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All Journal Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI)
Rizqi, Ramadhani Akbaru
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Computer Science & IT Education Other

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KLASIFIKASI IMAGE JENIS UBUR UBUR MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) Ma’arif, Wildan Mufti; Rizqi, Ramadhani Akbaru; Rolliawati, Dwi
Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI) Vol. 6 No. 1 (2025): Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI)
Publisher : Information Technology Education Department

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/jipti.v6i1.2632

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.
Co-Authors Ma’arif, Wildan Mufti Rolliawati, Dwi