Transfer Learning-Based Classification of Herbal Plants for Biodiversity Conservation - Samsudin, Angga Radlisa; Asroni, Ondi (Universitas Dharma Wacana, 2025)
Indonesia's status as a megabiodiversity country is threatened by land degradation, endangering its rich herbal plant species. The identification of these plants, crucial for ecological balance and traditional medicine, remains reliant on slow and subjective manual methods. This research addresses this problem by designing an automated, accurate, and accessible classification system for Indonesian herbal plants. The primary objective was to develop and evaluate a deep learning model based on transfer learning with the EfficientNet-B0 architecture, optimized for deployment on mobile devices. The methodology involved curating a dataset of 4,500 images across 15 species from Lombok, validated by botanists. The model was trained using a 70:15:15 data split, with extensive data augmentation (rotation, flipping, zooming) to improve generalization. Experimental results demonstrated that the proposed EfficientNet-B0 model achieved a high classification accuracy of 92.4% and an F1-score of 91.7%, outperforming baseline models like MobileNetV2 and ResNet50. The model was successfully converted to TensorFlow Lite and integrated into an Android application, which facilitates real-time identification with an average inference time of less than one second. The study concludes that the implemented system provides a robust tool for biodiversity conservation and community health initiatives. The main contribution lies in the creation of a specialized dataset, the optimization of EfficientNet-B0 for local herbs, and the development of a functional mobile application for real-world use.