Implementasi Arsitektur MobileNetV2 dengan Metode Transfer Learning untuk Identifikasi Objek Wisata Religi - Wijaya, Nabilah Putri; Christioko, Bernadus Very (Building of Informatics, Technology and Science (BITS), 2025)
Semarang has five religious tourism icons that represent pluralism, but their promotion is still conventional and not yet optimal in the digital era. This problem hinders its tourism potential in reaching a wider audience. This study aims to develop an accurate and efficient automatic image identification model as a modern solution to these promotional challenges. The method implemented is deep learning using the MobileNetV2 CNN architecture through a transfer learning approach. MobileNetV2 was chosen because it is superior in computational efficiency on resource-constrained devices compared to other models like EfficientNet. The model was trained and validated using a dataset consisting of a total of 7,500 images comprising five classes of religious tourist attractions, namely Grand Mosque of Central Java, Blenduk Church, Buddhagaya Watugong Temple, Pura Agung Giri Natha Temple, and Sam Poo Kong Temple. The dataset was divided into 70% training data, 15% validation data, and 15% test data. The evaluation results on the test data showed satisfactory performance, where the developed model achieved an overall accuracy of 98%, with a macro average F1-Score of 0.98. This figure indicates high and balanced performance across all classes. Individual testing also proved the model's ability to recognize relevant images with high confidence and reject images outside the class. This success shows that the implementation of MobileNetV2 is effective and can be basic technology for development of innovative digital tourism applications in Semarang.