https://ejournal.unib.ac.id/pseudocode/issue/feed 2025-08-31T14:28:17+00:00 Widhia Oktoeberza KZ, S.T., M.Eng. pseudocode@unib.ac.id Open Journal Systems <p><strong>Pseudocode </strong>is a scientific journal in the information science family that contains the results of informatics research, scientific literature on informatics, and reviews of the development of theories, methods, and applications of informatics engineering science. Pseudocode is published by UNIB Press, Universitas Bengkulu, Indonesia. Editors invite researchers, practitioners, and students to submit article manuscripts in the field of informatics engineering. Pseudocode is published 2 (two) times a year in February and September with p-ISSN 2355-5920 e-ISSN 2655-1845.</p> <p><strong>Jurnal Pseudocode </strong>is Accredited by the Ministry for Research, Technology and Higher Education (RISTEKDIKTI) in SINTA 4 No. 177/E/KPT/2023 since 15 October 2024. </p> https://ejournal.unib.ac.id/pseudocode/article/view/44616 2025-08-31T14:28:17+00:00 Nizar Haris Masruri nizarhm@iainponorogo.ac.id

<p><em>Steganography is a method in network security systems that functions to hide confidential information by inserting it into other media without changing the authenticity of the information. One of the steganography media is images. Image steganography has two domains: spatial and frequency. The spatial domain works on the pixels of an image, and the frequency domain works on the image frequency. Spatial image steganography is said to be good if the level of stego-image damage is very small. There are several intermediate methods in spatial steganography, namely: LSB, PVD, GLM, EBE, RPE, and others. However, there are two most popular methods, namely LSB (Least Significant Bit) and PVD (Pixel Value Differencing). Therefore, this study aims to test the quality of stego-images produced by these two methods. Testing is carried out using the MSE and PSNR value parameters. A stego-image is considered good when the MSE value is close to 0 and the PSNR value is above 30 dB. From the test data used, this study produced an MSE value of 0.0006 for LSB stego-images and 0.007 for PVD stego-images. The PSNR value was 90 dB for LSB stego-images and 72 dB for PVD stego-images. This study concluded that LSB stego-images have less damage than PVD stego-images, so the researchers recommend using the LSB method in carrying out the steganography process.</em></p> <p><em>Keywords: Spatial;MSE;PSNR;LSB;PVD</em></p>

2025-09-06T00:00:00+00:00 Copyright (c) 2025 Nizar Haris Masruri https://ejournal.unib.ac.id/pseudocode/article/view/41284 2025-05-10T06:42:58+00:00 Muhammad Oriza Nurfajri oriza@ugm.ac.id Guntur Budi Herwanto gunturbudi@ugm.ac.id

<p><em>This research proposes an ensemble learning approach for classifying Distributed Denial of Service (DDoS) attacks using the CIC-DDoS2019 dataset. DDoS attacks remain a significant threat to network security, necessitating efficient detection methods. We developed an ensemble model combining Random Forest, Gradient Boosting, and AdaBoost classifiers to enhance detection accuracy. Our methodology involves preprocessing the CIC-DDoS2019 dataset, extracting relevant features, and implementing both binary classification (benign vs. attack) and multiclass classification (attack type identification). The experimental results show that our ensemble model achieves an F1-score of 0.9967 for binary classification, with Gradient Boosting performing best among individual models. The multiclass classification reaches an accuracy of 0.8742 in distinguishing between different types of DDoS attacks. This research demonstrates that ensemble learning significantly improves the accuracy and reliability of DDoS attack detection compared to single-model approaches.</em></p> <p><em>Keywords: </em><em>Ensemble learning; DDoS attack; network security; CIC-DDoS2019; machine learning.</em></p>

2025-07-19T00:00:00+00:00 Copyright (c) 2025 Muhammad Oriza Nurfajri, Guntur Budi Herwanto