Analisis Sentimen Komentar YouTube Clash of Champions Ruangguru: Pendekatan Multinomial Logistic Regression dan Pemodelan Topik LDA
DOI:
https://doi.org/10.26418/justin.v14i2.97147Keywords:
Analisis Sentimen, Multinomial Logistic Regression, Clash of Champions, Pemodelan Topik, Latent Dirichlet AllocationAbstract
Konten edukatif umumnya memiliki tingkat konsumsi yang lebih rendah dibandingkan konten hiburan di YouTube Indonesia. Namun, program Clash of Champions produksi Ruangguru menunjukkan bahwa pendekatan edutainment mampu menarik perhatian publik secara luas. Penelitian ini bertujuan mengevaluasi pengaruh penyeimbangan data dan reduksi fitur berbasis PCA terhadap kinerja Multinomial Logistic Regression (MLR) dengan representasi TF-IDF, serta mengidentifikasi pola tematik komentar melalui pemodelan topik Latent Dirichlet Allocation (LDA). Hasil penelitian menunjukkan bahwa peningkatan deteksi sentimen negatif terutama dipengaruhi oleh strategi penyeimbangan data, sedangkan PCA tidak memberikan peningkatan signifikan secara independen namun berperan menjaga stabilitas dan efisiensi model. Kombinasi MLR dengan SMOTE dan PCA pada rasio data latih–uji 90:10 menghasilkan akurasi sebesar 0,75 dan F1-score sebesar 0,53 (negatif), 0,69 (netral), dan 0,85 (positif), menunjukkan performa paling seimbang dalam mengenali seluruh kelas sentimen. Pemodelan topik menghasilkan coherence score sebesar 0,5719 (positif) dan 0,4050 (negatif), mengindikasikan bahwa komentar positif cenderung terpusat pada beberapa tema utama, sementara komentar negatif lebih beragam dan mencerminkan isu yang spesifik.References
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