Ai-assisted Web-based Learning Media for Herbal Leaf Identification: Development, Model Evaluation, and Usability Testing
Year: 2026
Type: article
Abstract: The growing use of herbal plants highlights the need for effective learning media that help vocational students identify medicinal plant species more easily. This study aimed to develop an AI-supported, web-based learning tool for medicinal plant leaf identification, evaluate the performance of lightweight Convolutional Neural Network (CNN) models, and assess the tool's feasibility and usability. We used a Research and Development (R&D) approach based on the ADDIE model. We prepared a dataset of 3,100 images across 20 classes to compare four pre-trained lightweight CNN architectures: MobileNetV2, MobileNet, EfficientNet-B0, and EfficientNetV2-B0. The selected model was integrated into the web-based learning tool via a client-server architecture using an Application Programming Interface (API). Model performance was evaluated using accuracy, loss, precision, recall, and F1-score metrics, while feasibility was assessed by media and subject matter experts. 29 vocational students evaluated usability using the System Usability Scale (SUS). EfficientNetV2-B0 achieved the highest average accuracy (98.67%) across three random seeds, and the final model attained 97.25% accuracy on the test set. The tool received feasibility scores of 83.41% from media experts and 96.19% from subject matter experts. The average SUS score was 79.14, indicating good usability from the users' perspective. These findings demonstrate that integrating lightweight CNN models into web-based learning tools can provide a practical resource for learning to identify medicinal plant leaves. The developed tool can support students in both independent and classroom learning, although its broader implementation remains constrained by dataset diversity, device compatibility, internet dependency, and the limited scope of user evaluation.
Language: English
Topic: Multimedia Learning Systems
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W7225292875