Analisis Performa Deteksi Cacar Monyet dengan Model Klasifikasi Gambar Menggunakan Teachable Machine dan Keras

Digital Image Detection of Monkeypox Disease Performance Analysis using Image Classification Model with Teachable Machine and Keras. Advancements in artificial intelligence technology, particularly in the field of machine learning, have opened up significant new opportunities for developing disease detection systems that are faster, more accurate, and more efficient. This study aims to analyze the performance of Teachable Machine and Keras models in classifying monkeypox images using a quantitative approach. A dataset of skin images with indications of monkeypox was collected for the development of image classification models using both tools. The results of the study show that the developed models accurately recognized images that had been seen during the training data for the "Monkey Pox" category. However, when faced with test images that had not been seen before, the models showed limitations in generalizing, indicating overfitting in that category. Conversely, for the "Other" category, the models were able to recognize well both in the training data and the test data, demonstrating better generalization capability in this category. Therefore, for future research, it is recommended to conduct a more in-depth evaluation of the use of Data Augmentation techniques to expand data variation, as well as to explore other platforms or tools that can provide greater control over test data management.