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All Journal Scientific Journal of Informatics Journal of Information Technology and Cyber Security
Sari, Ghaluh Indah Permata
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Tide Prediction in Prigi Beach using Support Vector Regression (SVR) Method Utami, Tri Mar'ati Nur; Novitasari, Dian Candra Rini; Setiawan, Fajar; Ulinnuha, Nurissaidah; Farida, Yuniar; Sari, Ghaluh Indah Permata
Scientific Journal of Informatics Vol 8, No 2 (2021): November 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i2.28906

Abstract

Purpose: Prigi Beach has the largest fishing port in East Java, but the topography of this beach is quite gentle, so it is prone to disasters such as tidal flooding. The tides of seawater strongly influence the occurrence of this natural event. Therefore, information on tidal level data is essential. This study aims to provide information about tidal predictions. Methods: In this case using the SVE method. Input data and time were examined using PACF autocorrelation plots to form input data patterns. The working principle of SVR is to find the best hyperplane in the form of a function that produces the slightest error. Result: The best SVR model built from the linear kernel, the MAPE value is 0.5510%, the epsilon is 0.0614, and the bias is 0.6015. The results of the tidal prediction on Prigi Beach in September 2020 showed that the highest tide occurred on September 19, 2020, at 10.00 PM, and the lowest tide occurred on September 3, 2020, at 04.00 AM. Value: After conducting experiments on three types of kernels on SVR, it is said that linear kernels can predict improvements better than polynomial and gaussian kernels.
Explainable Artificial Intelligence Analysis of Transfer Learning Models for Alzheimer’s Disease MRI Classification Salsabila, Dea Amanda; Sari, Ghaluh Indah Permata; Hermawati, Fajar Astuti
Journal of Information Technology and Cyber Security Vol. 4 No. 1 (2026): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.133060

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder that leads to cognitive decline and requires early and accurate diagnosis to slow disease progression. Magnetic resonance imaging (MRI) is widely used to detect structural brain changes associated with Alzheimer’s disease; however, manual interpretation of MRI scans is time-consuming and subject to observer variability. Deep learning approaches have shown strong potential in automated MRI analysis, but their black-box nature limits clinical trust and interpretability. This study proposes a transfer learning–based deep learning framework for Alzheimer’s disease classification, complemented by explainable artificial intelligence (XAI) techniques to analyze model predictions. A pretrained VGG16 model is employed to classify MRI images into four cognitive impairment categories: no impairment, very mild impairment, mild impairment, and moderate impairment. To enhance transparency, Grad-CAM, Saliency Maps, and Guided Grad-CAM are applied to visualize brain regions that contribute most to model predictions. Experimental results demonstrate that the proposed approach achieves 95.41% accuracy, indicating that a well-balanced network architecture combined with integrated explainability techniques leads to effective, interpretable classification. The visual explanations highlight clinically meaningful brain regions that align with known Alzheimer’s disease–related structural changes. These findings suggest that combining deep transfer learning with explainable artificial intelligence can provide accurate and interpretable decision support for Alzheimer’s disease diagnosis. This study is limited by the use of a single publicly available dataset and two-dimensional MRI slices, which may affect generalizability across clinical environments.
Co-Authors Fajar Astuti Hermawati FAJAR SETIAWAN Nurissaidah Ulinnuha Salsabila, Dea Amanda Sari, Dian Candra Rini Novita Utami, Tri Mar'ati Nur Yuniar Farida, Yuniar