Abstract

CentOS merupakan distribusi Linux berbasis RHEL yang dikenal karena kestabilan dan keamanannya di lingkungan server. Penelitian ini menggunakan metode studi literatur untuk menganalisis perkembangan, kelebihan, kekurangan, serta potensi pengembangan CentOS. Hasil kajian menunjukkan bahwa CentOS unggul dalam kestabilan sistem, efisiensi sumber daya, dan keamanan kernel, meskipun kurang fleksibel untuk integrasi cloud dan memerlukan konfigurasi manual. Dibandingkan Ubuntu, CentOS lebih stabil dan cocok untuk server enterprise, sedangkan Ubuntu unggul dalam kemudahan penggunaan. Potensi pengembangan CentOS mencakup penerapan pada e-Government, pendidikan, cloud computing, dan sistem monitoring otomatis, menjadikannya fondasi penting dalam transformasi digital modern.

Keywords: linux, centos, open source, keamanan, cloud computing

Citation

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Links

• Publisher Page: https://jifosi.upnjatim.ac.id/index.php/jifosi/article/view/558
• Google Scholar: https://scholar.google.com/scholar?cluster=15704843006237176776

Abstract

This study uses the Convolutional Neural Network (CNN) method with the AlexNet model to classify eye diseases based on medical images. The dataset includes labeled images of three types of eye diseases: cataract, glaucoma, and diabetic retinopathy. The experimental results show that the model achieved an accuracy of 75.18%, which indicates that CNN with the AlexNet architecture can classify eye diseases quite well. This research shows that deep learning can be used to help doctors or health professionals in diagnosing eye diseases through automatic image analysis. Although the accuracy still needs to be improved, this study can serve as a reference for developing an automated diagnostic system in the future. Further research is expected to increase accuracy, expand the dataset, and apply other deep learning techniques to improve the performance of eye disease detection.

Keywords: Classification of Eye Diseases, Convolutional Neural Network, Alexnet, Deep Learning

Citation

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Links

• Publisher Page: https://ijconsist.org/index.php/ijconsist/article/view/160
• Google Scholar: https://scholar.google.com/scholar?cluster=9341093393877158497

Abstract

Domain Name System (DNS) is an essential component in internet infrastructure to direct domains to IP addresses or conversely. Despite its important role in delivering internet services, attackers often use DNS as a bridge to breach a system. A DNS traffic analysis system is needed for early detection of attacks. However, the available security tools still have many shortcomings, for example broken authentication, sensitive data exposure, injection, etc. This research uses DNS analysis to develop anomaly-based techniques to detect malicious traffic on the DNS infrastructure. To do this, We look for network features that characterize DNS traffic. Features obtained will then be processed using the Decision Tree algorithm to classifyincoming DNS traffic. We experimented with 2.291.024 data traffic data matches the characteristics of BotNet and normal traffic. By dividing the data into 80% training and 20% testing data, our experimental results showed high detection aacuracy (96.36%) indicating the robustness of our method.

Keywords: DNS traffic analysis, machine learning, decision tree

Citation

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Links

• Publisher Page: https://juti.if.its.ac.id/index.php/juti/article/view/1054
• Google Scholar: https://scholar.google.com/scholar?cluster=8696158275109397624

Abstract

Software requirements specification (SRS) is an essential part of software development. SRS has two features: functional requirements (FR) and non-functional requirements (NFR). Functional requirements define the needs that are directly in contact with stakeholders. Non-functional requirements describe how the software provides the means to carry out functional requirements. Non-functional requirements are often mixed with functional requirements. This study compares four primarily used machine learning methods for classifying functional and nonfunctional requirements. The contribution of our research is to use the PROMISE and SecReq (ePurse) dataset, then classify them by comparing the FastText+SVM, FastText+CNN, SVM, and CNN classification methods. CNN outperformed other methods on both datasets. The accuracy obtained by CNN on the PROMISE dataset is 99% and on the Seqreq dataset is 94%.

Keywords: CNN; FastText; Requirements Classification; Software Requirements; SVM

Citation

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Links

• Publisher Page: https://iptek.its.ac.id/index.php/jts/article/view/13005
• Google Scholar: https://scholar.google.com/scholar?cluster=1908119983044836934