Article Per Year (5 Year)
p-Index From 2021 - 2026
4.445
P-Index
This Author published in this journals
All Journal Jupiter JSI: Jurnal Sistem Informasi (E-Journal) Jusikom : Jurnal Sistem Komputer Musirawas Jurnal Kreativitas PKM International Journal of Nursing and Health Services (IJNHS) Jurnal Informatika Global Progresif: Jurnal Ilmiah Komputer Klorofil: Jurnal Penelitian Ilmu-Ilmu Pertanian Jurnal Digital Teknologi Informasi Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal Media Informatika JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Komputer, Informasi dan Teknologi Brilliance: Research of Artificial Intelligence Kharisma Tech SENTRI: Jurnal Riset Ilmiah International Journal Cister Indo-Fintech Intellectuals: Journal of Economics and Business Kohesi: Jurnal Sains dan Teknologi Jurnal Integrasi Manajemen Pendidikan INOVTEK Polbeng - Seri Informatika
Dedi Haryanto
Universitas Muhammadiyah Palembang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 35 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Kharisma Tech

 1 
AUTENTIKASI BERKELANJUTAN BERBASIS POLA KEYSTROKE MENGGUNAKAN METODE DEEP LEARNING Haryanto, Dedi
KHARISMA Tech Vol 21 No 1 (2026): KHARISMATech Journal
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v21i1.692

Abstract

This study developed a continuous authentication system based on keystroke patterns to enhance digital identity security. The problem arose because conventional authentication only verified users at the beginning of a session, making it vulnerable to session intrusion. The proposed approach utilized free-text keystroke dynamics by analyzing key press duration, inter-key latency, and typing speed as behavioral biometric features. Data were processed through normalization, sliding window segmentation, feature extraction, training of a hybrid deep learning model combining Convolutional Neural Network and Bidirectional Long Short-Term Memory, and evaluation using False Acceptance Rate, False Rejection Rate, and Equal Error Rate. The results showed that the system consistently identified legitimate users with high accuracy, fast detection response, and low error rates. It was concluded that keystroke-based continuous authentication provided an effective, adaptive, efficient, and non-intrusive security solution
Co-Authors . Apriansyah Aminullah Imal Alfresi Anita Kusumasari Apriansyah Apriansyah, Apriansyah Arif Setiawan Arjanah, Sukma Astuari, Ferdinan Basri, Mahdaniyah Dafa Haqiki, Arka Fitriani, Noviana Fitriani, Nurul Hikmah Hartini Hartini Hermanto Hermanto Hutapea, Fresley Ika Guslanda Bustam Jimmei Jimmei Jimmie Jimmie Jimmie, Jimmie Karnadi Luh Putu Ratna Sundari Markarma, A. Meliana Winda Perdana Muhammad Fikri Muhammad Ihsan Muhammad Ikhsan Saputra Mulyati Mulyati Nanda Putra, Wafig Paray Theo Lonando Perdana, Meilyana Winda Pratama, Ferdianto Putri, Intan Reno Saputra, Zulhipni Rusdin Rusdin Rustina Rustina Safira, Vindy Rizky Putri Sari, Nurlayli Indah Sobrina, Khoirunnisa Nabila Sri Primaini Sudarsono, Edi Sumarni Sumarni Suzanna, Suzanna Syafi'ul Hamidani Syamsiar, Syamsiar Tegriansyah, Aidil Fadli vebriandi, M.Yazed Wahidi, Kemala Rita Wempy Pirgana Wibowo, Prasetyo Sakti Yuniza Yuniza Zulhipni Reno Saputra Els