PENERAPAN ADABOOST UNTUK MENINGKATKAN AKURASI NAIVE BAYES PADA PREDIKSI PENDAPATAN PENJUALAN FILM
For economists and financial experts predicting the success of doing business is very interesting. With the data analytics the prediction process has been facilitated by the past data stored to find out what will happen in the future. This research was conducted to facilitate the film industry players in considering the factors that can influence the income of the film to be produced. The naive bayes method is a popular machine learning technique for classification because it is very simple, efficient, and has good performance on many domains. But naive bayes has a disadvantage that is very sensitive to too many features, thus making the accuracy to be low, in this case the adaboost method to reduce bias so that it can and improve accuracy from naive bayes. Validation is done by using 10 fold cross validation while measuring accuracy using confusion matrix and kappa. The results showed an increase in the accuracy of Naive Bayes from 83.22% to 84.44% and the kappa value from 0.706 to 0.731. So that it can be concluded that the application of adaboost on 2014 & 2015 CSM film data is able to improve the accuracy of the Naive Bayes algorithm
Ahmed, M., Jahangir, M., Afzal, H., Majeed, A., & Siddiqi, I. (2015). Using crowd-source based features from social media and conventional features to predict the movies popularity. Proceedings - 2015 IEEE International Conference on Ahmed, Mehreen Jahangir, Maham Afzal, Hammad Majeed, Awais Siddiqi, ImranSmart City, SmartCity 2015, Held Jointly with 8th IEEE International Conference on Social Computing and Networking, SocialCom 2015, (December 2015), 273–278. https://doi.org/10.1109/SmartCity.2015.83
Gorunescu, F. (2011). Data Mining: Concepts, Models and Techniques. Data mining - Concepts, Models and Technique. https://doi.org/10.1007/978-3-642-19721-5
Mittal, P., & Gill, N. S. (2014). a Comparative Analysis of Classification Techniques on Medical Data Sets, 454–460.
Nugroho, Y. S., & Pratiwi, R. W. (2016). Prediksi Rating Film Menggunakan Metode Naïve Bayes. Jurnal Teknik Elektro (ISSN 1411-0059), 8(2), 60–63.
Nurlaela, D. (2020). LAPORAN AKHIR PENELITIAN PDY : PENERAPAN ADABOOST UNTUK MENINGKATKAN AKURASI NAIVE BAYES PADA PREDIKSI PENDAPATAN PENJUALAN FILM. Bogor.
Tsou, B. K., & Ma, M. (2011). Aspect Based Opinion Polling from Customer Reviews. IEEE Transactions on Affective Computing, 2(1), 37–49. https://doi.org/10.1109/T-AFFC.2011.2
Abstract viewed = 59 times
PDF downloaded = 45 times