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All Journal Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF)
Zega, Imanuel
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Health Professions Public Health

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IMPLEMENTASI SISTEM INFORMASI PERPUSTAKAAN SLiMS DI UNIVERSITAS PIGNATELLI TRIPUTRA Rondonuwu, Yeremia Victor; Zega, Imanuel; Hardiyanti, Mawar; Kurniawan, David Thanlian
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 2 (2024): Volume 5 No. 2 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i2.25305

Abstract

Perpustakan merupakan sumber intelektual utama dalam konteks akademik, yang memiliki peran penting dalam menghubungkan pelajar dan pendidik dengan pengetahuan yang terus berkembang sehingga suntikan teknologi merupakan salah satu sarana untuk meningkatkan daya tarik bagi pengunjung perpustakaan agar dapat memberikan layanan secara dinamis terkhusus dalam bidang distribusi ilmu pengetahuan. Setelah proses melewati situasi pandemi Universitas Pignatelli Triputra memerlukan peningkatan layananan literasi baca secara digital sehingga diperlukan adanya teknologi SLiMS (Senayan Library Management System) sebagai media sistem informasi perpustakaan berbasis web yang dikembangkan oleh Perpustakaan Kementrian Kebudayaan Riset dan Teknologi Indonesia. Peneliti tergerak untuk melakukan pengabdian pada Universitas dengan melakukan pembangunan sistem informasi perpustakaan guna meningkatkan kualitas layanan perpustakaan. Sistem Informasi Perpustakaan Universitas Pignatelli Triputra kini telah terbit dan dapat diakses melalui situs resmi perguruan tinggi sehingga dapat dinyatakan bahwa proses pengabdian ini dapat dinyatakan mampu menghadirkan manfaat bagi perpustakaan dengan harapan mampu mendorong lahirnya digitalisasi perpustakaan bagi perguruan tinggi dan Pendidikan di Indonesia.
Natural Language Processing (NLP) and Support Vector Machine (SVM) Optimization in Detecting Phishing Website URLs Aritonang, Mhd Adi Setiawan; Simanulang, Maradona Jonas; Batubara, Toras Pangidoan; Zega, Imanuel; Afrizal, M Hafis
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5334

Abstract

Phishing remains one of the most pervasive cyber-threats, with recent reports indicating a sharp rise in both volume and sophistication of attacks. According to the Anti‑Phishing Working Group, phishing incidents reached nearly 1 million in Q4 2024. To address this evolving threat, this study aims to develop an automated phishing-URL classification system based on Natural Language Processing (NLP) and Support Vector Machine (SVM). We utilised the Kaggle "PhiUSIIL Phishing URL Dataset" comprising 256,795 URL records and applied comprehensive preprocessing, feature extraction (structural URL features plus NLP-based keyword analysis), and SVM training with grid search optimisation. Evaluation was performed via confusion matrix and standard metrics of accuracy, precision, recall and F1-score. The best model achieved an accuracy of 99.99%, precision of 99.98%, recall of 100%, and F1-score of 99.99%. These results demonstrate that the combined NLP + SVM approach can effectively distinguish phishing from legitimate URLs with very high reliability. The proposed system contributes to cybersecurity by offering a feasible AI-based solution for real-time URL screening that can be integrated into browser extensions or enterprise email filters to bolster phishing defences.
Co-Authors Afrizal, M Hafis Batubara, Toras Pangidoan Hardiyanti, Mawar Kurniawan, David Thanlian Mhd Adi Setiawan Aritonang Simanulang, Maradona Jonas Yeremia Victor Rondonuwu