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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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Optimizing Connectivity and Network Management with SDN Technology on VANET Using the SSF Method Dinata, Hane Yorda; Suranegara, Galura Muhammad
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 1 (2025): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i1.2867

Abstract

Vehicular Ad-Hoc Networks (VANET) represent a crucial innovation in transportation technology, enabling vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, VANET faces challenges such as signal fluctuations, data security issues, and high mobility, which affect network reliability. This study aims to optimize connectivity and network management in VANET using the Strongest-Signal-First (SSF) method supported by Software-Defined Networking (SDN). The research was conducted through simulations using Mininet-WiFi. The system was designed with two vehicles and four access points to evaluate the performance of the SSF method, focusing on quality of service (QoS) parameters such as data transfer, jitter, packet loss, and bandwidth. Data were collected over a 30-second simulation under varying bandwidth conditions. The results demonstrate that the SSF method effectively maintains communication reliability, achieving a maximum packet loss of only 0.05% and an average data transfer rate of 285 – 324 kB. However, the effects of fading and network dynamics caused fluctuations in minimum transfer rates (102 – 114 kB) and jitter (0.1 – 1.0 ms), particularly at lower bandwidths. The SSF method has proven to enhance communication stability in VANET. Nevertheless, challenges such as fading and high mobility require additional mechanisms to further improve network performance in dynamic environments.
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.
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