Implementasi Klasifikasi Genre Film Pada Platform Imdb Berdasarkan Deskripsi Sinopsis Film Menggunakan Algoritma Naïve Bayes, SVM, Dan Logistic Regression
DOI:
https://doi.org/10.69693/ijmst.v4i3.12210Keywords:
Natural Language Processing, Klasifikasi Genre Film, Machine Learning, IMDb, Sinopsis Film, Naïve Bayes, Support Vector Machine, Logistic RegressionAbstract
Pertumbuhan data film digital dan meningkatnya penggunaan platform IMDb menuntut metode otomatis yang mampu mengelompokkan genre berdasarkan informasi tekstual secara konsisten. Penelitian ini bertujuan mengimplementasikan Natural Language Processing dan membandingkan kinerja Multinomial Naïve Bayes, Support Vector Machine LinearSVC, SVM dengan pembobotan kelas seimbang, serta Logistic Regression untuk mengklasifikasikan genre primer film berdasarkan sinopsis. Dataset IMDb Top 1000 dari Kaggle diseleksi menjadi 811 film pada lima genre utama, yaitu Drama, Action, Comedy, Crime, dan Biography. Tahapan penelitian meliputi case folding, cleaning, tokenization, stopword removal, stemming, ekstraksi fitur Term Frequency-Inverse Document Frequency, pembagian data secara stratified 80:20, pelatihan model, evaluasi accuracy, precision, recall, F1-score, confusion matrix, dan validasi silang stratified lima lipatan. Baseline kelas mayoritas menghasilkan akurasi 35,6%. Logistic Regression memberikan hasil terbaik dengan akurasi pengujian 43,56% dan rata-rata validasi silang 44,63%. SVM Balanced mencapai akurasi 42,33%, LinearSVC 41,10%, dan Naïve Bayes 40,49%. Naïve Bayes menunjukkan bias kuat terhadap kelas Drama, sedangkan SVM dan Logistic Regression menghasilkan prediksi lebih seimbang. Kinerja yang masih terbatas terutama dipengaruhi ketidakseimbangan kelas, tumpang tindih naratif antargenre, sinopsis yang pendek, dan penyederhanaan label multigenre menjadi genre primer. Hasil penelitian menegaskan bahwa Logistic Regression merupakan model klasik paling efektif pada konfigurasi data dan fitur yang digunakan serta layak dijadikan baseline untuk penelitian lanjutan.
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