Deteksi Objek Berbasis Deep learning untuk Identifikasi Spesimen Biologi dalam Pembelajaran Biologi - Noor Hujjatusnaini (Jurnal Pendidikan Indonesia, 2026)
This study aimed to develop a deep learning–based object detection system to assist the identification of biological specimens in biology learning. The research employed the ADDIE development model, which consists of analysis, design, development, implementation, and evaluation stages. During the development stage, the object detection model was trained using a dataset of biological specimen images that had undergone preprocessing and data annotation. The results showed that the model achieved good performance with an accuracy of 92.4%. The implementation of the system was conducted through a limited trial involving 30 students in biology learning activities. The results indicated that the use of the system improved students’ ability to identify biological specimens, with identification accuracy increasing from 62% before using the system to 85% after using the system. In addition, questionnaire results revealed positive student responses toward the system, particularly in terms of ease of use, clarity of object information, and interest in technology-based learning. Expert validation by media and subject matter experts also indicated that the developed system was categorized as highly feasible for use as a learning medium. Therefore, the deep learning–based object detection system has the potential to serve as an innovative learning medium to support biological specimen identification in biology education.