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All Journal ILKOM Jurnal Ilmiah Building of Informatics, Technology and Science
Amrillah, Sigit Fathu
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Computer Science & IT

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Vulnerability Assessment and Penetration Testing on Student Service Center System Isnaini, Khairunnisak; Asyari, Muhammad Hasyim; Amrillah, Sigit Fathu; Suhartono, Didit
ILKOM Jurnal Ilmiah Vol 16, No 2 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i2.1969.161-171

Abstract

The number of system breaches has recently increased across various sectors, including the education sector. These breaches are carried out through various methods such as SQL Injection, XSS Attack, web defacement, malware, and others. Security vulnerabilities in the system also pose a potential threat to the Student Service Center owned by XYZ University, which stores a significant amount of confidential and sensitive data. The worst impact of all is the system is paralyzed, damaging the ongoing performance and reputation of institutions. The purpose of this research is to identify security vulnerabilities in the system using the Vulnerability Assessment and Penetration Testing (VAPT) method. The results showed that the system identified file upload functionality that poses a risk of being exploited for security attacks. Additionally, file path traversal can allow unauthorized access to directories, potentially enabling the injection of malicious code. Future research could explore the application of machine learning to enhance security measures and streamline the penetration testing process
Penerapan Metode K-Nearest Neighbors dan Naïve Bayes pada Analisis Sentimen Pengguna Aplikasi Bstation melalui Platform Playstore Amrillah, Sigit Fathu; Krisbiantoro, Dwi; Prasetyo, Agung
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5863

Abstract

Streaming is a method of distributing digital content directly over the internet, which allows users to access media without the need to download files. Bstation is a streaming platform that combines (OGV) and User-Generated Content (UGC). This research assesses the effectiveness of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in analyzing sentiment in user reviews of the Bstation application, using a data sample of 5,000 reviews. The problem faced is the large number of users of the Bstation application, so sentiment analysis is needed to measure and understand the public's assessment of the application more accurately. This research aims to analyze the sentiment of Bstation users on Playstore and compare the performance of K-Nearest Neighbors (KNN) and Naïve Bayes to determine the best method for classifying reviews and user sentiment patterns. The findings showed that Naïve Bayes achieved 84% accuracy, surpassing KNN which only achieved 68%. Naïve Bayes showed 86% precision and 88% recall for negative sentiment, while achieving 78% precision and 76% recall for positive sentiment. recall for positive sentiment. In contrast, KNN achieved 80% precision and 66% recall for negative sentiments, and 54% recall for positive sentiments. recall for negative sentiments, and 54% precision and 71% recall for positive sentiments. The F1-Score for Naïve Bayes is also higher, reflecting a better balance overall. better balance overall. The macro average and weighted average weighted average for precision, recall, and F1-score with Naïve Bayes were 82% and 83%, respectively, while KNN recorded a macro average of 0.67. In conclusion, Naïve Bayes is more effective in sentiment analysis than KNN, providing more consistent and accurate performance
Co-Authors Asyari, Muhammad Hasyim Didit Suhartono Dwi Krisbiantoro, Dwi Isnaini, Khairunnisak Jayawarsa, A.A. Ketut