Implementation Of Convolutional Neural Network Method For Classification Of Baum Test
Irvan Setiawan, Tristyanti Yusnitasari, Hafizh Nurhady, Noor Vika Hizviani · 2020 Fifth International Conference on Informatics and Computing (ICIC) · 2020
One of the conditions for hiring workers in Indonesian companies is to pass a psychology test. Psychology test has many types of tests, one of which is the Baum test, a tree-drawing test. Baum test requires participants to draw a tree following the imagination of the participants. Later, the picture of the participant tree obtained from the Baum test will be assessed by psychologists as one of the labor assessment tools. This assessment is based on the shape of the tree crown, leaf shape, tree size on paper, etc. Currently, the assessment of the Baum test is still done manually by a psychologist. In order to help psychologists in assessing the Baum test, we propose an application in the form of a model that can study and classify tree images based on the tree size in the image by using the Convolutional Neural Network (CNN) method. CNN method allows computers to classify an object. The data are provided in the form of digital images of tree images from Baum testing that has been conducted. The data that has been collected will be used to train the model. The model used in this study has an architecture in the form of a convolution layer with a kernel size of 3×3, a pooling layer with a parameter size of 2×2, a dropout layer, a flatten layer, and a dense layer. The model was trained with 60 epochs and had a test and training data scale of 70:30. The model in this research has an accuracy of 74.07% in predetermined data testing and has an accuracy of 75% in new data testing.