IMPLEMENTASI ALGORITMA YOLO UNTUK MENDETEKSI JENIS TANAMAN HIAS BERBASIS ANDROID
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Abstract
Ornamental plants possess high aesthetic value and environmental benefits, yet identifying their species often poses a challenge, especially for beginners. This study aims to develop an Android-based application employing the You Only Look Once version 8 (YOLOv8) algorithm to detect ornamental plant species through leaf images in real-time. The dataset comprises 1,096 images of ornamental plant leaves, including snake plant (Sansevieria), aloe vera (Aloe vera), and coral cactus (Cereus peruvianus). The data were annotated using bounding box techniques, and the model was trained on Google Colab with an 80:20 split between training and testing datasets. The training resulted in an accuracy rate of 96% based on the mean Average Precision (mAP) metric. The application was developed using Android Studio with a user-friendly interface, enabling real-time detection on Android devices with a minimum RAM specification of 3 GB. Application testing involved black-box testing to ensure functionality and usability testing with 31 respondents, revealing a user satisfaction rate of 87%. Some challenges encountered included the impact of lighting on detection accuracy and result variability across different devices. This study contributes to the utilization of artificial intelligence technology for biodiversity education and supports environmental conservation efforts
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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