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All Journal Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat sudo Jurnal Teknik Informatika
Dinata, Hane Yorda
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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The Training of Implementing Artificial Intelligence, Machine Learning and Big Data in Cloud Computing Suranegara, Galura Muhammad; Jayadinata, Asep Kurnia; Nikawanti, Gia; Ichsan, Ichwan Nul; Ash Shiddiq, Reza Nurfaudzan; Faudzan, Muhammad Iqbal; Syaifullah, Muhammad Wildan; Kheqal, Abdul; Dinata, Hane Yorda
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 6, No 1 (2025): Reka Elkomika
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v6i1.48-56

Abstract

Disruption is an inevitable consequence of rapid technological advancements. It occurs when existing human resources struggle to keep pace with the swift evolution of technology. One effective way to address this issue is by organizing training sessions aimed at enhancing skills. The Telecommunications Systems Study Program successfully held a training event designed to improve participants' skills, particularly in technology. The participants, including students from vocational high schools (SMK), expressed high levels of satisfaction with the event. The topics covered ranged from cloud computing, artificial intelligence, machine learning, to big data. The training did not merely provide theoretical knowledge but also included practical applications. This community service activity was organized by students serving as the event infrastructure team, with faculty members leading the event as the organizing committee. The participants were students from ten different vocational high schools (SMK) and took place over a period of one day, from 8 AM to 3 PM. Thanks to this training method, the student development significance value reached 33%.
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.
Psychoacoustically-Weighted Adaptive Digital Filtering for Enhanced Speech Quality and Audio Size Efficiency Dinata, Hane Yorda; Laksita, Eldyana Citra
sudo Jurnal Teknik Informatika Vol. 5 No. 1 (2026): Edisi Maret
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/sudo.v5i1.1373

Abstract

Balancing perceptual quality with computational efficiency remains challenging in speech enhancement systems. This research presents an adaptive filtering framework integrating psychoacoustic modeling with multi-stage noise reduction. The architecture combines spectral subtraction and Wiener filtering, modulated by Bark-scale perceptual weighting derived from critical band theory. Unlike conventional approaches, the system exploits frequency-dependent auditory sensitivity to concentrate processing on perceptually salient regions while reducing representation of masked components. Experimental validation across diverse acoustic conditions yielded an average SNR improvement of 4.2 dB over baseline techniques, with simultaneous 31.7% file size reduction through psychoacoustically-guided quantization. PESQ assessment produced a mean opinion score of 4.23, confirming excellent quality preservation. Convergence analysis revealed 23% faster adaptation attributed to perceptually-weighted cost functions. Robustness testing across white noise, babble, and environmental sounds demonstrated consistent performance with minimal variance, indicating strong generalization capability. These findings show that incorporating human auditory principles simultaneously improves perceptual quality, computational efficiency, and system adaptability—critical for bandwidth-constrained applications in mobile communications, streaming platforms, and assistive devices
Optimized Hybrid CLDNN Architecture with Enhanced Temporal-Spatial Feature Extraction for Robust Automatic Modulation Classification in Cognitive Radio Networks Alifi, Daryan Pratama; Dinata, Hane Yorda; Suranegara, Galura Muhammad; Ichsan, Ichwan Nul
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.28935

Abstract

Automatic Modulation Classification (AMC) is a pivotal technology for efficient spectrum management in future cognitive radio networks. While Deep Learning has advanced the field, standard Convolutional Neural Networks (CNN) often struggle to capture long-term temporal dependencies in signals affected by fading. This study proposes an Optimized Hybrid CLDNN architecture that integrates a "Wide-Kernel" CNN (k=7) for enhanced spatial feature extraction and a "High-Capacity" LSTM (100 units) for robust temporal modeling. Experimental validation using the RadioML 2016.10a dataset demonstrates that the proposed optimizations yield significant performance gains. Specifically, the model achieves a classification accuracy of 84.5% at 0 dB SNR, outperforming standard baselines in the critical transition regime. Furthermore, it reaches a peak accuracy of 92.4% at high SNR (+18 dB). A notable finding is the reduction of inter-class confusion between 16-QAM and 64-QAM, where the misclassification rate is suppressed to approximately 15%, confirming the architecture's effectiveness in resolving hierarchical modulation ambiguities in dynamic wireless environments.
Co-Authors Alifi, Daryan Pratama Ash Shiddiq, Reza Nurfaudzan Faudzan, Muhammad Iqbal Galura Muhammad Suranegara Ichwan Nul Ichsan Jayadinata, Asep Kurnia Kheqal, Abdul Laksita, Eldyana Citra Nikawanti, Gia Syaifullah, Muhammad Wildan