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All Journal Scientific Journal of Informatics Journal of Student Research Exploration
Budi Prasetiyo
Department of Computer Science, Universitas Negeri Semarang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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 1 
S-box Construction on AES Algorithm using Affine Matrix Modification to Improve Image Encryption Security Alamsyah Alamsyah; Budi Prasetiyo; Yusuf Muhammad
Scientific Journal of Informatics Vol 10, No 2 (2023): May 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i2.42305

Abstract

Abstract.Purpose: In this study, the AES algorithm was improved by constructing the S-box using a modified affine matrix and implementing it so that there was an increase in security in image encryption.Methods: The method used in this study starts from selecting the best irreducible polynomial based on previous studies. The irreducible polynomial chosen is . With this irreducible polynomial, an inverse multiplicative matrix is formed. The formed inverse mutiplicative matrix is implemented in the affine transformation process using the best 3 affine matrices based on previous research and 8-bit additional constants using AES S-box. This formulation produces 3 different S-boxes, i.e., S-box1, S-box2, and S-box3. Finally, the resulting S-boxes are implemented to carry out the image encryption process and are tested for their security level.Result: The test results show an increase in image encryption security compared to previous studies. The increase in security occurred at the entropy value of 7.9994 and the NPCR value of 99.6288%.Novelty: The novelty of this paper is the improvement of the S-box construction which is implemented in image encryption resulting in increased security in image encryption.
The Comparison of Statement Analysis on Disney Case Using Naive Bayes, SNM, and Logistic Algorithm Methods Rizkiyanti Choirunnisa; Budi Prasetiyo
Journal of Student Research Exploration Vol. 4 No. 1 (2025): January 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v4i1.388

Abstract

The Walt Disney Company or also known as Disney, is one of the most famous companies in the world that focuses on the production of animation and film. Disney has been serve in this entertainment industry for over 90 years. Since Disney’s first film was released, Disney was become very famous until this day, especially when Disney has collaborates with many companies, it’s not only focusing on animation production but also making films and many live-action versions of the animations. Recently, Disney has been a hot topic among Disney’s movie fans due to the selection of actors for live action movie characters. Therefore, on this time, the author will conduct a sentiment analysis of Disney using analysis methods called Naïve Bayes, Support Vector Machine, and Logistic Algorithm. After passing through all the testing stages, the highest accuracy result is analysis using the Support Vector Machine (SVM) method. The test results show a high accuracy of 0.60 or 60% with the highest F1-Score in the Positive sentiment class by 67%.
Implementation of Lexicon-Based and SVM Methods in Sentiment Analysis of Sayurbox App Users Raihan Muhammad Rizki Rahman; Budi Prasetiyo
Journal of Student Research Exploration Vol. 4 No. 1 (2025): January 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v4i1.391

Abstract

The ever-growing technology certainly produces a large amount of data, which can provide useful information if analyzed and used properly. The purpose of this research is to analyze user sentiment towards the Sayurbox application on the Google Play Store with a Lexicon-Based approach and the Support Vector Machine (SVM) algorithm. User review data is obtained through web scraping with a total of 16,468 reviews. After preprocessing and sentiment labeling, training and test data were divided. The results showed that SVM achieved accuracy, recall, and precision of 94%, 96%, and 96% respectively, with 9 prediction errors. The model tends to predict reviews as positive sentiment, indicating user satisfaction with Sayurbox's product service, delivery, quality, and price. The findings make a contribution to the understanding of user sentiment in e-commerce services and can assist Sayurbox in improving their user experience.
Integrating Convolutional Neural Network Features Extraction with Extreme Learning Machines for Image Classification of Pandava Characters in Wayang Kulit Alfiatul Fitria; Budi Prasetiyo
Journal of Student Research Exploration Vol. 4 No. 1 (2025): January 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v4i1.407

Abstract

This research focuses on the utilization of image processing techniques—the Convolutional Neural Networks (CNNs) and Extreme Learning Machine (ELMs)—to classify the characters of Wayang Kulit automatically. The Pandava characters or casts are classified in accordance with the characters from traditional Indonesian puppets, commonly known as shadow puppets. The focus is to introduce such rich cultural heritage to younger generations by using technology. Prior research has utilized classification of characters using Convolutional Neural Networks(CNNs), Extreme Learnings Machines(ELMs), and Support Vector Machines(SVMs), which led to varied accuracy levels. In our subsequent experiments, three proposed models, with varying underlying model assumptions, were evaluated. The proposed models generated moderate accuracies ranging from 39 to 52%. The results suggest that our models have room for further development to enhance their performance. Strategies from parameter tuning to the in-depth analysis of the confusion matrix are discussed. Above all, the research is geared towards ensuring the appreciation and preservation of traditional cultural heritage in this digital era.
Co-Authors Alamsyah Alamsyah Alfiatul Fitria Raihan Muhammad Rizki Rahman Rizkiyanti Choirunnisa Yusuf Muhammad