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All Journal Emerging Science Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal INFOTEL International Journal of Electrical Engineering and Intelligent Computing
Eueung Mulyana
School Of Electrical Engineering And Informatics, Institut Teknologi Bandung, Bandung 40132
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science

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Studi Literatur dari Kompresi Video Berbasis Pembelajaran Kholidiyah Masykuroh; Eueung Mulyana
JURNAL INFOTEL Vol 15 No 3 (2023): August 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i3.943

Abstract

Developments in telecommunications technology today, such as cellular with the fifth generation (5G), the development of IoT prototypes, and the migration of analog TV to digital TV starting in 2022. The development of various research using machine learning. The problem with video format information is that the video file size is quite large, so the transmission process requires a large bandwidth. In addition, sharing services such as Video on Demand (VoD) and Video Broadcasting are sensitive to delay. In comparison, the transmission media has limited capacity, such as terrestrial TV, Ethernet/Fast Ethernet, and wireless cellular data such as 2G, 3G HSPA, 4G, etc. Based on reports from Cisco, the development of internet users has increased by 10% per year, with 80% of total traffic using video. Developments in various video compression standards, such as the most recent H.264 and H.265, produce high-quality, low-bitrate video. Much research has been carried out with various proposed compression methods based on machine learning. Either uses singular block learning based or end-to-end. This research focuses on the literature study of video compression with machine learning.
Analisis Kinerja Sistem Pemantauan Berbasis Streaming Telemetry gNMI dengan Simulasi Jaringan Containerlab Fierda Kurniacahya Ariefputra; Eueung Mulyana
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 2: Mei 2024
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v13i2.10185

Abstract

The rapid growth of the Internet has impacted the digital service development. This surge in demand has created opportunities for digital service industry players. Despite its positive impact, the growth of the Internet also poses technical challenges. In managing the increasing data traffic, resource monitoring plays a vital role. One of the latest methods for monitoring these resources is the utilization of the Google’s Remote Procedure Call (gRPC) Network Management Interface (gNMI) streaming telemetry system. While it seems superior to current protocols, there is a need for further exploration into the implementation of streaming telemetry systems. This paper specifically investigates the trade-offs and performance of gNMI streaming telemetry. The design and simulation were conducted utilizing containerlab, a Docker-based networking lab tool. In the Docker-based simulation, integration between the monitoring system and network topology was implemented. The results from observing each protocol indicate that the monitoring system’s metric retrieval activity had minimal impact on network conditions. This is evident in the consistently low average network latency and nearly uniform throughput, except in instances of packet loss and congestion. Simulation observations indicate that the gNMI monitoring system utilized input/output (I/O) resources more intensively compared to other protocols. The research also examined the integration of gNMI streaming telemetry and log monitoring, revealing a 70 MB rise in memory usage and a 33% increase in Disk I/O resources. Furthermore, the study uncovered signs of a 50% increase in CPU utilization by the gNMI monitoring system compared to the average data recorded in the observations.
Performance Evaluation of Significant Feature for Interest Flooding Attack Detection on Named Data Networking Jupriyadi; Syambas, Nana R.; Mulyana, Eueung
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-07

Abstract

One of the internet architectures of the future that has advantages over the current system is Named Data Networking (NDN). However, Denial of Service (DoS) attacks, such as interest flooding attacks (IFA), can still disrupt the network. Detecting IFA attacks is crucial for preventing further damage. Several approaches to detection systems have been proposed, including a classification approach to detecting attacks with multiple detection parameters or features. However, the many detection system features that can be extracted from the network result in longer computation times for the classification algorithms. This research focuses on enhancing the detection of IFA by evaluating the features of the detection system and identifying significant features to improve detection accuracy and reduce computation time. We employed various feature selection algorithms, including information gain, wrapper naive Bayes, gain ratio, and correlation-based feature selection (CFS). The selected features are tested to detect attacks using several classification algorithms, including naive Bayes, random forest, J48, and Bayesian network. Our proposed method found only three essential features for detecting IFA from 18 features available, resulting in better detection accuracy and increasing by 47.8% the time to build the model. This study enhances NDN security while reducing computational cost, making real-time attack detection more feasible.
Performance Visualization of Southbound Interface in Software Defined Networking Fahrizal Djohar; Eueung Mulyana; Suciana Suciana; Andi Muhammad Ilyas; Muhammad Natsir Rahman; Achmad Prajudin Sardju
International Journal Of Electrical Engineering and Inteligent Computing Vol 1, No 1 (2023): International Journal of Electrical Engineering and Intelligent Computing
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/ijeeic.v1i1.6926

Abstract

Software Defined Networking (SDN) makes Internet network configuration easier by separating the control plane and data plane. The control plane on the controller has information on network devices in the data plane and centrally control these devices. One of the controllers in SDN being developed is the Open Network Operating System (ONOS). ONOS provides interfaces such as Representational State Transfer (REST) Application Programming Interface (API). The ONOS core REST API provides some information from the network connected to it, such as devices, statistics, and the information in JSON file. The primary objective of this study is to develop an interface that simplifies performance monitoring through graphical representation. This involves testing the visualization with various topologies and conducting a comparative analysis of the visualization results across these topologies. The creation of the interface entails presenting statistical data, available in the form of a JSON file from the ONOS controller via the REST API, on the web interface in graphical format. The resulting visualization generates a graph that aligns with the performance characteristics of each topology, reflecting device details, ports, and additional parameters such as the count of sent and received packets, as well as sent and received bytes. The performance visualization outcomes specific to each topology are consistent with the number of connections and are prominently displayed on the web interface. Additionally, this research evaluates network throughput and bandwidth by sending ICMP packet and iperf tests across each topology. Among all the openflow tests performed on various network topologies, it was observed that the tree topology exhibited the lowest network capacity utilization, followed by the leaf-spine topology, and finally the ring topology.
QLAF: Q-Learning Adaptive Forwarding for Disaster Emergency Communication in Named Data Networking Ratna Mayasari; Galih N. Nurkahfi; Nana R. Syambas; Eueung Mulyana
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-03

Abstract

Reliable communication is essential for effective disaster response; however, conventional IP-based networks often fail when the network infrastructure is damaged. Disaster communication networks need adaptive forwarding strategies that maintain reliability under rapid topology changes, various link qualities, and resource constraints. This research proposes a Q-Learning-based Adaptive Forwarding (QLAF) strategy designed to enhance reliability in heterogeneous disaster emergency communication networks. QLAF implements reinforcement learning into the NDN forwarding plane, enabling each router to autonomously learn optimal forwarding faces based on multiple performance metrics: Round-Trip Time (RTT), throughput, and link stability. The proposed strategy was implemented in the Named Data Networking Forwarding Daemon (NFD) and evaluated using the MiniNDN emulator over a BRITE-generated 25-node disaster topology that integrates terrestrial, cellular, and satellite links. We compared QLAF and Adaptive Smoothed RTT-based Forwarding (ASF), Access strategy, and Self-Learning. Experimental results show that QLAF achieves a Packet Delivery Ratio (PDR) of 99.91%. These results show that QLAF gives a robust solution for reliability-sensitive disaster communication, guaranteeing high data delivery performance under unstable network conditions. However, its latency overhead limits its applicability to real-time scenarios.
Co-Authors Achmad Prajudin Sardju Andi Muhammad Ilyas Fahrizal Djohar Fierda Kurniacahya Ariefputra Galih N. Nurkahfi Jupriyadi Kholidiyah Masykuroh Muhammad Natsir Rahman Nana R. Syambas Ratna Mayasari Suciana Suciana Syambas, Nana R.