Comparison of DenseNet-121 and MobileNet for Coral Reef Classification

Heru Pramono Hadi (Universitas Dian Nusnwantoro, Semarang, Indonesia)
Eko Hari Rachmawanto (Universitas Dian Nusnwantoro, Semarang, Indonesia)
Rabei Raad Ali (Northern Technical University, Mosul, Iraq)

MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Vol 23 No 2 (2024)

Coral reefs are a type of marine organism that has beauty and benefits for other sea creatures’ ecosystems. However, despite its beauty and usefulness, coral reefs are vulnerable to damage such as coral bleaching, which can impact other coral reef ecosystems. This research aims to classify digital images of healthy, bleached, and dead coral reefs. This research method is DenseNet-121 and MobileNet is based on Convolutional Neural Networks. This research uses a dataset from 1582 coral reef image data with three main classes: 720 were bleached, 150 were dead, and 712 were healthy. The testing process is carried out using several forms of split datasets, namely 60:10:30, 50:10:40, and 70:10:20. The test results obtained with a data sharing percentage of 60:10:30 show that MobileNet architecture achieved 88.00% accuracy, and DenseNet-121 achieved 91.57% accuracy. Using a data split percentage of 50:10:40, MobileNet achieved 84.51% accuracy, and DenseNet- 121 achieved 90.52% accuracy. Meanwhile, with a data separation percentage of 70:10:20, MobileNet achieved 85.48% accuracy, and DenseNet-121 achieved 92.74% accuracy.