Machine Learning-Driven Seaweed Genera Identification on a Web Application Using Teachable Machine - Azhar, Riza Nur; Yudiati, Ervia; Sedjati, Sri (Jurnal Perikanan Universitas Gadjah Mada, 2024)
This study aims to evaluate the use of machine learning technology, particularly Teachable Machine, in identifying seaweed genus in Indonesia. The limitations of identification databases and the lack of previous research were the main drivers of this study. The research focused on Panjang Island with three representative field data collection stations. This field data became the basis for training machine learning models for identification. In addition to field data, information from the literature on seaweed visual characteristics was also taken to support the identification process. The machine learning model developed achieved 99.42% accuracy in identifying 13 classes of 9 seaweed genus. The implementation of the model on the web application showed satisfactory responsive performance, including in the speed test on Google PageSpeed. Overall, the integration of machine learning technology in a web application platform provides a practical solution for accurate and accessible seaweed identification. This invention has great potential in supporting research, conservation, and sustainable utilization of marine resources in Indonesia.