistently demonstrates better accuracy than the individual classifiers. These findings indicate that the ensemble approach is more effective and reliable in improving classification performance in the context of movie rating prediction.
Keywords: classification, ensemble learning, KNN, decision tree, SVM
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