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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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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.
PRO DAN KONTRA PENGGUNAAN AI PADA DUNIA PENDIDIKAN Zamsuri, Ahmad; Syafitri, Wenni; Guntoro, Guntoro; Waldelmi, Idel; Bimby, Novia Putri
Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Vol 5, No 2 (2025): Desember 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jpstm.v5i2.5414

Abstract

Abstract: The Community Service (PkM) activity aims to enhance the knowledge of teachers at MI AL FATTAAH regarding the utilization of Generative Artificial Intelligence (Gen-AI) in education. Currently, student assessment is still conducted conventionally, meaning the utilization of Gen-AI technology is not yet optimal. Non-involvement in this technological development could negatively impact the quality of education in the future. Through socialization and education activities, this PkM introduced the concepts, usage, and result analysis of Gen-AI in the educational context, highlighting the pros and cons of its implementation. Effectiveness assessment was conducted using pre-tests and post-tests with the Coefficient of Reproducibility (CR) and Coefficient of Scalability (CS). CR and CS results of 1 indicate that the knowledge transfer was effective and the activity was executed well. This PkM not only improved the teachers' understanding of AI but also has the potential to become a learning model for similar educational institutions.            Keywords: Gen-AI, Socialization, Utilization, Education  Abstrak: Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan meningkatkan pengetahuan guru MI AL FATTAAH mengenai pemanfaatan Generative Artificial Intelligence (Gen-AI) dalam pendidikan. Selama ini, penilaian murid masih dilakukan secara konvensional, sehingga pemanfaatan teknologi Gen-AI belum optimal. Ketidakterlibatan dalam perkembangan teknologi ini dapat berdampak negatif terhadap kualitas pendidikan di masa depan. Melalui kegiatan sosialisasi dan edukasi, PkM ini memperkenalkan konsep, penggunaan, serta analisis hasil Gen-AI dalam konteks pendidikan, dengan menyoroti aspek pro dan kontra penerapannya. Penilaian efektivitas dilakukan melalui pre-test dan post-test menggunakan koefisien Reprodusibilitas (CR) dan Skalabilitas (CS). Hasil CR dan CS sebesar 1 menunjukkan bahwa transfer pengetahuan berlangsung efektif dan kegiatan terlaksana dengan baik. PkM ini tidak hanya meningkatkan pemahaman guru terhadap AI, tetapi juga berpotensi menjadi model pembelajaran bagi lembaga pendidikan sejenis. Kata kunci: Gen-AI, Sosialisasi, Pemanfaatan, Edukasi
Enhanced social media phishing detection model using LSTM and BERT Syafitri, Wenni; Pane, Eddisyah Putra; Purwanto, Edi
Science, Technology, and Communication Journal Vol. 6 No. 2 (2026): SINTECHCOM Journal (February 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i2.360

Abstract

Phishing attacks are a major cyber threat, with more than 30% of incidents occurring via social media platforms, especially short message services. This study evaluates deep learning approaches for automated phishing detection using BERT and Hybrid (BERT-LSTM) architectures fine-tuned on 15950 annotated SMS. The BERT-only model achieved superior performance (F1 0.9928, recall 0.9952, AUC 0.999) with no statistically significant improvement from adding BiLSTM layers (0.0006). K-fold cross-validation demonstrated robust generalisation (coefficient of variation 0.10%). Dataset saturation analysis indicated that 15,950 SMS are sufficient for effective transfer learning. Mild overfitting (6.3x loss ratio) remained within acceptable bounds and did not affect validation metrics. The 1.77% false positive rate and 99.52% recall enable practical deployment for production phishing defence. Results demonstrate that transfer learning with BERT achieves production-grade performance while challenging conventional assumptions about architectural complexity.
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