Result: Studies from Electronic Engineering Polytechnic Institute of Surabaya Yield New Data on Support Vector Machines (Classification of Intraoral Images in Dental Diagnosis Based on GLCM Feature Extraction Using Support Vector Machine).

The article focuses on the development of an AI-based diagnostic tool aimed at improving the classification of dental conditions and tooth types to enhance dental diagnostics. Conducted by researchers from the Electronic Engineering Polytechnic Institute of Surabaya in East Java, Indonesia, the study utilized a dataset of 3,910 dental images and employed various machine learning algorithms, with the Support Vector Machine (SVM) achieving the highest accuracy. Despite the promising potential of AI in dentistry, the study identified limitations in dataset diversity and feature extraction methods, suggesting that future research should aim to expand the dataset and explore advanced techniques to improve model performance. The findings emphasize the need for more reliable AI tools to enhance patient outcomes and streamline clinical workflows in dentistry. [Extracted from the article]