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All Journal International Journal of Electrical and Computer Engineering International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Advances in Applied Sciences Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA INOVTEK Polbeng - Seri Informatika Jurnal Informatika Universitas Pamulang RESEARCH : Computer, Information System & Technology Management Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Telematika Informatika : Jurnal Informatika, Manajemen dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Elektronika Listrik dan Teknologi Informasi Terapan Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Informatika dan Rekayasa Perangkat Lunak Amalee: Indonesian Journal of Community Research & Engagement Jurnal Teknika Jurnal Sistem Komputer dan Informatika (JSON) KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Joutica : Journal of Informatic Unisla Insand Comtech : Information Science and Computer Technology Journal Jurnal Informatika: Jurnal Pengembangan IT Jurnal Ilmiah Sistem Informasi
Nafiiyah, Nur
Prodi Teknik Informatika Fakultas Teknik Universitas Islam Lamongan
Aerospace Engineering Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Neuroscience Physics Public Health Social Sciences Transportation Other

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Sea Land Segmentation of East Java’s North Coast Using Landsat 9 and ResNet50 Nafiiyah, Nur; Ilyas; Rifky Aisyatul Faroh; Salwa Nabilah; Nur Azizah Affandy
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7435

Abstract

Coastal regions are among the most vulnerable ecosystems due to the combined impacts of natural processes and human activities. Climate change, population growth, and coastal development accelerate shoreline dynamics, increasing the need for accurate and efficient coastal monitoring. Satellite-based remote sensing, combined with deep learning techniques, provides a promising solution for large-scale and continuous shoreline analysis. This study proposes a deep learning–based approach for coastal land–sea segmentation using the ResNet50 architecture applied to Landsat 9 OLI imagery of the North Coast of East Java, Indonesia. The dataset consists of multispectral images processed into 224×224 pixel tiles, accompanied by manually generated ground truth segmentation maps. Two optimization strategies, Adam and Stochastic Gradient Descent (SGD), are evaluated to determine the most effective optimizer for improving segmentation performance. Experimental results demonstrate that the Adam optimizer outperforms SGD across multiple training epochs, achieving the highest segmentation accuracy with mean Intersection over Union (IoU) and Dice coefficient values of 0.888 and 0.934, respectively. These findings indicate that optimizer selection significantly influences the performance of ResNet50-based coastal segmentation. The proposed approach shows strong potential for supporting automated and large-scale coastal monitoring applications using medium-resolution satellite imagery.
Transfer Learning-Based Convolutional Neural Network for Classifying Organic and Recyclable Waste Nafiiyah, Nur; Zulkarnaen, M. Ari; Harjoko, Agus; Hidayanto, Achmad Nizar
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 8 No. 1 (2026): Maret
Publisher : Universitas Wahid Hasyim

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Abstract

The problem of waste management continues to increase along with population growth and lifestyle changes, highlighting the need for a fast and accurate waste classification system to support recycling processes. This study implements a transfer learning approach using seven Convolutional Neural Network (CNN) architectures: MobileNet, MobileNetV2, Xception, EfficientNetB0, VGG16, VGG19, and ResNet50 to classify waste into two categories: organic and recyclable. Each model is modified by adding a Global Average Pooling layer followed by a fully connected layer with 256 neurons before the output layer. The models are trained twice using 30 epochs, a batch size of 2, the Adam optimizer, and a learning rate of 0.0001. Experimental results show that ResNet50 achieves the best performance, with an accuracy of 89.84%, precision of 96.34%, recall of 82.82%, and an F1-score of 89.07%, followed by MobileNet with an accuracy of 89.25%. In contrast, Xception demonstrates the lowest performance, with an accuracy of 83.81%. Analysis of training and validation curves indicates that ResNet50 and MobileNet exhibit better stability and lower overfitting tendencies compared to other models.
Coastline segmentation on Landsat 8 OLI images using majority voting with deep learning models Nur Nafiiyah; Salwa Nabilah; Nur Azizah Affandy; Rifky Aisyatul Faroh; Esa Prakasa
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp588-596

Abstract

Coastlines are highly dynamic due to both natural processes and anthropogenic factors, including global warming and sea level rise. Accurate coastline segmentation is essential for effective monitoring and management. Although previous studies have applied deep learning for coastline detection, many existing models still suffer from instability across scenes, blurred boundaries, and segmentation artifacts, indicating that model generalization remains a challenge. This study aims to develop a more robust coastline segmentation approach by introducing an automated majority voting strategy that integrates three deep learning models: ResNet50, ResNet18, and MobileNet-V2. Landsat 8 OLI imagery is used for training and testing. The Jaccard index results show that ResNet18, ResNet50, and MobileNet-V2 achieved scores of 0.96, 0.98, and 0.95 respectively, while the proposed majority voting method also achieved 0.98. Despite the producing a similar numerical score to the best individual model (ResNet50), the ensemble method improves segmentation consistency by reducing artifacts such as unwanted peripheral shapes and cracks within land areas. These findings demonstrate that combining multiple segmentation outputs yields more stable and reliable coastline detection than using single models. Future work will apply this approach to broader Indonesian coastal regions to further assess its generalizability across diverse shoreline conditions.
Co-Authors ., Ichsan Yudistura Achmad Nizar Hidayanto Agus Harjoko Agus Setia Budi, Agus Setia Agus Subhan Akbar, Agus Subhan Alhidayati Asymal Ali Mokhtar Anny Yuniarti Arif Try Hidayatullah Ayu Ismi Hanifah Bagus Nur Bakti Aji Chastine Fatichah edy susanto Eha Renwi Astuti Eko Sulistiono Sulistiono Esa Prakasa Esa Prakasa, Esa Husnul Khotimah Ika Purnama Sari Ilyas Irma Dwi Rohmawati jacinda, nisrina Jauharul Maknun Khoiruddin, Muhammad Ubay Khotim Hidayati Hidayati M Sholihul Hadi M. Lazuardi Elsony Masruroh Masruroh Miftahus Sholihin Moh Muslikh Muchammad Khudori Muhammad Ali Haydar Muhammad Nurul Huda Munir Munir, Munir Nabilah, Salwa Nela Nevrivanti Aulia Prasetyo, Puguh Rouf Purnomo Hadi Susilo, Purnomo Hadi Putra, Ramadhan Hardani Retno Wardhani Rifky Aisyatul Faroh Rifqi Firdaus Al Jauhari Rizki Ardhian Ahmad Salwa Nabilah Setyan, Ardath Prahara Sholikah Indrawati Sholikha, Ni'matus Siti Mujilahwati tsabit yunan al rajab Yusuf Eka Yana Yusuf Permadi Zuhri, Ahmad Syafiq Maulana Zulkarnaen, M. Ari