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Zahara, Amalia
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Klasifikasi Stingless Bee Menggunakan Metode Image Classification Berbasis OpenCV Zulfachmi, Zulfachmi; Zahara, Amalia; Hardinata, Danil
Jurnal Bangkit Indonesia Vol 13 No 2 (2024): Bulan Oktober 2024
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v13i2.321

Abstract

Stingless bees play an important role as natural pollinators in ecosystems and as producers of economically valuable products such as honey, propolis, and bee bread, which are utilized in the food and health industries. Identifying stingless bee species remains a challenge due to the many species with similar morphology, requiring more efficient and accurate methods. This study aims to develop an automatic system based on image processing technology for the identification of stingless bee species using Convolutional Neural Networks (CNN), TensorFlow, and the Single Shot MultiBox Detector (SSD) implemented with OpenCV. The test results showed that the developed system was capable of automatically detecting and classifying stingless bee species with an average accuracy of 98%, especially when the object was directly aligned with the camera. Out of 40 tested samples, 31 samples were recognized, and 9 samples were not, resulting in a success rate of 77.5%. Factors influencing detection success include the quality of training data, camera positioning, and morphological similarities between species.
Co-Authors Hardinata, Danil Zulfachmi, Zulfachmi