AI-BASED CLASSIFICATION OF SCHOOL STUDENT NEATNESS USING CONVOLUTIONAL NEURAL NETWORK (CNN)
DOI:
https://doi.org/10.33480/jitk.v11i4.7182Keywords:
Convolutional Neural Networks, Discipline, Image Classification, Scout Uniforms, Student NeatnessAbstract
The neatness of students in Scouting uniforms is a form of implementation of discipline that reflects compliance with school rules. At SDIT Ajimutu Global Insani, the uniform attributes assessed include the completeness of the Scout uniform. The neatness in wearing these attributes greatly supports the creation of an orderly and conducive learning environment. This study aims to classify the level of neatness of Scouting uniforms automatically using an artificial intelligence-based approach. The data used are in the form of student images recorded using a Canon EOS 60D DSLR camera, with a total data of 510 images, consisting of 88 female students and 82 male students. The method used in this study is Convolutional Neural Network (CNN) with a transfer learning approach using the MobileNetV2 architecture with Transfer Learning and data augmentation techniques to improve model accuracy. The system was developed to classify uniform neatness into two categories: neat and untidy. The test results show that the model is able to classify with an accuracy level of 73%, with precision, recall, and f1-score having the same results with an accuracy of 73%. These findings indicate that the developed system can help teachers and schools in evaluating student discipline objectively and continuously. Thus, this study contributes to improving the quality of education through the habituation of orderly behavior integrated with technology.
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