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All Journal Dinamik TELKOMNIKA (Telecommunication Computing Electronics and Control) JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Abdimasku : Jurnal Pengabdian Masyarakat SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Hadi, Heru Pramono
Universitas Dian Nuswantoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Comparison of DenseNet-121 and MobileNet for Coral Reef Classification Heru Pramono Hadi; Eko Hari Rachmawanto; Rabei Raad Ali
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3683

Abstract

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.
Comparison of DenseNet-121 and MobileNet for Coral Reef Classification Hadi, Heru Pramono; Rachmawanto, Eko Hari; Ali, Rabei Raad
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3683

Abstract

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.
KLASIFIKASI TERUMBU KARANG MENGGUNAKAN CNN MOBILENET Hadi, Heru Pramono; Rachmawanto, Eko Hari; Sari, Christy Atika
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 8, No 01 (2024): SEMNAS RISTEK 2024
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v8i01.7177

Abstract

Terumbu karang merupakan bagian dari ekosistem laut yang indah, namun dibalik keindahan tersebut, terumbu karang juga rentan akan kerusakan ekosistem yang terjadi, yang dimana dapat disebabkan oleh terumbu karang rentan mengalami pemutihan oleh aktivitas yang terjadi di sekitar ekosistem terumbu karang tersebut. Oleh karena itu, diperlukan proses klasifikasi atau pemilahan antara terumbu karang yang terkena pemutihan, sehat ataupun mati sehingga dapat diambil suatu tindakan konservatif yang tidak merusak ekosistem terumbu karang tersebut. Pada penelitian ini, akan dilakukan proses klasifikasi terumbu karang dengan menggunakan metode transfer learning Convolutional Neural Network yaitu dengan arsitektur MobileNet. Dalam proses penelitian ini, akan menggunakan dataset yang berjumlah total 1582 data citra terumbu karang yang memiliki 3 kelas utama dengan sebaran data yaitu 720 data bleached, 150 data dead dan 712 data healthy. Hasil yang didapatkan setelah dilakukannya proses pengujian pada penelitian ini yaitu arsitektur MobileNet mendapatkan akurasi pengujian yaitu sebesar 88%.
Co-Authors Ali, Rabei Raad Alzami, Farrikh Ashari, Ayu Aufa Nisrina Aulia Azzami, Salman Yuris Adila Cahaya Jatmoko Cahyani, Tiara Indah Candra Irawan Christy Atika Sari Edi Faisal Edi Faisal Edi Sugiarto Edi Sugiarto Eko Hari Rachmawanto Fahmi Amiq Faisal, Edi Heru Lestiawan Hindarto, Aris Nur Indra Gamayanto Jannah, Safira Baiti Maurensa, Giacinta Moh. Yusuf, Moh. MY. Teguh Sulistyono Nurhindarto, Aris Pristyanti Nawang Putri R. Septiansa Anggoro Rabei Raad Ali Rohman, M. Hilma Minanur Sasono Wibowo Sinaga, Daurat Suhariyanto - Suhariyanto Suhariyanto Sukamto, Titien Sukamto, Titien Suhartini Titien S. Sukamto Titien Sukamto Wellia Shinta Sari Winaryanti, Hida Sekar Zaenal Arifin