Prediksi Rating Film IMDb Menggunakan Decision Tree

Penulis

  • Rifqy Rosdiyah Ilmi Universitas Negeri Maulana Malik Ibrahim, Malang
  • Fachrul Kurniawan Universitas Negeri Maulana Malik Ibrahim, Malang
  • Sri Harini Universitas Negeri Maulana Malik Ibrahim, Malang

DOI:

https://doi.org/10.25126/jtiik.20241046615

Abstrak

Industri Film bukan hanya industri atau pusat hiburan semata melainkan menjadi pusat bisnis global. Popularitas atau kesuksesan film box office  selalu menjadi perhatian di seluruh dunia. Data kesuksesan atau popularitas film saat ini tersedia secara online. IMDb merupakan satu dari sekian situs daring penyedia informasi yang berkaitan dengan film, acara televisi, yang meliputi sinopsis, daftar pemain, ulasan penilaian, dan tentunya pemberian rating film. Keberhasilan film dapat ditandai dengan perolehan rating yang tinggi. Prediksi rating film menjadi topik menarik untuk menilai keberhasilan film baik yang telah diproduksi maupun yang belum diproduksi. Pada penelitian ini, dilakukan prediksi nilai rating film menggunakan metode decision tree. Hasil dari penelitian ini diperoleh kesimpulan bahwa atribut popularitas film dan nilai vote user pada laman IMDb berpengaruh terhadap nilai rating film. Nilai akurasi penggunaan model decision tree pada data training, validasi dan testing bertuturt – turut adalah 0,7529, 0,7237 dan 0,7079.

 

Abstract

The film industry is not just an industry or entertainment but also a global business center. The popularity or success of box office movies has always been a concern around the world. Data on the success or popularity of a movie is currently available online. IMDb is one of the many online sites that provide information related to movies, television shows, which include synopsis, cast lists, rating reviews, and of course movie rating assignments. Prediction of movie ratings is an interesting topic to assess the success of films that have been produced and those that have not been produced. Prediction of movie ratings values can be modeled through machine learning using the decision tree model. From this research, it can be concluded that the popularity of the film and the value of user votes on the IMDb page have an effect on the film rating value. The accuracy values of using the descision tree model in training data, validation and testing are respectively 0.7529, 0.7237 and 0.7079.

Downloads

Download data is not yet available.

Referensi

ABARJA, R.A. DAN WIBOWO, A., 2020. Movie rating prediction using convolutional neural network based on historical values. International Journal of Emerging Trends in Engineering Research, 8(5), hal.2156–2164. https://doi.org/10.30534/ijeter/2020/109852020.

BRISTI, W.R., ZAMAN, Z. DAN SULTANA, N., 2019. Predicting IMDb Rating of Movies by Machine Learning Techniques. 2019 10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, (May 2020). https://doi.org/10.1109/ICCCNT45670.2019.8944604.

CHAKRABORTY, P., ZAHIDUR, M. DAN RAHMAN, S., 2019. Movie Success Prediction using Historical and Current Data Mining. International Journal of Computer Applications, 178(47), hal.1–5. https://doi.org/10.5120/ijca2019919415.

DIXIT, P., HUSSAIN, S. DAN SINGH, G., 2020. Predicting the IMDB rating by using EDA and machine learning Algorithms. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, (August), hal.441–446. https://doi.org/10.32628/cseit206481.

FIKIR, O.B., YAZ, I.O. DAN ÖZYER, T., 2010. A movie rating prediction algorithm with collaborative filtering. Proceedings - 2010 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2010, hal.321–325. https://doi.org/10.1109/ASONAM.2010.6.

GAIKAR, D., SOLANKI, R., SHINDE, H., PHAPALE, P. DAN PANDEY, I., 2019. Movie Success Prediction Using Popularity Factor from Social Media. International Research Journal of Engineering and Technology, (daring) 6(4), hal.5185–5190. Tersedia pada: .

HURTADO, R., BOBADILLA, J., BOJORQUE, R., ORTEGA, F. DAN LI, X., 2019. A new recommendation approach based on probabilistic soft clustering methods: A scientific documentation case study. IEEE Access, 7, hal.7522–7534. https://doi.org/10.1109/ACCESS.2018.2890079.

KASIH, P., 2019. Pemodelan Data Mining Decision Tree Dengan Kelompokification Error Untuk Seleksi Calon Anggota Tim Paduan Suara. Innovation in Research of Informatics (INNOVATICS), 1(2), hal.63–69. https://doi.org/10.37058/innovatics.v1i2.918.

MHOWWALA, Z., SULTHANA, A.R. DAN SHETTY, S.D., 2020. Movie rating prediction using ensemble learning algorithms. International Journal of Advanced Computer Science and Applications, 11(8), hal.383–388. https://doi.org/10.14569/IJACSA.2020.0110849.

VAHIDI FARASHAH, M., ETEBARIAN, A., AZMI, R. DAN EBRAHIMZADEH DASTJERDI, R., 2021. A hybrid recommender system based-on link prediction for movie baskets analysis. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00422-0.

DEL VECCHIO, M., KHARLAMOV, A., PARRY, G. DAN POGREBNA, G., 2021. Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries. Journal of the Operational Research Society, 72(5), hal.1110–1137. https://doi.org/10.1080/01605682.2019.1705194.

WIDIYANINGTYAS, T., HIDAYAH, I. DAN ADJI, T.B., 2021. User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system. Journal of Big Data, (daring) 8(1). https://doi.org/10.1186/s40537-021-00425-x.

ZHANG, R. DAN MAO, Y., 2019. Movie Recommendation via Markovian Factorization of Matrix Processes. IEEE Access, 7, hal.13189–13199. https://doi.org/10.1109/ACCESS.2019.2892289.

Diterbitkan

30-08-2023

Terbitan

Bagian

Ilmu Komputer

Cara Mengutip

Prediksi Rating Film IMDb Menggunakan Decision Tree. (2023). Jurnal Teknologi Informasi Dan Ilmu Komputer, 10(4), 791-798. https://doi.org/10.25126/jtiik.20241046615