Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal ICT : Information Communication & Technology
Muhammad Edwin
Box Office Movie Indonesia
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Backpropagation Neural Network Untuk Prediksi Kesuksesan Film Secara Finansial Ani Rahmani; Muhammad Edwin
Jurnal ICT : Information Communication & Technology Vol 19, No 1 (2020): JICT-IKMI, Juli 2020
Publisher : STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36054/jict-ikmi.v19i1.145

Abstract

A film is one of the entertainment media in human life. It makes the film industry becomes a potential business to get promising income. Prediction of success of a film financially supports film producers, production houses, distributors, and stakeholders in making decisions. The existing research on film predictions is from a popularity point of view. There is also research to predict success from a financial point of view, but using social media film rating data, which has been released. This paper is a study to find a predictive model for the success of films from the financial aspect of films that have not been released. The prediction model uses the Backpropgation neural network (BNN) algorithm by calculating the accuracy, precision, recall, and f-score. The observed variables  are a combination of film success parameters, namely, budget, genre, production company, holiday, runtime, and competition factor. The study results show that the use of more recent data, although less data, can produce better accuracy values, compared to using more data, films released in older years. From a financial point of view, the difference in the year the film was released, especially if it was tens of years, significantly affects the prediction results. Finally, the number of hidden layers and hidden nodes has a significant non-linear role in determining the prediction results.
Co-Authors Ani Rahmani