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All Journal JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab InComTech: Jurnal Telekomunikasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Cerdas TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Journal of Information Systems and Informatics Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) INTEGRITAS : Jurnal Pengabdian Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Journal of Computer Networks, Architecture and High Performance Computing REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Journal La Multiapp KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Locus Penelitian dan Pengabdian Paradigma EPSILON: Journal of Electrical Engineering and Information Technology J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Eduvest - Journal of Universal Studies Ranah Research : Journal of Multidisciplinary Research and Development Jurnal INFOTEL Telecommunications, Networks, Electronics, and Computer Technologies (TELNECT) Integrated : Journal Of Information Technology and Vocational Education Jurnal Abmas
Galura Muhammad Suranegara
Universitas Pendidikan Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Neuroscience Public Health Social Sciences Transportation Other

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Hyperparameter Tuning of XGBoost for Flooding Attack Detection in SDN-based Vehicular Ad Hoc Networks (VANETs) under Limited Resources Chairunisa Rahma Putri; Galura Muhammad Suranegara; Ichwan Nul Ichsan
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i1.3510

Abstract

Software-Defined Network (SDN) based Vehicular Ad Hoc Network (VANET) infrastructure network enables centralized vehicle control. However, due to its centralized nature, SDN-based VANET is vulnerable to flooding attacks such as Distributed-Denial of Services (DDoS) or Denial of Service (DoS) attacks that can disrupt network availability and endanger traffic safety. This study aims to detect flooding attacks using the Extreme Gradient Boosting (XGBoost) algorithm with a focus on hyperparameter tuning in a limited computing environment to find optimal hyperparameter values for the model. This study uses basic Google Colab with 12 GB RAM with a total dataset of 431,371 entries. The results obtained from this study conclude that hyperparameter tuning achieves optimal performance at n_estimators = 150 and max_depth = 15, resulting in 99.97% accuracy, 99.99% precision, 99.97% recall, and 99.98% F1 score, which proves the effectiveness of the model in detecting flooding attacks. The novelty of this study lies in the application and evaluation of hyperparameter tuning on the XGBoost algorithm in a resource-constrained environment to improve attack detection in SDN-VANET.
Performance Limit of Handcrafted Features in Cassavia LSB Steganalysis Mukhamad Salman Nurdin; Endah Setyowati; Galura Muhammad Suranegara
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 2 (2026): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i2.3983

Abstract

Handcrafted feature based steganalysis remains widely used in resource constrained environments despite rapid progress in deep learning detectors. This study investigates the performance limit of compact handcrafted features for binary classification of cover and stego images in Least Significant Bit (LSB) steganalysis on the Cassavia dataset. Five representative models deep neural network (DNN), one dimensional convolutional neural network (1D CNN), random forest, Light Gradient Boosting Machine (LightGBM), and SMOTE enhanced DNN are trained on 44,000 images using 16 descriptors that combine statistical LSB measures with a reduced subset of Spatial Rich Model (SRM) residual features. All models converge to a narrow accuracy band of 72.58-75.50% with Area Under Curve (AUC) values close to 0.50 and pronounced overfitting in the training–validation curves, indicating that the dominant bottleneck arises from limited feature expressivity rather than model capacity or implementation errors. Feature importance analysis further reveals that only a small subset of descriptors contributes substantially, exposing strong redundancy in the handcrafted feature set. Within this CPU friendly LSB based setting, these results establish a practical performance ceiling that is shared across both classical and deep models, while highlighting LightGBM as an attractive option for embedded steganalysis and motivating future hybrid designs that combine handcrafted statistical priors with learned deep representations.
A Comparative Study of Dynamic Load Balancing Algorithms for Microservices in Heterogeneous Multi-Cloud Environments Domma Uli Sitinjak; Galura Muhammad Suranegara
Eduvest - Journal of Universal Studies Vol. 6 No. 1 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i1.52467

Abstract

Microservices-based application architectures in cloud environments require load balancing mechanisms that can adapt to differences in server capacity and workload fluctuations. This study aims to evaluate the performance of dynamic load balancing algorithms—Least Connection, Weighted Least Connection, and Least Response Time—in heterogeneous server environments using Amazon Web Services (AWS) and Google Cloud Platform (GCP). The evaluation was conducted through staged testing by observing application performance based on p95 latency, throughput, error rate, and load distribution patterns. The results indicate that no single algorithm consistently outperforms the others across all scenarios and platforms. Weighted Least Connection tends to produce a more proportional load distribution according to server capacity, while Least Connection and Least Response Time are more influenced by the number of active connections and initial response time. Overall, both AWS and GCP are able to maintain application performance stability across all load levels. These findings confirm that the effectiveness of dynamic load balancing algorithms in heterogeneous cloud environments is influenced by workload characteristics and server capacity, indicating that algorithm selection should be tailored to the specific system objectives.
STUDI KINERJA ALGORITMA LOAD BALANCING STATIS PADA INFRASTRUKTUR HOMOGEN TERHADAP APLIKASI WEB MICROSERVICES Alysha Namora Putri Harahap; Galura Muhammad Suranegara
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7029

Abstract

Technological developments have led to the emergence of cloud computing as a solution to improve system reliability and scalability. One of its components, load balancer, plays an important role in distributing the workload to keep the system optimal, so choosing a load balancing algorithm is a crucial step. This study tested several static load balancing algorithms in a homogeneous server environment based on microservices architecture on two cloud platforms, namely AWS and GCP. With the current experimental design, the tests showed that no algorithm consistently excelled in all scenarios, while AWS tended to display more stable performance than GCP. These results can serve as a basis for further research exploring variations in configuration, load scenarios, or larger system scales to evaluate algorithm and platform performance more comprehensively.
Efficient Image Transmission for Autonomous Systems Using Residual Dense Feature Networks Over LoRa Networks Muhamad Fadly Rizqy Praptawilaga; Galura Muhammad Suranegara; Arief Suryadi Satyawan
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 1 (2025): March 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i1.7584

Abstract

Autonomous systems face challenges in transmitting high-quality images over bandwidth-constrained networks like LoRa, which operates at data rates of 0.3–50 kbps. This study proposes the Residual Dense Feature Network (RDF Net), a super-resolution model designed to optimize image transmission within the constraints of LoRa networks. By leveraging Contrast-Aware Channel Attention (CCA), Enhanced Spatial Attention (ESA), Blueprint Separable Convolution (BSConv), and a progressive approach, RDF Net achieves 20x upscaling, enabling low-resolution images (40x40 pixels) to be reconstructed into high-resolution outputs (800x800 pixels) on a central server. Experimental evaluations demonstrate that Model-4, combining CCA and ESA, delivers state-of-the-art perceptual quality and structural fidelity, while Model-3, using ESA, offers a computationally efficient alternative for resource-constrained scenarios. Simulations of LoRa’s bandwidth limitations reveal that transmitting a single 40x40 image requires approximately 0.208–0.56 seconds at a data rate of 50 kbps. While this demonstrates the feasibility of near real-time communication, the trade-off between latency and visual fidelity remains a critical consideration, particularly for latency-sensitive applications. These findings underscore RDF Net’s potential to address the challenges of high-quality visual communication in bandwidth-constrained environments, paving the way for enhanced autonomous system applications. Further optimization, including adaptive compression strategies, and testing on actual LoRa hardware are recommended to validate its performance in real-world scenarios and explore its applicability to diverse autonomous systems.
AODV Routing Optimization in Wireless Mesh Networks Using SDN-Inspired Control and ETX-Driven Machine Learning Weight Adaptation Mochamad Yusril; Galura Muhammad Suranegara
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12737

Abstract

Wireless Mesh Networks (WMNs) require adaptive routing to sustain Quality of Service (QoS) under dynamic conditions, yet conventional AODV is limited by its reliance on hop count, which does not reflect actual link quality. This study proposes a hybrid framework integrating AODV, SDN-inspired centralized control, and Machine Learning (ML)-based weight optimization using the Expected Transmission Count (ETX). The model was evaluated in NS-3 (v3.45) through a four-scenario ablation study with 30 repetitions per scenario and OLSR as a baseline. A Gradient Boosting model trained on 885 samples generated routing weights based on seven QoS-related features. The results show that the proposed method significantly improves performance compared to standard AODV, with throughput increasing by 32.71%, delay decreasing by 40.19%, and routing overhead reduced by 38.92%, all statistically significant. The model achieved high predictive accuracy (R² = 0.9929) without overfitting, with ETX emerging as the most influential feature. Overall, the integration of SDN control and ML optimization enhances routing efficiency, stability, and adaptability in WMNs, offering strong potential for IoT and smart city applications.
Design and Build a Machine Learning-Based Brute-Force Login Early Detection System with Adaptive Risk Scoring and Automated Actions Tubagus Setyo Mulyatama; Galura Muhammad Suranegara
Eduvest - Journal of Universal Studies Vol. 6 No. 8 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i8.53456

Abstract

Brute-force login attacks pose a significant cybersecurity threat to information systems, potentially leading to unauthorized access and data loss. This research designs and builds an early detection system for brute-force login attacks by integrating a machine learning-based classification model, an adaptive risk scoring mechanism, and automated response actions. The Random Forest model was trained using login attempt log data collected from a controlled test website over two months with 100 dummy accounts, resulting in 30,000 entries comprising 17,000 normal, 5,000 suspicious, and 8,000 attack data samples. The adaptive risk scoring mechanism combines the ML model output with four contextual indicators frequency of failed attempts, access time, IP location, and user agent to generate a dynamic risk score, enabling the system to make decisions based on multiple indicators rather than a single factor. Evaluation results demonstrate 96.5% accuracy, 95.8% precision, and 97.2% recall in three-class classification. Ablation studies confirm that the integration of adaptive risk scoring improves accuracy from 91.2% (RF only) to 96.5% (RF with all indicators). The system successfully blocked 8 IP addresses identified as attacking sources, achieving a false positive rate of 0.7% and an average response time of 42 milliseconds. This study concludes that the combination of Random Forest, adaptive risk scoring, and automated actions provides an effective and comprehensive approach for the early detection of brute-force login attacks.
Analisis Quality of Service pada Implementasi Multi VPN Tunnel dalam Infrastruktur SD -WAN Feny Indriany; Galura Muhammad Suranegara
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 6 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i6.5919

Abstract

Perkembangan kebutuhan komunikasi data antar lokasi mendorong penerapan SD-WAN sebagai solusi untuk meningkatkan fleksibilitas dan efisiensi pengelolaan jaringan. Dalam implementasinya, SD-WAN memanfaatkan VPN sebagai media komunikasi antar site melalui jaringan internet. Namun perbedaan mekanisme komunikasi pada VPN WireGuard, Tailscale, dan ZeroTier berpotensi menghasilkan performa jaringan yang berbeda. Penelitian ini bertujuan mengimplementasikan multi VPN tunnel menggunakan WireGuard, Tailscale, dan ZeroTier pada infrastruktur SD-WAN serta menganalisis performa berdasarkan parameter QoS yang meliputi throughput, latency, jitter, dan packet loss. Metode penelitian yang digunakan adalah metode eksperimen dengan melakukan implementrasi ketiga VPN pada lingkungan SD-WAN, kemudian dilakukan pengujian menggunakan Ping, iPerf3, dan Wireshark. Hasil pengujian menunjukan bahwa seluruh VPN berhasil membangun komunikasi antar site dengan baik dan menghasilkan packet loss 0%. Pada parameter throughput, Tailscale dan ZeroTier memperoleh nilai tertinggi sebesar 99 Mbps, sedangkan WireGuard memperoleh 95 Mbps. Namun, WireGuard menunjukkan performa terbaik pada parameter latency 4 ms dan jitter 2 ms, lebih rendah ZeroTier latency 7 ms dan jitter 27 ms, serta Tailscale latency 17 ms dan jitter 60 ms. Berdasarkan hasil penelitian, WireGuard menjadi VPN yang paling optimal untuk implementasi SD-WAN karena mampu memberikan kualitas komunikasi yang lebih stabil dengan latency dan jitter yang lebih rendah.
Co-Authors A.A. Ketut Agung Cahyawan W Abd. Rasyid Syamsuri Ade Gafar Abdullah, Ade Gafar Afina Carmelya, Anindya Agnesia, Gisella Agustin, Sarah Ahmad Fauzi Ahmad Fauzi Alysha Namora Putri Harahap Arief Suryadi Satyawan Ash Shiddiq, Reza Nurfaudzan Avia Aulia Faridah, Tsabitah Basuki, Akbari Indra Chairunisa Rahma Putri Dani Prasetyo Adi, Puput Daryan Pratama Alifi Dewi Dewi, Tarisa Citra Dianti, Palda Puspita Diky Zakaria Dinata, Hane Yorda Domma Uli Sitinjak Dwi Wahyu Lestariningsih Dwitami, Ghalda Azzahra Edgard Altamerano Ferdinand Elysa Nensy Irawan Endah Setyowati Endah Setyowati Excel Thrive Valerian Haryanto Fadhila, Esa Noer Fadillah, Khoerunnisa Nur Faudzan, Muhammad Iqbal Fauzi Ahmad Muda Fauzi, Sadam Fauziyah Rhaudhatul Jannah Feny Indriany Fujiyanti, Vina Gumelar, Ega Restu Hadi Putri, Dewi Indriati Hafiyyan Hamdani, Nizar Alam Hane Yorda Dinata Helfy Susilawati Ichwan Ichwan Nul Ichsan Istiqomah, Mutia Jamilah, Nur Asy-Syifa Jannah, Fauziyah Rhaudhatul Jayadinata, Asep Kurnia Kheqal, Abdul Laili, Adisty Nurrahmah Larasati, Sifa Liptia Venica Maranatha, Jojor Renta Meiliya Cahya Yustina Mirza Etnisa Haqiqi, Mokhamamad Mochamad Yusril Muhamad Fadly Rizqy Praptawilaga Muhammad Gani Baihaqi Darussalam Muhammad Husni Muttaqin Muhammad Raihan Ramadhan Mukhamad Salman Nurdin Nikawanti, Gia Paramaputra, Arya Pandya Pebriyanti, Cahyani Perkasa, Aldewo Dillon Pratama, Hafiyyan Putra Pratama, Herdi Rizky Putri, Adhwa Alifia Putri, Nova Nurul Rahman, Fadhila Dwi Restyasari, Nissa Reza Nurfaudzan Ashsiddiq Rifki Destrizal Nugraha Sanada, Pratiwi Subagja, Banda Suprih Widodo, Suprih Suryadi Satyawan, Arief Syaifullah, Muhammad Wildan Syifaul Fuada Tubagus Setyo Mulyatama Ulfa, Husnul Viona Mojang Pamungkas Vormes Gema Merdeka Wendha Alfen Pratama winda pratiwi Yovanka, Diva Nuranty Zaelani, Cahya