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All Journal Abdi Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Pengabdian Kepada Masyarakat Jurnal Algoritma Scientific Journal of Informatics Jurnal ilmiah teknologi informasi Asia
Safitri, Pima Hani
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Journal : Scientific Journal of Informatics

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A Deep Learning Model Comparation for Diabetic Retinopathy Image Classification Mustaqim, Tanzilal; Safitri, Pima Hani; Muhajir, Daud
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.20939

Abstract

Purpose: This study compares the performance of various deep learning models for diabetic retinopathy (DR) classification, emphasizing the impact of different optimization functions. Early detection of DR is vital for preventing blindness, and the research investigates how optimization functions influence the classification accuracy and efficiency of several convolutional neural networks (CNNs). This study fills a gap in the existing literature by examining how optimization functions affect model performance in conjunction with architectural considerations. Methods: This paper uses the APTOS 2019 dataset, which comprises 3,663 retinal fundus images classified into five classes of diabetic retinopathy severity. Four CNN-based models, including CNN, ResNet50, DenseNet121, and EfficientNet B0, were trained using five optimization techniques: Adam, SGD, RMSProp, AdamW, and NAdam. The performance of the experimental scenarios was evaluated through accuracy, precision, recall, F1-score, training duration, and model size. Result: EfficientNet B0 demonstrated superior computational efficiency with a minimal model size of 16.16 MB. Subsequently, DenseNet121 with the SGD optimizer achieved the highest test accuracy of 96.86%. The experimental results indicate that the optimizer significantly influences model performance. AdamW and NAdam yield superior outcomes for deeper architectures such as ResNet50 and DenseNet121. Novelty: This paper offers an analytical examination of deep learning models and optimization techniques for DR classification, helping to clarify the trade-offs between computational efficiency and classification performance. The findings contribute to the development of more accurate and efficient DR detection systems, which could be utilized in real-world, resource-limited settings.
Freshwater Filling Optimization Based on Price Using XGBoost and Particle Swarm Optimization on Cargo Ship Voyage Yulianto, Ilham; Fauzi, Muhammad Dzulfikar; Safitri, Pima Hani
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24988

Abstract

Purpose: Efficient freshwater management is critical in cargo ship operations, yet current practices often involve fixed refilling strategies that ignore price differences across ports and fail to predict actual consumption accurately. These inefficiencies lead to unnecessary operational costs. To address this, the study introduces a combined approach using XGBoost for predict freshwater usage and Particle Swarm Optimization (PSO) to minimize refilling costs through optimal port selection. Methods: Freshwater demand was predicted using an XGBoost regression model trained on real operational data from 2024, which included historical voyage distances and freshwater consumption records from cargo ships. Based on these predictions, Particle Swarm Optimization (PSO) was applied to identify cost-efficient refilling locations along each ship’s route, minimizing total water procurement cost while satisfying operational constraints. The proposed framework was validated through simulated voyage scenarios to evaluate its impact on cost efficiency and planning effectiveness. Result: The integration of XGBoost and PSO effectively optimized freshwater refilling strategies, achieving a relative prediction error of 9.48% in freshwater consumption prediction and cost savings from 9 to 40% from across 3 ships sample through strategic port selection based on consumption patterns and price variability. Novelty: Unlike prior works focused on fuel or generic logistics optimization, aim of this study is to combine XGBoost and PSO for optimizing freshwater refilling on cargo ship voyages using actual operational data. The results demonstrate practical, scalable improvements in cost efficiency, making a novel contribution to maritime resource planning.
Co-Authors Ahmad Wali Satria Bahari Johan Alqis Rausanfita Ardian Yusuf Wicaksono Arip Ramadan Desita Nur Rachmaniar DEWI RAHMAWATI Fauzi, Muhammad Dzulfikar Firdaus, Raihan Krishna Lokajaya, Gabriel Azarya Muhajir, Daud Muhammad Ilham Alhari Putra, Muhammad Alifian Rahmawati, Dyah Putri Saputra, Ananda Bintang Sebastian, Gerrard Tanzilal Mustaqim Vessa Rizky Oktavia Yulianto, Ilham