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Hidra Amnur
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admjitsi@gmail.com
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Kampus Politeknik Negeri Padang, Jurusan Teknologi Informasi. Gedung E. Limau Manis, Pauh. Padang - Sumatera Barat. Indonesia
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JITSI : Jurnal Ilmiah Teknologi Sistem Informasi
Published by Society of Visual Informatics
ISSN : 27224619     EISSN : 27224600     DOI : 10.30630/jitsi
Core Subject : Science,
Computer Science & IT
The journal scopes include (but not limited to) the followings: Computer Science : Artificial Intelligence, Data Mining, Database, Data Warehouse, Big Data, Machine Learning, Operating System, Algorithm Computer Engineering : Computer Architecture, Computer Network, Computer Security, Embedded system, Coud Computing, Internet of Thing, Robotics, Computer Hardware Information Technology : Information System, Internet & Mobile Computing, Geographical Information System Visualization : Virtual Reality, Augmented Reality, Multimedia, Computer Vision, Computer Graphics, Pattern & Speech Recognition, image processing Social Informatics: ICT interaction with society, ICT application in social science, ICT as a social research tool, ICT education
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 12 Documents
 1   2 
Search results for , issue "Vol 6 No 2 (2025)" : 12 Documents clear
Pengaruh Augmentasi Data Terhadap Akurasi Pelatihan Model CNN untuk Klasifikasi Jenis Ikan Al-Fahrezi, Muhammad Abel
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 6 No 2 (2025)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.6.2.471

Abstract

Sustainability of marine resources and management of aquatic ecosystems depend on accurate fish classification. CNNs have proven successful in image classification tasks; however, they often face the problem of limited data variation. The purpose of this study was to examine how data augmentation affects the training accuracy of CNN models for fish species classification. Two scenarios were studied: the first scenario involved training without data augmentation, and the second scenario involved training with data augmentation. In both scenarios, a custom CNN architecture for ten epochs was used. Experimental results showed that using data augmentation with the configuration used actually caused the model performance to deteriorate. Loss values ​​on both datasets increased, with training accuracy dropping from 76.08% to 63.81%, and validation accuracy also dropping from 91.13% to 84.55%. Overly aggressive augmentation parameters or insufficient training time for the introduced data variation could have caused this decline. Interestingly, validation accuracy was consistently higher than training accuracy in both situations, indicating that certain datasets have specific features. This study emphasizes the importance of carefully optimizing augmentation parameters and training duration to maximize the benefits of data augmentation in image classification.
Penerapan Algoritma Random Forest untuk Deteksi Phishing pada Website Fahri, Muhammad
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 6 No 2 (2025)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.6.2.472

Abstract

Phishing attacks have become one of the most rapidly increasing cybersecurity threats in recent years. Phishing websites are designed to deceive users into divulging sensitive information such as login credentials, credit card data, and other personal details. This research proposes the implementation of the Random Forest algorithm for automated phishing website detection. The dataset used in this study comprises 10,000 classified URL samples, with 49 distinct features extracted. The research methodology includes data preprocessing, URL feature extraction, Random Forest model training, and performance evaluation. The evaluation results demonstrate that the developed Random Forest model achieved an accuracy of 98.20%, precision of 98.22%, recall of 98.22%, and an F1-score of 98.22%. This study proves that the Random Forest algorithm is highly effective for phishing detection and can be implemented as a preventive security system in internet Browse.

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