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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal CoreIT Dinamisia: Jurnal Pengabdian Kepada Masyarakat Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Pengabdian Masyarakat Multidisiplin Jurnal Teknik Informatika (JUTIF) Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Science, Technology, and Communication Journal
Wenni Syafitri
Universitas Lancang Kuning
Aerospace Engineering Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Jurnal CoreIT

 1 
Penilaian Risiko Keamanan Informasi Menggunakan Metode NIST 800-30 (Studi Kasus: Sistem Informasi Akademik Universitas XYZ) Wenni Syafitri
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 2, No 2 (2016): Desember 2016
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (782.18 KB) | DOI: 10.24014/coreit.v2i2.2356

Abstract

Sistem informasi akademik Universitas XYZ merupakan terobosan terbaru dibidang pelayanan akademik. Sistem ini menyediakan berbagai informasi yang dibutuhkan oleh civitas akademika. Sehingga kebutuhan akan keberlangsungan sistem ini semakin penting. Permasalahan yang pernah ada di SI Akademik Universitas XYZ seperti berkaitan dengan celah kerawanan keamanan informasi. Jika permasalahan ini tidak dapat diperbaiki secara berkelanjutan, alhasil akan memberikan dampak ataupun risiko kepada keberlangsungan sistem ini, khusunya civitas akademika. Penelitian ini menggunakan NIST SP 800-30 sebagai metode yang digunakan untuk menyelesaikan permasalahan tersebut. Maka berdasarkan hasil penelitian yang telah dilakukan, Universitas xyz memiliki 1 tingkat risiko tinggi, 5 tingkat risiko sedang dan 52 tingkat risiko rendah.
Optimization Of Social Media Phishing Detection Models Syafitri (Scopus ID: 57200085316), Wenni; Guntoro, Guntoro; Zamsuri, Ahmad; Waldelmi, Idel
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 1 (2025): June 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i1.37602

Abstract

Phishing is one of the most dangerous attacks in the cyber world. Very few researchers have focused on social media phishing, although SMS phishing can be related to the messaging features available on various social media platforms. This study will utilize PSO and PCA techniques to optimize the performance of RF in social media phishing. This study will compare the performance of PSO and RF with that of PCA and RF. An optimized phishing message detection model was built using NLP, incorporating TF-IDF for feature extraction, PCA and PSO for feature optimization, and Random Forest as a classifier to distinguish phishing messages from normal messages. The RF model optimized by PSO produces nearly balanced metrics: precision (0.9877), recall (0.9728), and F1 (0.9802), all of which are high. The RF model with PCA optimization achieves a slightly lower Accuracy (0.9639) and the lowest Precision (0.9585). Although there were no significant differences in the classification process, PSO and PCA made a real contribution to future research development.
Phishing Detection in Deep Learning: Systematic Literature Review Abdillah (Scopus ID: 57210600304), Rahmad; Syafitri, Wenni
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 1 (2024): June 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i1.31009

Abstract

Abstract. Phishing is an attack that is harmful to organizations and individuals in cybersecurity. Many researchers use deep learning techniques to detect phishing. However, the proposed techniques still have shortcomings in terms of performance, especially in detecting unknown attacks, even though they have been developed in such a way. Therefore, to gain a more comprehensive understanding of the current state of research on the use of deep learning to detect phishing, a systematic literature review (SLR) is needed. This SLR aims to identify deep learning techniques, performance measures, overfitting techniques, datasets, parameters, phishing types, and recommendations for future phishing detection research. The method used by SLR consists of a research question and research objective, Search strategy, Inclusion and exclusion criteria, and Data extraction and Analysis. Over the past five years, SLR successfully identified 25 quality articles on phishing detection using deep learning. The contribution of this SLR is to provide insight into the current state of research and identify future research areas of phishing detection using deep learning techniques.
Co-Authors Abdillah (Scopus ID: 57210600304), Rahmad Ahmad Zamsuri, Ahmad Bayu Febriadi, Bayu Bimby, Novia Putri Canny Siska Georgina Edi Purwanto Farroh Sakinah Febrizal As-Syam Guntoro, Guntoro Haris Simare Mare idel waldelmi, idel Loneli Costaner M. Ryan Fadholi Muhamad Sadar, Muhamad Novriyanto Novriyanto Pane, Eddisyah Putra Setiadi Yazid, Setiadi