Visualization and Prediction of Film Award Nominations by Using of Visual Data Mining (VDM) and Exploratory Data Analysis (EDA) Method

Film is considered as a historical process of society in the form of a living image. With the Academy Awards International Awards Festival, every filmmaker will strive to produce a quality that can be enjoyed by anyone, regardless of their demographic, therefore creating a trend. This trend may reflect the history of human life, norms and behaviors at that time. The aim of this study is to provide a comprehensive picture by creating a visualization of Best Picture nominees of the 1993 - 2017 Academy Awards. This procedure has never been done before, especially in the form of a dashboard by using Visual Data Mining (VDM) method on Tableau software tools. In contrast to reference studies, the Holt-Winters exponential smoothing method is used to find 2018 predictions from several examined parameter values. Data used in this study were taken from three different sources that are available in the Internet. The result of this study is that 5 different dashboards were created to visualize the trend pattern search for the parameters and the successful application of the Holt-Winters exponential smoothing prediction method. This research was validated by applying the User Acceptance Test (UAT) to 5 respondents.