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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Teknotan: Jurnal Industri Teknologi Pertanian Bulletin of Electrical Engineering and Informatics PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic AGRIEKONOMIKA Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Kultivasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Agrotek Tropika Journal of Tropical Crop Science Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Community Services Cel International Journal of Applied Mathematics, Sciences, and Technology for National Defense Jurnal Pengabdian Masyarakat Nasional Journal Collabits
Rahmat Budiarto
Department Of Agronomy, Faculty Of Agriculture, Universitas Padjadjaran, Sumedang, 45363, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
Security and Performance Evaluation of PPTP-Based VPN with AES Encryption in Enterprise Network Environments Heryanto, Ahmad; Setiawan, Deris; Audrey, Berby Febriana; Hermansyah, Adi; Afifah, Nurul; Azhar, Iman Saladin B.; Idris, Mohd Yazid Bin; Budiarto, Rahmat
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4818

Abstract

In the context of the current digital era, Virtual Private Networks (VPNs) serve a critical function in ensuring the confidentiality and integrity of data transmitted across public networks, particularly within corporate environments. This study presents a comprehensive analysis of VPN security and performance, with a specific focus on the Point-to-Point Tunneling Protocol (PPTP) and the implementation of encryption algorithms such as AES-128 and AES-256. Despite the widespread adoption of PPTP due to its simplicity and broad compatibility, it exhibits significant security vulnerabilities, primarily stemming from its reliance on the outdated RC4-based Microsoft Point-to-Point Encryption (MPPE) and the susceptible MS-CHAP authentication protocol, which is highly vulnerable to brute-force and dictionary attacks. Empirical findings indicate that, although AES-128 and AES-256 introduce minor performance trade-offs compared to unencrypted configurations, AES-256 demonstrates markedly enhanced security, achieving a 98.9% authentication success rate and a threat detection time of 122 milliseconds. Nevertheless, increased user load adversely impacts network performance, with throughput declining from 95 Mbps to 40 Mbps as the user count rises from 5 to 50, accompanied by elevated latency and packet loss. Comparative analysis across three encryption scenarios AES-128, AES-256, and MPPE-PPTP reveals a consistent degradation in network performance as user load increases, with AES-256 offering the strongest security at the cost of slightly reduced throughput and increased latency under high-load conditions. MPPE-PPTP, while providing better throughput, lacks adequate security, making it unsuitable for high-risk environments. Based on these observations, this study recommends the implementation of AES-256 encryption in enterprise networks requiring high security, supported by continuous performance monitoring and strategic capacity planning. Furthermore, the adoption of a secure site-to-site VPN architecture is proposed to facilitate reliable and secure communication between geographically distributed office locations.
STATE OF THE ART ANALYSIS ON BATTERY-RELATED THREATS AND DEFENSES OF IOT DEVICES USING KITCHENHAM Azka Ghafara Putra Agung; Aditya Pradana; Rahmat Budiarto
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1567

Abstract

The Internet of Things (IoT) keeps growing in size every year, but its growth also companied with threats to its security. This paper centers on the research article that focuses on various attacks on IoT system and devices through power drain techniques targeting IoT devices. This paper discusses various existing attack models, and security model. The main objective is to reveal the state of the art of the security issues of IoT related to attacks to the devices’ power. The literature review is performed by implementing Kitchenham method and utilizing Google Scholar and Science Direct databases. 42 publications between 2010 and 2023, fulfilling the selection criteria are selected and comprehensively reviewed. To counteract power drain-induced Denial of Service (DoS) threats, the paper evaluates existing defense mechanisms specifically tailored to mitigate these attacks. These defenses encompass adaptive power management strategies, hardware-level security enhancements, and network-level security measures. The effectiveness, practicality, and trade-offs of these defense mechanisms are examined. The combination of these papers offers comprehensive insights into battery-related security concerns in the IoT landscape, with sleep deprivation attacks, Denial of Service-induced battery drain, and Vampire attack, highlighting the importance of robust security measures in the IoT ecosystem.
Deep Learning-Based Autism Detection Using Facial Images and EfficientNet-B3 Hasanudin, Muhaimin; Afiyati, Afiyati; Budiarto, Rahmat; Wahab, Abdi; Jokonowo, Bambang; Indrianto, Indrianto; Yosrita, Efy; Hanifah, Nurul Afif
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.2026.7.1.4574

Abstract

This study presents a novel deep learning approach for early detection of Autism Spectrum Disorder (ASD) using facial image analysis. Leveraging the EfficientNet-B3 model, the research addresses limitations in traditional diagnostic methods by autonomously extracting discriminative facial features associated with ASD. A balanced dataset of 2,940 facial images (1,470 autistic and 1,470 non-autistic children) from Kaggle was pre-processed to 200x200 pixels and evaluated under three dataset-splitting scenarios (80:10:10, 70:15:15, and 60:20:20) to assess generalisability. The model, trained with the Adam optimiser over 10 epochs, achieved optimal performance in the 80:10:10 scenario, with 84.67% precision, 84.35% recall, and 84.32% F1 score. Results demonstrate high confidence (>90% probability) in distinguishing autistic from non-autistic individuals on unseen data. The study underscores the potential of integrating deep learning into clinical decision-support systems for ASD detection, offering a robust, scalable, and efficient solution to improve diagnostic accuracy and reduce reliance on manual methods.
Co-Authors Abdi Wahab Abdullakasim, Supatida Adi Hermansyah, Adi Aditya Pradana Afiyati, Afiyati Ahmad Heryanto, Ahmad Ahmed, Ali Siraj Al Aufa, Elfa Muhammad Ihsan Ali Firdaus Alshaflut, Ahmed ANDRIA AGUSTA ANNE NURAINI Anni Yuniarti Anto Saputra, Iwan Pahendra Audrey, Berby Febriana Azka Ghafara Putra Agung Bambang Jokonowo Bedine Kerim, Bedine Bin Idris, Mohd Yazid Deris Stiawan Dikdik Kurnia Dwi Budi Santoso Dwinanda, Syahvan Rifqi Edi Santosa Efendi, Darda Efy Yosrita, Efy Envry Artanti Duidahayu Putri Erik Setiawan Ermatita - Erni Suminar Ezura, Hiroshi Fadlan Atalla Muhammad Fajri, Hauzan Ariq Musyaffa Fakhrudin, Zidan Al Buqhori Fakhrurroja, Hanif Farida Farida Farida Fauziah, Rossita Fiky Yulianto Wicaksono Firnando, Rici Firstina Iswari Ghorbanpour, Mansour Giyarto, Gunes Hadipurnawan Satria Hanifah, Nurul Afif Harjunadi Wicaksono, Harjunadi Haryanto, Yoyon Hauzan Ariq Musyaffa Fajri Hayane Adeline Warganegara, Hayane Adeline Helvi Yanfika Idris, Mohd Yazid Bin Iman Saladin B. Azhar Indah Listiana Indrianto Indrianto Iswari, Firstina Jajang Sauman Hamdani Jatmika, Muhammad O. Juli Rejito Kemahyanto Exaudi Komala, Mega Kus Hendarto, Kus Kusumadewi, Vira Kusumiyati Kusumiyati Luciana Djaya, Luciana M. Miftakul Amin Maolana, Adrian Mochamad Arief Soleh, Mochamad Arief Mohamed Shenify Mohd Yazid Idris Mohd Yazid Idris Mohd. Yazid Idris Mugianto, Dwi Rizki Muhammad Afif Muhammad Rifqi Muhammad Rizki Muhammad, Fadlan Atalla Mutiara, Pipit Nisa, Kahirun Noor Istifadah Nursuhud Nursuhud Nuzulastri, Sari Osman, Mohd Azam Pakpahan, Hansel Arie Pertiwi, Hanna Prasetyo, Lindo Pratita, Dian Galuh Pratomo, Adji Prihandi, Ifan Putra Perdana Prasetyo, Aditya Putri, Azizah Tiara Putri, Dina Putri, Envry Artanti Duidahayu Rahma, Siti Auliya Rahmad, Khozaeni Bin Rahmat, Bayu Pradana Nur Ramadani, Selika Fitrian Reza Maulana Rika Meliansyah Roedhy Poerwanto Rofiq, Muhamad Abdul Rossita Fauziah Rufaidah, Fathi Ruminta Ruminta Salamah, Raisha Nur Samsuryadi Samsuryadi Saputra, Muhammad Ajran Sarmayanta Sembiring Semendawai, Jaka Naufal Setiawan, Deris Shadiq, Jafar Sidabutar, Alex Onesimus SIska Rasiska, SIska Siti Julaeha, Siti Susanto Susanto Syamsul Arifin, M. Agus SYARIFUL MUBAROK Varinto, Irvan Waluyo, Nurmalita Wawan Sutari Wibawa, Rangga Widyastuti, R.A.D. Yanyan Mochamad Yani, Yanyan Mochamad Yaya Sudarya Triana Yazid Idris, Mohd. Yudho Suprapto, Bhakti Yulianto, Fiky Yusti Yusti, Yusti Zulhipni Reno Saputra Els