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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Teknik Informatika (JUTIF)
Ahmad Heryanto, Ahmad
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Visualization of Granblue Fantasy Game Traffic Pattern Using Deep Packet Inspection Method Stiawan, Deris; Prabowo, Christian; Heryanto, Ahmad; Afifah, Nurul; Minarno, Agus Eko; Budiarto, Rahmat
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 3, August 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v5i3.1073

Abstract

Granblue Fantasy is one of Role Playing Games (RPG). It’s a video role-playing game developed by Cygames. This research to observes the Granblue Fantasy Game. The purpose is to analyze the traffic data of the Granblue Fantasy to find the pattern using Deep Packet Inspection (DPI), Capturing the Data Traffic, Feature Extraction Process and Visualize the Pattern. The Pattern are Gacha, Solo Raid, Casino and Multiraid. This research demonstrate that Multiraid battle has more data than other pattern with TTL 237.
The incorporation of stacked long short-term memory into intrusion detection systems for botnet attack classification Heryanto, Ahmad; Stiawan, Deris; Hermansyah, Adi; Firnando, Rici; Pertiwi, Hanna; Bin Idris, Mohd Yazid; Budiarto, Rahmat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3657-3670

Abstract

Botnets are a common cyber-attack method on the internet, causing infrastructure damage, data theft, and malware distribution. The continuous evolution and adaptation to enhanced defense tactics make botnets a strong and difficult threat to combat. In light of this, the study's main objective was to find out how well techniques like principal component analysis (PCA), synthetic minority oversampling technique (SMOTE), and long short-term memory (LSTM) can help find botnet attacks. PCA shows the ability to reduce the feature dimensions in network data, allowing for a more efficient and effective representation of the patterns contained. The SMOTE addresses class imbalances in the dataset, enhancing the model's ability to recognize suspicious activity. Furthermore, LSTM classifies sequential data, understanding complex network patterns and behaviors often used by botnets. The combination of these three methods provided a substantial improvement in detecting suspicious botnet activities. We also evaluated the effectiveness using performance metrics such as accuracy, precision, recall, and F1-score. The results showed an accuracy of 96.77%, precision of 88.95%, recall of 88.58%, and F1-score of 88.64%, indicating that the proposed model was reliable in detecting botnet traffic compared to other deep learning models. Furthermore, LSTM can classify sequential data, understanding complex network patterns and behaviors often used by botnets.
Haystack-based Facebook’s data storage architecture: store, directory, and cache Sutikno, Tole; Heryanto, Ahmad; Ahmad, Laksana Talenta
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp671-681

Abstract

Haystack is Facebook's unique way of managing large amounts of user-generated content like photos. The architecture prioritizes performance, reliability, and scalability to overcome network-attached storage system bottlenecks. Haystack speeds data access and ensures data integrity during hardware failures by using physical and logical volumes. This study examines the architecture of Facebook's Haystack data storage system and its effects on scalability and efficiency in handling large photo data. According to the study, the store, directory, and cache functions work together to reduce input/output (I/O) operations and improve metadata processing, which traditional network-attached storage systems cannot do. Haystack manages massive photo data storage and retrieval, solving network-attached storage (NAS) limitations. It balances throughput and latency by minimizing disk operations and optimizing metadata processing. Each store, directory, and cache contribute to this ecosystem. The Haystack architecture reduces disk operations and metadata processing bottlenecks with distributed caching. A cache allows instant access to frequently requested images and balances read and write operations across the system. We should study advanced storage system architectures based on Facebook's Haystack architecture. This could involve investigating faster metadata processing algorithms, using artificial intelligence (AI) to improve fault detection and repair systems, and assessing the economic impact of distributed caches.
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.
HIGH AVAILABILITY IN SERVER CLUSTERS BY USING BACKPROPAGATION NEURAL NETWORK METHOD Heryanto, Ahmad; Gunanta, Aditya
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 4 No. 1 (2021): Jurnal Teknologi dan Open Source, June 2021
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v4i1.936

Abstract

Server is a host device applications to serve every request in finding information needs. The server must fully support the services used for the organization's digital needs 24 hours in a day, 7 days in a week, and 365 days in a year. The concept of High Availability is needed to maintain the quality of server services. The algorithm used to build HA can use both classical and modern algorithms. The algorithm used in this research is using backpropagation neural network. In this study, the parameter values to obtain optimal accuracy are learning rate 0.1, training data 80 and test data 20, the number of nodes in hidden layer 4, minimum error 0.0001, and the number of iterations 2500.The best accuracy value using these parameters is 93.79% .
Co-Authors Adi Hermansyah, Adi Agus Eko Minarno Ahmad, Laksana Talenta Audrey, Berby Febriana Bin Idris, Mohd Yazid Deris Stiawan Firnando, Rici Gunanta, Aditya Idris, Mohd Yazid Bin Iman Saladin B. Azhar Muhammad Afif Pertiwi, Hanna Prabowo, Christian Rahmat Budiarto Setiawan, Deris Tole Sutikno Yundari, Yundari