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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Ilmiah Informatika Komputer Techno.Com: Jurnal Teknologi Informasi Jurnal Penelitian Pendidikan Jurnal Sistem dan Informatika Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) IJIS - Indonesian Journal On Information System JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah Data Science: Journal of Computing and Applied Informatics Jurnal Informatika Global Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika JTIK (Jurnal Teknik Informatika Kaputama) SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Senopati : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Jurnal Pengabdian Kepada Masyarakat Simpatik: Jurnal sistem Informasi dan Informatika JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Unnes Journal of Mathematics Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal ilmiah teknologi informasi Asia Indonesian Journal of Mathematics and Natural Sciences Recursive Journal of Informatics
Florentina Yuni Arini
Universitas Negeri Semarang
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Journal : recursive journal of informatics

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XGBoost Implementation for Marine Fish Image Classification Using VGG19-Based Transfer Learning Anggito Wahyu Adi; Florentina Yuni Arini
Recursive Journal of Informatics Vol. 4 No. 2 (2026): September 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v4i2.37834

Abstract

Abstract. Accurate identification of fish species is important for trade supervision, marine biology, and fisheries management. This study develops a classification system for nine marine fish species by combining VGG19-based feature extraction with XGBoost. Using 9,000 balanced RGB images from the Kaggle Large-Scale Fish Dataset, the workflow includes EDA, preprocessing, an 80:20 data split, feature extraction, and model training. EDA showed that Red Mullet and Striped Red Mullet share similar visual characteristics, while the Shrimp class appears more distinct. The model achieved 97% accuracy and was deployed through a Flask web application that processes, extracts features, and classifies images. These findings demonstrate that integrating VGG19 with XGBoost offers an effective and efficient approach for marine fish image classification. Purpose: The primary objective here is to build a hybrid classification model for marine fish that is both efficient and robust, merging deep feature extraction from the VGG19 architecture with the XGBoost classifier. Specifically, this research seeks to improve classification accuracy while reducing computational overhead, thereby providing a reliable automated identification system that can support fisheries management, ecological monitoring, and marine biological research. Methods: This research employed a dataset of 9,000 images representing nine marine fish species. The preprocessing stage included resizing each image to 224×224 pixels and applying normalization. Deep feature representations were obtained using a pre-trained VGG19 model, utilizing only its convolutional layers to extract semantic features. These features were then classified with the XGBoost algorithm, chosen for its efficiency, resilience, and strong performance on structured data. The model was trained using an 80:20 split for training and testing, and its performance was assessed through accuracy, precision, recall, F1-score, and a confusion matrix. Result/Findings: The combination of VGG19 feature extraction with XGBoost produced strong results, achieving an accuracy of about 97%. This performance was further supported by consistently high precision, recall, and F1-scores across all nine species. Novelty/Value: A hybrid architecture integrating VGG19-based transfer learning with the XGBoost classifier is presented with an approach rarely explored in this domain. This combination capitalizes on deep semantic feature extraction from VGG19 alongside efficient structured classification via XGBoost, producing a model that delivers high accuracy with significantly reduced computational costs. Additionally, the hybrid approach proposed in this study remains competitive with fully end-to-end deep learning models yet operates with fewer parameters and reduced training demands. These results illustrate that well-designed hybrid pipelines can match the performance of conventional deep learning architecture while providing clear benefits in terms of training efficiency, interpretability, and scalability.
Co-Authors Abas Setiawan Agus Setyawan Ahmad Imam Mutaqin Ahmad Mustofa Hadi Romadhoni Ahmad Rozaq Heryansyah Ahmad Zidhan Ilmana Aisyah Nathania Araminta Aji, Yusuf Pandu Satrio Alaida, Salma Keysha Alamsyah - Aldevis, Mohammad Farrel Aloysius Oktavian Alul Hidja Syanjalih Amin Suyitno Ananda Hisma Putra Kristianto Andayani, Lafifa Linta Qisisin Anggito Wahyu Adi Anggraeni, Dinda Ayu Anwar, Alfani Salsabilla Ardiansyah, Ikhsan Ardin Winata Ariel James Maloringan Aryaputra, Daffa Pramata Arzaki Zunior Putra Astagina, Paramesti Asteen Retno Mukti Aufa Putra Wicaksono Awan Saputra Romadhoni Bagaskara, Josephin Nova Basysyar, Muhammad Ramzy Brata, Prayoga Adi Brigita Winona Elvaretta Radhiti Daffa Pramata Aryaputra Danish Adli El Said Dewanti, Rahima Ratna Dhifansa Pradibtya Rafi Djuanda, Lyon Ambrosio Duankhan, Poomin Endang Sugiharti, Endang Fadhlullah, Muhammad Azzam Fadil, Muhammad Haidar Husni Fairuz Trideas Hilmy Fajariansyah, Ridwan Faqih, Muhammad Najmuddin Farrel Athaillah Putra Farrel Fatih Bhimawan Fatih Akbar Alim Putra Fernando Dinar Setiawan Firdaus Zahid, Ahmad Galvin Fittra Marga Ardana Gerard Sean Dwayne Hanifah, Anissa Bunga Haryolukito Pambudi, Fawwaz Hernawan, Yoga Heryansyah, Ahmad Rozaq Hexa Sakti Tunjung Hidayat I Gede Ardhy Niratha Ikhsan Rakha Athaya Inoru Nian Alfita Intan Permata Sari Fauziah Irfan, Mohammad Syarif Isa Akhlis Isnaeni, Siti Itsna Sabila Hidayati Januar Pancaran Nur Fajri Julianto, Richy khairunnisa, Nadhia Adzqiya Kristianto, Ananda Hisma Putra Lyon Ambrosio Djuanda Mahdi Habibi Mardlootillah, Hanif Ilmi Mohammad Farrel Aldevis Much Aziz Muslim Muhammad Abdurrafi Muhammad Alvin Adinata Muhammad Danil Aminuddin Muhammad Hikam, Muhammad Muhammad Kahvi Khakam Syah Muhammad Lutfi Wibowo Muhammad Nur Furqon Muhammad Rifqi Rahman Muhammad Sulthonul Izza Muhammad Zidan Aisar Muhammad Zuniar Hilmi Munajid Toharo Muthia Nis Tiadah Mutia Zahra Fatiha Misbah Muzakki, Naufal Habib Nafi', Raihan Muhammad Naryapramono, Afrilza Daffa Nathania Adristina Nia Nur Hidayaturrohmah Nico Anselmus Sihombing Oktavian, Aloysius Pambudi, Fawwaz Haryolukito Pastika, Puan Bening Pongthanoo, Patcharanikarn Prameswari, Della Egyta Pramudya Kirana Mandala Putra Putra, Fatih Akbar Alim Putriaji Hendikawati Rafif Musyaffa Indarto Raharjo, Bagus Purbo Rahima Ratna Dewanti Rahmat Hidayat Raihan, Muhammad Ramadhan, Farhan Husyen Ramdhani, Khusnun Najwa Reza Zaidan Amrullah Rifan, Slamet Rinandi, Tyto Riza Arifudin Rizky Aulia Adi Saputro Romadhoni, Awan Saputra Ryo Pambudi Salsabila, Kansa Maulina Samudra Azriel Pradana Santoso, Tony Budi Saputro, Rizky Aulia Adi Sari, Yuliana Mustika Satria, Diva Sekar Tri Handayani Septiana, Dina Wachidah Supriyono Supriyono Syanjalih, Alul Hidja Taufiqur Ramadhan Tiadah, Muthia Nis Varindya Ditta Iswari Wahyudiantoro, Rizky Tri Warianta, Dwi Tatang Whisnu Ulinnuha Setiabudi, Whisnu Ulinnuha Wibowo, Muhammad Lutfi Wicaksana, Rangga Wicaksono, Aufa Putra Zaenal Abidin Zahra Zakiyah Kaltsum