Classification of Film Genres Based on Synopsis Using Support Vector Machine (SVM) Method with TF-IDF and N-Gram
DOI:
https://doi.org/10.22303/csrid-.18.2.2026.381-394Keywords:
support vectro machine, TFD-IF, N-Gram, Movie Genre Classification, sinopsysAbstract
With the wide variety of film genres available, audiences often face difficulties in determining a movie’s genre solely based on its synopsis. Therefore, an automated system is needed to classify movie genres effectively. This study aims to classify movie genres based on film synopses using the Support Vector Machine (SVM) method combined with TF-IDF and N-Gram feature extraction techniques. The dataset used in this research was obtained from Kaggle and consisted of 10,000 movie records with multi-label characteristics, which were subsequently transformed into Single-label data. Four main genres were selected, namely Action, Comedy, Drama, and Horror, resulting in a final dataset of 6,000 records. The research process included data preparation, text preprocessing, TF-IDF feature extraction using unigram and bigram models, data splitting, and model evaluation. All data processing procedures were carried out in Google Colab using the Python programming language. The evaluation results indicate that the SVM model with a linear kernel achieved an accuracy of 83%. The Horror genre demonstrated the best performance, with a precision value of 91%, recall of 92%, and F1-score of 92%. These findings suggest that the combination of TF-IDF, N-Gram, and SVM provides satisfactory results for movie genre classification based on film synopses.
References
K. Sleman, P. Daerah, and I. Yogyakarta, “SISTEM REKOMENDASI FILM INDONESIA MENGGUNAKAN COLLABORATIVE FILTERING,” vol. 9, no. 5, pp. 9070–9076, 2025.
R. Octavia and A. P. Yuwono, “Analisis Resepsi Penonton terhadap Makna Normalisasi Pelecehan Seksual pada Film Dear David,” vol. 8, no. 1, pp. 11–23, 2024.
A. Firizkiansah, A. Muhammad, I. R. Maulana, U. S. Indonesia, and K. Bekasi, “Optimasi Klasifikasi Data Teks Menggunakan Algoritma Logistic Regression dengan TF-IDF dan SMOTE,” vol. 2, no. 1, pp. 29–36, 2025.
B. Falakhi, I. Cholissodin, and R. S. Perdana, “Klasifikasi Sinopsis Novel berdasarkan Jenis Genre menggunakan Multi- class Support Vector Machine dan Chi-square,” vol. 7, no. 1, pp. 192–202, 2023.
M. Das, S. Kamalanathan, and P. Alphonse, “A Comparative Study on TF-IDF feature Weighting Method and its Analysis using Unstructured Dataset,” vol. 5571, pp. 0–2, 2021.
W. M. Jihadul Akbar, Hairul Fahmi, “MULTI LABEL KLASIFIKASI GENRE FILM BERDASARKAN SINOPSIS MENGGUNAKAN METODE LONG SHORT-TERM MEMORY ( LSTM ),” vol. 8, pp. 110–119, 2025.
A. Anugrah, M. Arif, C. Hamud, J. Zulkaidah, and R. Leo, “Penerapan Text Mining menggunakan Metode TF-IDF untuk Menentukan Genre Film,” vol. 7, no. 3, pp. 415–422, 2025.
N. Arifin, U. Enri, and N. Sulistiyowati, “PENERAPAN ALGORITMA SUPPORT VECTOR MACHINE ( SVM ) DENGAN TF-IDF N-GRAM UNTUK TEXT CLASSIFICATION,” vol. 6, no. 2, 2021.
M. R. Qisthiano, “KLASIFIKASI TERHADAP PREDIKSI KELULUSAN MAHASISWA DENGAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) M,” pp. 2–6, 2022.
A. Shamim, “IMDb Movie Genre Classification Dataset,” kaggle. [Online]. Available: https://www.kaggle.com/datasets/adilshamim8/nlp-task
T. Gori et al., “PREPROCESSING DATA DAN KLASIFIKASI UNTUK PREDIKSI KINERJA DATA PREPROCESSING AND CLASSIFICATION FOR PREDICTING STUDENT,” vol. 11, no. 1, pp. 215–224, 2024, doi: 10.25126/jtiik.20241118074.
D. Lestari and S. Lestari, “Penerapan Data Mining Klasifikasi Tingkat Pemahaman Siswa Pada Kegiatan Belajar Mengajar dengan Metode Decision Tree (Studi Kasus SDN Malaka Jaya 11 Duren Sawit),” vol. 5, no. 2, pp. 1260–1268, 2024.
M. Liu et al., “Handling missing values in healthcare data : A systematic review of deep learning-based imputation techniques,” pp. 1–34, 2022.
A. Ramadhani and N. S. Harahap, “Performance Comparison of Multilabel Text Classification Methods on Translated Hadiths of Bukhari Using Support Vector Machine and Long Short Term Memory Perbandingan Performa Metode Klasifikasi Teks Multilabel Hadis Terjemahan Bukhari Menggunakan Support,” vol. 5, no. July, pp. 896–907, 2025.
C. Kaope and Y. Pristyanto, “The Effect of Class Imbalance Handling on Datasets Toward Classification Algorithm Performance,” vol. 22, no. 2, pp. 227–238, 2023, doi: 10.30812/matrik.v22i2.2515.
Z. Abidin, T. Suratno, and M. F. Putri, “PENERAPAN RANDOM OVERSAMPLING DAN PRINCIPAL COMPONENT ANALYSIS UNTUK MENINGKATKAN AKURASI PREDIKSI KEBANGKRUTAN APPLICATION OF RANDOM OVERSAMPLING AND PRINCIPAL COMPONENT ANALYSIS TO ENHANCE THE ACCURACY OF BANKRUPTCY PREDICTION FOR,” vol. 12, no. 5, 2025.
H. Rifaldi, Dianda, Abdul Fadlil, “TEKNIK PREPROCESSING PADA TEXT MINING MENGGUNAKAN DATA TWEET ‘MENTAL HEALTH,’” Decod. J. Pendidik. Teknol. Inf., vol. 3, no. 2, pp. 161–171, 2023.
R. R. Salam, M. F. Jamil, and Y. Ibrahim, “Sentiment Analysis of Cash Direct Assistance Distribution for Fuel Oil Using Support Vector Machine Analisis Sentimen Terhadap Bantuan Langsung Tunai ( BLT ) Bahan Bakar Minyak ( BBM ) Menggunakan Support Vector Machine,” vol. 3, no. April, pp. 27–35, 2023.
M. Amien, “SEJARAH DAN PERKEMBANGAN TEKNIK NATURAL LANGUAGE PROCESSING (NLP) BAHASA INDONESIA: TINJAUAN TENTANG SEJARAH, PERKEMBANGAN TEKNOLOGI, DAN APLIKASI NLP DALAM BAHASA INDONESIA,” no. 2007, pp. 1–7, 2022.
A. Nur, A. Saputra, R. E. Saputro, D. Intan, and S. Saputra, “Enhancing Sentiment Analysis Accuracy Using SVM and Slang Word Normalization on YouTube Comments,” vol. 9, no. 2, pp. 687–699, 2025.
S. J. Angelina, A. Bijaksana, P. Negara, and H. Muhardi, “Analisis Pengaruh Penerapan Stopword Removal Pada Performa Klasifikasi Sentimen Tweet Bahasa Indonesia Analyzing The Impact Of Applying Stopword Removal On Indonesian Tweet Sentiment Classification,” vol. 02, no. 1, pp. 165–173, 2023, doi: 10.26418/juara.v2i1.69680.
R. Hafeez et al., “Contextual Urdu Lemmatization using Recurrent Neural Network Models,” pp. 1–20, 2023.
D. Septiani, I. Isabela, F. Teknik, U. N. Jakarta, F. Teknik, and U. N. Jakarta, “Analisis term frequency inverse document frequency (tf-idf) dalam temu kembali informasi pada dokumen teks,” vol. 25, pp. 81–88.
K. Hadi and E. Utami, “Analysis of K-NN with the Integration of Bag of Words , TF-IDF , and N-Grams for Hate Speech Classification on Twitter,” vol. 12, no. 2, pp. 289–298, 2024.
N. H. Ovirianti, M. Zarlis, and H. Mawengkang, “Support Vector Machine Using A Classification Algorithm,” vol. 6, no. 3, pp. 2103–2107, 2022.
S. Dewi, “Arus Jurnal Sains dan Teknologi ( AJST ) Analisis Sentimen Aplikasi WhatsApp berdasarkan Ulasan di,” vol. 2, no. 2, 2024.
S. Sathyanarayanan and B. R. Tantri, “Confusion Matrix-Based Performance Evaluation Metrics,” vol. 27, no. 4, 2024.
A. Indrajaya, “PERBANDINGAN KLASIFIKASI KACANG KERING DENGAN ALGORITMA KNN DAN SVM LINEAR,” vol. 20, no. 1, pp. 62–70, 2025.
