KLASIFIKASI IMAGE JENIS UBUR UBUR MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)
Abstract
This study aims to classify jellyfish species based on visual images using the Convolutional Neural Network (CNN) method. The main challenge lies in the morphological similarities between species, making manual identification prone to errors. This research employs a dataset containing images of six jellyfish species: Moon Jellyfish, Blue Jellyfish, Mauve Stinger Jellyfish, Compass Jellyfish, Barrel Jellyfish, and Lion's Mane Jellyfish. The dataset undergoes preprocessing techniques such as normalization, dimension adjustment, and image augmentation.The designed CNN model consists of convolutional and pooling layers to recognize complex visual patterns. Model testing was conducted using validation and test datasets, achieving a classification accuracy of over 90%. These results demonstrate the effectiveness of the CNN method in addressing the challenges of automatic marine species identification.This study is expected to contribute to marine biodiversity conservation and support the development of AI-based technologies for ecosystem management. The implications include broader applications in marine species identification and environmental preservation.
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Keywords:
Convolutional Neural Networks, Deep Learning, Jellyfish Classification : Convolutional Neural Network; Deep Learning; Klasifikasi Ubur-Ubur.
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2025-04-01
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Ma’arif, W. M., Rizqi, R. A., & Rolliawati, D. (2025). KLASIFIKASI IMAGE JENIS UBUR UBUR MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN). Jurnal Inovasi Pendidikan Dan Teknologi Informasi (JIPTI), 6(1), 85–97. https://doi.org/10.52060/jipti.v6i1.2632
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Copyright (c) 2025 Wildan Mufti Ma’arif, Ramadhani Akbaru Rizqi, Dwi Rolliawati
This work is licensed under a Creative Commons Attribution 4.0 International License.
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