Animation industry involves huge funds in production process and its success will give great income. Predicting the box-office of animated film has become an interesting topic to be discussed, because past studies are shown to be contradictory. Sharda and Delen conducted a similar study that used seven parameters, i.e. MPAA rating, competition, star value, genre, special effects, sequel and number of screens; and generated pinpoint accuracy (i.e. Bingo) with 36.9% and within one category (1-Away) with 75.2%. The authors proposed new and simple parameters that can be used to predict the success of animated films, i.e. the actors/actress, animation studio, genre, MPAA rating and the sequel of the film. These five parameters are relatively simple because it can be easily collected. In this study, the use of neural networks in predicting the financial performance of 120 animated films from 1995 until 2013 was explored. There are three categories of financial performance that become the class label of this study, they are: low, medium and high. Our prediction result in bingo is 58% and 1-away is 89,7%. By using the simple parameters, this study can reach a better accuracy. It is expected that this prediction can help animation film industry to predict the expected revenue range before its theatrical release.
Exaggeration is one of 12 basic principles of animation. The aim of exaggeration is to increase audience's significance or attention. Unfortunately, exaggeration action can be in the form of violence scenes. So, it may affect the child's psychology and behavior. This research aimed to make prediction system of cartoons which could have negative impact on children by using neural network method. The input parameters of our neural network were scenes duration, duration of exaggeration action, total duration of actions, and quantitative of exaggeration action. The output parameter of our neural network system are G, PG, PSG and R . These labels are MPPA rating value. Our neural network prediction system used three scenarios of parameters input set with back propagation method, the value of epoch: 0, number of epoch: 500, learning rate: 0.3 and momentum: 0.2. These scenarios were qualitative, quantitative and the combination of quantitative-quantitative parameters input set. The accuracy of our prediction system using quantitative parameters input set was 76%, second scenario got 63% accuracy rate and third scenario got 76% accuracy rate. This result showed that the exaggeration scene in an animation film is able to influence the behavior of children despite the scene have short duration.
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