Journal of ICT Research and Applications <p><img class="imgdesc" src="" alt="" /></p> <p style="text-align: justify;"><em>Journal of ICT Research and Applications</em> welcomes full research articles in the area of Information and Communication Technology from the following subject areas: Information Theory, Signal Processing, Electronics, Computer Network, Telecommunication, Wireless &amp; Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management.</p> <p style="text-align: justify;">Abstracts and articles published on Journal of ICT Research and Applications are available online at ITB Journal and indexed by <a href="">Scopus</a>, <a href=";hl=id">Google Scholar</a>, <a href="">Directory of Open Access Journals</a>, <a href=";colors=7&amp;lang=en&amp;jour_id=167017">Electronic Library University of Regensburg</a>, <a href=";Find=journal+of+ICT+RESEARCH+AND+APPLICATIONS&amp;GetResourcesBy=QuickSearch&amp;resourceTypeName=allTitles&amp;resourceType=&amp;radioButtonChanged=">EBSCO Open Science Directory</a>, <a href="">International Association for Media and Communication Research (IAMCR)</a>, <a href="">MIAR: Information Matrix for the Analysis of Journals Universitat de Barcelona</a>, Cabells Directories, <a href="">Zurich Open Repository and Archive Journal Database</a>, <a href="">Open Academic Journals Index</a>, Indonesian Publication Index and ISJD-Indonesian Institute of Sciences. The journal is under reviewed by Compendex, Engineering Village.</p> <p>ISSN: 2337-5787<br />E-ISSN: 2338-5499</p> <p>Reg. No. 691-SIC-UPPGT-SIT-1963, <a title="Accreditation Certificate" href="">Accreditation No. 164/E/KPT/2021</a></p> <p>Published by the Institute for Research and Community Services, Institut Teknologi Bandung.</p> <p><span style="text-decoration: underline;"><strong>Publication History</strong></span></p> <p><strong>Formerly known as:</strong></p> <ul> <li>ITB Journal of Information and Communication Technology (2007 - 2012)</li> </ul> <p>Back issues can be read online at:</p> <p><span style="text-decoration: underline;"><strong>Scimago Journal Ranking</strong></span></p> <p><a title="SCImago Journal &amp; Country Rank" href=";tip=sid&amp;clean=0"><img src=";title=true" alt="SCImago Journal &amp; Country Rank" border="0" /></a></p> LPPM ITB en-US Journal of ICT Research and Applications 2337-5787 Improving the Performance of Low-resourced Speaker Identification with Data Preprocessing <p>Automatic speaker identification is done to tackle daily security problems. Speech data collection is an essential but very challenging task for under-resourced languages like Burmese. The speech quality is crucial to accurately recognize the speaker’s identity. This work attempted to find the optimal speech quality appropriate for Burmese tone to enhance identification compared with other more richy resourced languages on Mel-frequency cepstral coefficients (MFCCs). A Burmese speech dataset was created as part of our work because no appropriate dataset available for use. In order to achieve better performance, we preprocessed the foremost recording quality proper for not only Burmese tone but also for nine other Asian languages to achieve multilingual speaker identification. The performance of the preprocessed data was evaluated by comparing with the original data, using a time delay neural network (TDNN) together with a subsampling technique that can reduce time complexity in model training. The experiments were investigated and analyzed on speech datasets of ten Asian languages to reveal the effectiveness of the data preprocessing. The dataset outperformed the original dataset with improvements in terms of equal error rate (EER). The evaluation pointed out that the performance of the system with the preprocessed dataset improved that of the original dataset.</p> Win Lai Lai Phyu Hay Mar Soe Naing Win Pa Pa Copyright (c) 2023 Journal of ICT Research and Applications 2023-12-31 2023-12-31 17 3 275 291 10.5614/itbj.ict.res.appl.2023.17.3.1 An Efficient Intrusion Detection System to Combat Cyber Threats using a Deep Neural Network Model <p>The proliferation of Internet of Things (IoT) solutions has led to a significant increase in cyber-attacks targeting IoT networks. Securing networks and especially wireless IoT networks against these attacks has become a crucial but challenging task for organizations. Therefore, ensuring the security of wireless IoT networks is of the utmost importance in today’s world. Among various solutions for detecting intruders, there is a growing demand for more effective techniques. This paper introduces a network intrusion detection system (NIDS) based on a deep neural network that utilizes network data features selected through the bagging and boosting methods. The presented NIDS implements both binary and multiclass attack detection models and was evaluated using the KDDCUP 99 and CICDDoS datasets. The experimental results demonstrated that the presented NIDS achieved an impressive accuracy rate of 99.4% while using a minimal number of features. This high level of accuracy makes the presented IDS a valuable tool.</p> Mangayarkarasi Ramaiah C. Vanmathi Mohammad Zubair Khan M. Vanitha M. Deepa Copyright (c) 2024 Journal of ICT Research and Applications 2023-12-31 2023-12-31 17 3 292 315 10.5614/itbj.ict.res.appl.2023.17.3.2 Quality Analysis of Telemetry Tracking and Command at Ground Stations using the Association Rule Mining Approach <p>LAPAN built several remote ground stations to support the telemetry tracking and command (TTC) system for the LAPAN-A2 and LAPAN-A3 satellites. These remote ground stations are located in Kototabang/KT (West Sumatra), Biak/BK (Papua), Parepare/PR (South Sulawesi), Rumpin/RP, Rancabungur/RB (Bogor, West Java), and Svalbard/SV (Norway). Problems that often arise in the TTC process are telecommands not being sent (commands sent from the ground station to the satellite) or telemetry packages not being received (feedback on telecommands sent by the satellite to the ground station). This research attempted to calculate and analyze the quality of TTC using a data-mining approach, i.e., rule mining. The calculations were performed using five main parameters: satellite name, ground station, azimuth, altitude, and communication status. The research output consisted of a combination of remote ground station parameters that may result in a successful or failed TTC. For the LAPAN-A3 satellite at the Svalbard ground station, 19 failed communication combinations were generated with a dataset of 57,029. Communication failures occur in azimuth and elevation, i.e., areas blocked by obstacles.</p> Rizki Permala Nurrochman Ferdiansyah Nurul Muhtadin Copyright (c) 2024 Journal of ICT Research and Applications 2023-12-31 2023-12-31 17 3 316 335 10.5614/itbj.ict.res.appl.2023.17.3.3 WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things <p>Accurate throughput predictions can significantly improve the quality of experience (QoE), where QoE denotes a network’s capacity to provide satisfactory service. By increasing the results of good throughput predictions, the best strategy can be planned for managing data transmission networks with the aim of better and faster data transmission, thereby increasing QoE. Consequently, this paper investigates how to predict the throughput of wireless sensor networks utilizing multimedia data. First, we conducted a comparative analysis of relevant prior research on the topic of throughput prediction in Multimedia Internet of Things (Multimedia IoT). We developed a throughput prediction framework for wireless sensor networks based on what we learned from these studies using machine learning. The Throughput Prediction Framework identifies historical throughput data and employs these traits to predict throughput. In the final phase, multiple camera nodes and local servers are utilized to test a framework for throughput prediction. Our analysis demonstrates that WSN-IoT predictions are quite precise. For a 1-second time breakdown, the average absolute percentage error for all investigated scenarios ranges from 1 to 8 percent.</p> Rosa Eliviani Yoanes Bandung Copyright (c) 2024 Journal of ICT Research and Applications 2023-12-31 2023-12-31 17 3 336 355 10.5614/itbj.ict.res.appl.2023.17.3.4 A Decoupling Technique for Beamforming Antenna Arrays Using Simple Guard Trace Structures <p>This paper discusses decoupling techniques for suppressing electromagnetic coupling between elements of beamforming antenna arrays. Guard trace structures, which are commonly used for crosstalk reduction on printed circuit board technology, are proposed to be inserted between the array elements for coupling reduction. Two types of guard trace structures, i.e., straight guard traces and serpentine guard traces, were explored, and the effect of using via holes on both types of guard traces was studied. For this purpose, two-element antenna arrays with guard trace structures inserted between array elements were designed and simulated. The simulation results showed that a straight guard trace with vias (straight GTV) and a serpentine guard trace without vias (serpentine GT) could effectively reduce EM coupling between elements of array antennas. To verify the simulation results, prototypes of antenna arrays with straight GTV and serpentine GT were realized and measured. The measurement results showed coupling reductions of 5 dB and 6.4 dB could be achieved when straight GTV and serpentine GT are inserted between two array elements separated by edge-to-edge distances of 4 mm and 9.05 mm, respectively. Therefore, the proposed decoupling technique is suitable for beamforming antenna arrays with a very close distance between array elements.</p> Zulfi Zulfi Joko Suryana Achmad Munir Copyright (c) 2024 Journal of ICT Research and Applications 2023-12-31 2023-12-31 17 3 356 372 10.5614/itbj.ict.res.appl.2023.17.3.5 Utilizing Generative Adversarial Network for Synthetic Image Generation to Address Imbalance Challenges in Chest X-Ray Image Classification <p>Deep learning-based classifiers need lots of image data to train. Unfortunately, not all real-world cases are supported by a huge amount of image data. One of the cases are images for classification of pneumonia infections with chest X-rays images. This study proposes a way of synthesizing chest X-rays with abnormal conditions in order to use the synthesized images for classification purposes. A GAN-based technique can generate synthetic images with greater quality that resemble original images thus can provide a more balanced data distribution than other approaches. To indirectly evaluate the quality of our GAN-based synthetic images, we used CNN-based classification architectures on diverse datasets. Three scenarios examined the effects of synthetic picture categorization. Scenario-1: adding 90% of synthesized images to the original images into the training dataset. Scenario-2: adding 50% of synthesized images to the original images. Scenario-3: adding 10% of synthesized image to the original images. The classification test revealed significantly increased F1 scores in all scenarios. Our study also emphasizes the significance of addressing the problem of imbalanced collections of chest X-ray images and the capability of GANs to alleviate this issue.</p> Nugraha Priya Utama Muhammad Faris Muzakki Copyright (c) 2024 Journal of ICT Research and Applications 2023-12-31 2023-12-31 17 3 373 384 10.5614/itbj.ict.res.appl.2023.17.3.6 Leveraging Data Management Capabilities for Innovation Capabilities: The Moderating Role of Cross-Functional Integration <p>In today’s dynamic and competitive business environment, data are crucial for sustaining a competitive advantage. Organizations are also constantly seeking ways to enhance their innovation capabilities in order to stay ahead of the competition. One critical factor that has been identified as influential in enabling innovation are the organization’s data management capabilities. Past studies have found that cross-functional integration may enhance the impact of data management on innovation. Hence, this study aimed to investigate the influence of data management capabilities on explorative and exploitative innovation by considering the role of cross-functional integration as a moderating variable. This study used 116 data samples from medium and large companies across different industries in Indonesia. The PLS-SEM analysis was applied to test the research hypotheses. The results indicate that data management capabilities as a third-order construct, consisting of three dimensions, namely data governance, technology, and skills, have significant direct influences on explorative and exploitative innovation. This study demonstrated that cross-functional integration still plays an important role in amplifying the relationship between data management capabilities and innovation capabilities, especially in relation to explorative innovation.</p> Destina Ratna Asih Khodijah Kadarsah Rajesri Govindaraju Budhi Prihartono Copyright (c) 2024 Journal of ICT Research and Applications 2023-12-31 2023-12-31 17 3 385 411 10.5614/itbj.ict.res.appl.2023.17.3.7