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All Journal Journal of System and Computer Engineering Informatics, Electrical and Electronics Engineering
Mafula, Veradella Yuelisa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Hill Cipher-Based Visual Cryptography for Copyright Protection of Images Using Flexible Matrix Keys Mafula, Veradella Yuelisa; Fauzan, Abd. Charis; Prabowo, Tito; Ramadhan, Muhammad Rizky
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1634

Abstract

The widespread distribution of digital images on the internet has diminished the copyright protection associated with them. In some cases, copyrighted and economically valuable digital images should not be modified or distributed without permission, as altering the original image can harm its owner. This violation is common, but many internet users are unaware of it. The goal of this research is to protect intellectual property rights of digital images using visual cryptography based on the Hill Cipher algorithm with matrix key flexibility. Hill Cipher is chosen for its ability to encrypt data in blocks, making it more secure than classical cryptographic algorithms that encrypt data individually. Visual cryptography is used to secure digital images through encryption and decryption. Encryption scrambles the image, while decryption restores it. The research method involves collecting digital image datasets, preprocessing, Hill Cipher encryption, and decryption. Key flexibility includes matrix keys of 2x2, 3x3, and 4x4 to enhance security. This research has demonstrated the effectiveness of the Hill Cipher algorithm in protecting digital images through encryption and decryption processes with flexible matrix keys of size 2x2 and 3x3. The results of the experiments, including encryption and decryption using both matrix sizes, have been thoroughly analyzed with respect to various cryptographic metrics: histogram analysis, energy, entropy, and running time.
Comparative Analysis of Machine Learning Models for Identifying Cybercrimes in Social Media Comments Fauzan, Abd. Charis; Arifin, Mochammad; Mafula, Veradella Yuelisa
Jurnal Teknik Elektro dan Informatika Vol 4 No 2 (2024): INFOTRON
Publisher : Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/infotron.v4i2.23069

Abstract

The rapid growth of social media has created opportunities for digital interaction but has also introduced challenges, particularly in addressing cybercrimes such as defamation, threats, and SARA-related content. Cybercrime detection on social media is critical as it helps mitigate the spread of harmful behavior, safeguard users, and support law enforcement in addressing violations like Indonesia's Information and Electronic Transactions Law (UU ITE). This study conducts a comparative analysis of machine learning algorithms—Naive Bayes, Support Vector Machines (SVM), and Random Forests—to identify cybercrimes in social media comments. Using a sentiment-labeled dataset obtained from Kaggle, consisting of Indonesian social media comments from Twitter (X), the comments are categorized into seven specific classes: Neutral Sentiment, Positive Sentiment, Negative Sentiment, Insulting Government, Insulting or Defaming Others, Threatening Others, and SARA-Based Content. The results show that Random Forest achieved the highest overall accuracy (91%) and performed best in detecting moderately represented classes such as Insulting Government. SVM demonstrated robust performance with 88% accuracy, particularly excelling in identifying dominant classes like Negative Sentiment, while Naive Bayes, though computationally efficient, struggled with minority classes, achieving an accuracy of 73%. However, the dataset's imbalance posed challenges for all algorithms, particularly with underrepresented categories. This limitation underscores the need for more diverse and representative datasets to improve model performance and ensure broader applicability of the findings.
Co-Authors Abd. Charis Fauzan Mochammad Arifin Muhammad Rizky Ramadhan Prabowo, Tito