Analisis Sentimen Terhadap Review Film Menggunakan Metode Modified Balanced Random Forest dan Mutual Information

 (*)Firdausi Nuzula Zamzami Mail (Universitas Telkom, Bandung, Indonesia)
 Adiwijaya Adiwijaya (Universitas Telkom, Bandung, Indonesia)
 Mahendra Dwifebri P (Universitas Telkom, Bandung, Indonesia)

(*) Corresponding Author



Information exchange is currently the most happening on the internet. Information exchange can be done in many ways, such as expressing expressions on social media. One of them is reviewing a film. When someone reviews a film he will use his emotions to express their feelings, it can be positive or negative. The fast growth of the internet has made information more diverse, plentiful and unstructured. Sentiment analysis can handle this, because sentiment analysis is a classification process to understand opinions, interactions, and emotions of a document or text that is carried out automatically by a computer system. One suitable machine learning method is the Modified Balanced Random Forest. To deal with the various data, the feature selection used is Mutual Information. With these two methods, the system is able to produce an accuracy value of 79% and F1-scores value of 75%.


Modified Balanced Random Forest; Mutual Information; Sentiment Analysis; Movie Review; Classification

Full Text:


Article Metrics

Abstract view : 247 times
PDF - 124 times


Shivaprasad, T.K., Shetty, J. (2017). “ Sentiment Analysis of Product Reviews: A Review ”. International Conference on Inventive Communication and Computational Technologies (ICICCT 2017). 298-303

Trivedi, S.K., Tripathi, A. (2016). “Sentiment Analyis of Indian Movie Review with Various Feature Selection Techniques”. 2016 IEEE International Conference on Advances in Computer Applications (ICACA). 181-185.

Amolik, A., Jivane, N., Bandhari, M., Venkatesan. (2016). “Twitter Sentiment Analysis of Movie Reviews using Machine Learning Techniques”. International Journal of Engineering and Technology (IJET). 7(6). 2038-2043.

Zainuddin, N., Selamat, A.(2014). “Sentiment Analysis Using Support Vector Machine”. 2014 International Conference on Computer, Communications, and Control Technology (I4CT). 333-337.

Sharma, A., Dey, S. (2012). “An Artificial Neural Network Based Approach for Sentiment Analysis of Opinionated Text”. Proceedings of the 2012 ACM Research in Applied Computation Symposium. 37-42.

Parmar, H., Bhanderi, S., Shah, G. (2014). “ Sentiment Mining of Movie Reviews using Random Forest with Tuned Hyperparameters “. International Conference on Information Science. 1-6.

Pratiwi, A.I., Adiwijaya. (2018). “On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis”. Hindawi Applied Computational Intelligence and Soft Computing Volume 2018. 1-5.

Hedge, Y., Padma, S.K. (2017). “ Sentiment Analysis using Random Forest Ensemble for Mobile Product Reviews in Kannada”. 2017 IEEE 7th International Advance Computing Conference. 777-782.

Mubarok, M.S., Adiwijaya, Aldhi, M.D. (2017). “Aspect-based sentiment analysis to review products using Naïve Bayes”. International Conference on Mathematics: Pure, Applied and Computation: Empowering Engineering using Mathematics. 1-8.

Ahmad, I.S., Bakar, A.A., Yaakub, M.R. (2019). “ A review of feature selection in sentiment analysis using information gain and domain specific ontology ”. International Journal of Advanced Computer Research. 9(44). 283-292

Mediamer, G., Adiwijaya, Faraby, S.A. (2019). “Development of Rule-Based Feature Extraction in Multi-label Text Classification”. International Journal on Advanced Science, Engineering and Information Technology. 9(4). 1460-1465

Bakar, M.Y.A., Adiwijaya, Faraby, S.A. (2018). “Multi-Label Topic Classification of Hadith of Bukhari (Indonesian Language Translation)Using Information Gain and Backpropagation Neural Network”. 2018 International Conference on Asian Language Processing (IALP). 344-350

Suyanto. 2018. Machine Learning Tingkat Dasar dan Lanjut. Bandung: Informatika Bandung.

Agusta, P.A., Adiwijaya. (2019). “Modified balanced random forest for improving imbalanced data prediction”. International Journal of Advances in Intelligent Informatics. 5(1). 58-65.

Ulfa, M.A., Irmawati, B., Husodo, A.Y. (2018). “Twitter Sentiment Analysis using Naïve Bayes Classifier with Mutual Information Feature Selection”. Journal of Computer Science and Informatics Engineering. 2(2). 106-111

Kotsiantis, S., Pintelas, P.E. (2005). “Handling imbalanced datasets: A review”. International Transactions on Computer Science and Engineering. 30

Jones, G., Xu, Y., Li, J., Wang, B., Sun, C. (2007). “A Study on Mutual Information-based Feature Selection for Text Categorization”. Joutnal of Computational Information Systems. 1007 – 1011.

M. D. Purbolaksono, F.D. Reskyadita, Adiwijaya,A. A. Suryani and A. F. Huda,"Indonesian Text Classification using Back Propagation and Sastrawi Stemming Analysis with Information Gain for Selection Feature," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 1, pp. 234-238, 2020.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Analisis Sentimen Terhadap Review Film Menggunakan Metode Modified Balanced Random Forest dan Mutual Information


  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email :

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.