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All Journal Jurnal Teknik Informatika Journal of Computers and Digital Business
Yulistia , Anita
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Computer Science & IT Decision Sciences, Operations Research & Management

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Penerapan Teknologi Mikrotik Dalam Jaringan Point-To-Point Untuk Meningkatkan Kinerja Infrastruktur Jaringan Kautsar, Afthar; Yulistia , Anita; Ritonga , Meini Syakinah; Armansyah
JEKIN - Jurnal Teknik Informatika Vol. 4 No. 3 (2024)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v4i3.729

Abstract

Infrastruktur jaringan yang baik dapat meningkatkan kelancaran operasional dan produktivitas suatu organisasi. Penggunaan perangkat MikroTik pada infrastruktur ini berperan penting dalam memberikan layanan komunikasi dan informasi. Optimalisasi jaringan point-to-point dengan MikroTik memfasilitasi koneksi langsung antara dua lokasi jarak jauh, meningkatkan efisiensi dan kecepatan transfer data. Penelitian ini bertujuan untuk meningkatkan kinerja infrastruktur jaringan. Metode yang digunakan antara lain analisis permasalahan, pengumpulan data, implementasi, pengujian. Hasil penelitian menunjukkan peningkatan kinerja infrastruktur jaringan yang signifikan terlihat dari peningkatan kecepatan transfer data, penurunan latensi, dan stabilitas koneksi pada Dinas Komunikasi dan Informatika Kota Medan. Penelitian ini memberikan solusi efektif untuk mengatasi permasalahan konektivitas dan meningkatkan kinerja jaringan.
Applying Random Forest Algorithm for Phishing URL Identification Kautsar, Afthar; Aida, Maghfira; Yulistia , Anita
Journal of Computers and Digital Business Vol. 4 No. 3 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i3.782

Abstract

Phishing attacks continue to be one of the most pervasive cybersecurity threats, particularly through malicious URLs designed to mimic legitimate websites and steal sensitive user information. To address this challenge, this study employs the Random Forest algorithm for automated phishing URL detection using a publicly available dataset from Kaggle. The dataset contains diverse structural, technical, and popularity-based features that capture behavioral and lexical characteristics of each URL. Following data preprocessing and an 80/20 train–test split, the Random Forest classifier achieved strong predictive performance, attaining an accuracy of 94.94%, a precision of 95.19%, and a recall of 96.94%. The model further demonstrated robust classification capability with an F1-score of 96.06% and an ROC AUC value of 0.985, indicating excellent discrimination between phishing and legitimate URLs. Feature importance analysis shows that factors such as the URL’s presence in Google’s index, page rank metrics, and specific structural patterns significantly influence prediction outcomes. Additionally, performance visualizations including ROC and Precision–Recall curves reinforce the model’s reliability and stability. Overall, the findings suggest that Random Forest provides an effective and efficient solution for phishing URL detection, offering promising potential for integration into real-world cybersecurity systems.
Co-Authors Aida, Maghfira Armansyah Kautsar, Afthar Ritonga , Meini Syakinah