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All Journal Journal of Computer Science Application and Engineering Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK)
Amirah
PT. Lentera Ilmu Publisher
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Other

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First Step for Vehicle License Plate Identification Using Machine Learning Approach Amirah; Ahmad Sanmorino
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 1 No. 1 (2023): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher

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Abstract

Automated vehicle license plate identification, critical in modern transportation systems, finds application in traffic monitoring, law enforcement, and transportation optimization. This study explores machine learning's potential to enhance accuracy and efficiency in this domain. Leveraging neural networks and pattern recognition, it aims to build an automated system robust across diverse conditions. Addressing limitations in traditional methods, it focuses on adapting to lighting, angles, and image quality variations. The societal impact includes streamlining law enforcement and optimizing traffic flow, revolutionizing transportation and surveillance. Methodologies cover data collection, ethical considerations, preprocessing, feature extraction, model selection, and iterative refinement. Ethical data handling ensures privacy compliance. Feature extraction methods like HOG, LBP, CNNs, and color histograms capture crucial aspects for identification. Model selection spans SVMs, CNNs, decision trees, and ensemble methods, considering task complexity and dataset characteristics. This study evaluates machine learning's potential for revolutionizing license plate identification systems.
Preliminary Study for Cyber Intrusion Detection Using Machine Learning Approach Amirah; Fitrah Karimah
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 1 No. 1 (2023): JAFOTIK - February
Publisher : PT. Lentera Ilmu Publisher

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Abstract

This article discusses the importance of information system security in the current technological era and how the increasingly complex threat of cyber attacks demands a more sophisticated approach to detection and prevention. This initial study explores the potential of applying Machine Learning in cyber intrusion detection as a first step to developing detection systems that are adaptive and responsive to evolving threats. Through a methodology involving the collection of representative data on cyber attacks, data preparation, and Machine Learning model selection, this article describes the initial stages for understanding and testing the potential of this technology in the context of cyber security. Although it includes an example dataset, data preparation steps, and the selection of several Machine Learning algorithms, this study only gets to the model selection stage, while the model training process and performance evaluation are the focus of future work. The conclusions of this initial study emphasize the importance of selecting appropriate algorithms with specific features for effective intrusion detection against growing cyber threats.
Co-Authors Fitrah Karimah