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Journal of Technology and System Information
Published by Indonesian Journal Publisher
ISSN : -     EISSN : 30322081     DOI : https://doi.org/10.47134/jtsi
Core Subject : Science,
Computer Science & IT
The Journal of Technology and System Information is dedicated to publishing cutting-edge research and advancements in the broad and dynamic intersection of technology and information systems. The focus of the journal is to facilitate the exchange of knowledge and ideas in these interconnected domains, fostering a deeper understanding of the role of technology in shaping information systems and vice versa. The journal welcomes contributions that span theoretical, empirical, and practical aspects, with an emphasis on the transformative impact of technology on information systems and vice versa. The scope of JTSI is a Information Technology and Systems, Data Management and Analytics, Emerging Technologies, System Design and Optimization, Cybersecurity and Privacy, Networks and Communication Systems, Artificial Intelligence and Machine Learning, Human-Computer Interaction.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 11 Documents
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Search results for , issue "vol. 3 no. 2 (2026): april" : 11 Documents clear
Employee Performance Management Information Systems: A Systematic Literature Review Muhammad Dani Wahyudi
Journal of Technology and System Information Vol. 3 No. 2 (2026): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i2.5650

Abstract

The accelerated digital transformation of human resource management has intensified the need for integrated systems capable of monitoring and evaluating employee performance in a systematic and data-driven manner. This study aims to examine the development trajectories, implementation patterns, and effectiveness of Employee Performance Management Information Systems (EPMIS) across diverse organizational settings. Employing a Systematic Literature Review (SLR) approach, this research adheres to the PRISMA framework to ensure methodological rigor, transparency, and replicability. Data were collected from major academic databases, resulting in the selection of 30 empirical studies published between 2021 and 2026, which were subsequently analyzed using qualitative synthesis techniques. The findings demonstrate that the adoption of EPMIS contributes significantly to enhanced transparency, accountability, and administrative efficiency in both public and private sector organizations. Additionally, these systems facilitate objective and continuous performance evaluation through real-time data integration, thereby positively influencing employee motivation and work discipline. Nevertheless, the effectiveness of EPMIS implementation is contingent upon several moderating factors, including the availability of adequate technological infrastructure, the level of digital literacy among users, and the extent of organizational readiness for digital transformation. Challenges in these areas may hinder optimal system utilization and reduce overall impact. In conclusion, EPMIS represents a strategic instrument for improving employee performance management; however, its successful implementation requires comprehensive organizational support, continuous capacity building, and alignment between technological systems and human resource competencies.
Survey on Using Chaotic Maps in Image Encryption Techniques Malath Kareem
Journal of Technology and System Information Vol. 3 No. 2 (2026): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i2.5744

Abstract

In this work, a direct experiment realization is proposed to encrypt 256×256 pixels grayscale image by using five different chaotic systems (Logistic, Tent, Henon, Lorenz, and a Logistic–Tent hybrid system). The permutation and diffusion process were executed with chaotic sequences derived from the same maps in order to quantitatively analyze the influence of each chaotic system on the statistical security metrics and the computation cost in a single unified execution environment. The results indicate that the quality of randomness was increased by increasing the complexity of the chaotic map as the entropy values were 7.91 for the Logistic map and 7.94 for the Tent map, 7.96 for the Henon map, 7.98 for the Lorenz system and a maximum of 7.99 for the hybrid model. This is a 0.08 better than worst model and it is closer to ideal entropy value 8. At the same time, the correlation coefficient of two adjacent pixels sharply reduced from 0.0043 to 0.0008, about 81% of decrease, which quantitatively verifying that the spatial dependencies in plain images is almost completely eliminated in the encrypted images. Regarding differential diffusion, all schemes attained NPCR values above 99.5%; however, the Lorenz system and the hybrid model yielded the best values of 99.71% and 99.74%, accompanied by UACI of 33.42% and 33.45%. This means that on average the intensity of a plain-pixel change was magnitude 33 variation to the average among 99.7% cipher-pixels. This numerical behaviour is mirrored in key sensitivity tests where a change in the control parameter μ or the initial condition x₀ in the 6th decimal place causes total decryption breakdown which is quantitatively in line with the large values of NPCR and UACI and establishes the presence of an extremely sensitive and non-approximable effective key space. The run times were 0.38 s and 0.42 s for the Tent map and the Logistic map, respectively, and rose to 0.50 s for the Henon map, and 0.63 s for the Lorenz system, whereas the hybrid model realized a medium execution time of 0.56 s. There is thus a clear quantitative trade-off between the security and the computational cost, as the marginal entropy increase of 0.01–0.02 in the Lorenz system was reached at an extra cost of 0.07 s with respect to the hybrid scheme, for this reason the security-to-time ratio of the latter was bigger. Also, when numerically compared with traditional algorithms, the hybrid chaotic approach provided better performance than AES in entropy value (7.99 compared to 7.85), in diminishing pixel correlation by up to 95% (0.0008 compared to 0.015), and in reducing execution time by 0.16 seconds. These results clearly illustrate that hybrid chaotic encryption can be expected to provide much better statistical security along with faster computation, which suggests that it is suitable for real-time image encryption and for use in systems with limited resource.
Zero-Trust Network Access with Federated Learning for Privacy-Preserving Intrusion Detection in Distributed Communication Systems Karar Talal
Journal of Technology and System Information Vol. 3 No. 2 (2026): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i2.5746

Abstract

The fast-growing distributed communication systems, such as cloud environment, Internet of Things (IoT) platform, and edge computing computers, have greatly compounded the threat of contemporary cybersecurity. The common traditional intrusion detection systems (IDS) are based on centralized collection and analysis of data, which initiates significant issues concerning the privacy of data, scale issues, and communication overheads. This paper will overcome these issues by developing a new Zero-Trust Network Access (ZTNA) model that combines Federated Learning (FL) with privacy-constrained intrusion detection in distributed communication setting. This suggested architecture will be made of three collaborative layers: edge nodes which process local data and train local models, a federated aggregation server which coordinates the global model update using the Federated Averaging (FedAvg) algorithm, and a zero-trust policy engine which dynamically assesses access control decisions based on user trust scores, and network risk assessments. Deep learning techniques, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Transformer, are used to create local IDS models with which spatial and temporal patterns of attacks can be effectively detected. The experiments are carried out with well-known datasets of cybersecurity benchmarks that are UNSW-NB15, CSE-CIC-IDS2018 and TON IoT. The environment of implementation makes use of the TensorFlow federated, PyTorch, Docker based edge nodes, and a Kubernetes orchestration framework to recreate realistic distributed conditions. Experimental evaluation proves that the offered framework is much more effective in terms of increasing the accuracy of intrusion detection and decreasing false positive rates and maintaining data privacy. Moreover, federated learning combined with zero-trust policies eliminates centralized dependency of data and improves adaptive control access of network elements in dynamic network context. The findings illustrate how the suggested method has the potential of establishing scalable, privacy conscious, and robust intrusion detection systems in next generation distributed communication networks.
Perancangan Sistem Informasi Rental Mobil Berbasis Web Pada PT Mamek Rental Menggunakan Metode Iterasi Rohanan Yusuf; A.R Walad Mahfuzhi; Yulia Darnita; Muhammad Husni Rifqo
Journal of Technology and System Information Vol. 3 No. 2 (2026): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i2.5620

Abstract

Penelitian ini bertujuan untuk merancang sistem informasi rental mobil berbasis web pada PT Mamek Rental guna meningkatkan efisiensi pelayanan, pengelolaan data, dan proses administrasi penyewaan kendaraan. Permasalahan yang dihadapi PT Mamek Rental adalah proses pemesanan yang masih dilakukan secara manual melalui telepon atau pelanggan datang langsung ke lokasi rental. Selain itu, pengisian formulir berulang menggunakan kertas serta pencatatan data armada dan transaksi secara manual menyebabkan proses pelayanan menjadi kurang efektif, rentan terjadi kesalahan pencatatan, kehilangan data, dan keterlambatan penyampaian informasi. Metode penelitian yang digunakan adalah metode iterasi (iterative model), yang terdiri dari tahapan perencanaan, analisis, dan perancangan sistem secara bertahap dan berulang sesuai kebutuhan pengguna. Pengumpulan data dilakukan melalui observasi, wawancara, dan studi pustaka. Hasil penelitian berupa rancangan sistem informasi rental mobil berbasis web yang mencakup halaman registrasi pengguna, login admin dan pelanggan, halaman beranda, detail mobil, pemesanan, konfirmasi pembayaran, hingga dashboard admin. Sistem yang dirancang mampu memberikan kemudahan dalam proses pemesanan kendaraan, pengelolaan data pelanggan, pengolahan transaksi, serta penyajian informasi kendaraan secara cepat dan akurat. Kesimpulan dari penelitian ini adalah bahwa perancangan sistem informasi rental mobil berbasis web dapat menjadi solusi untuk mengatasi permasalahan pengelolaan data dan pelayanan di PT Mamek Rental serta dapat dikembangkan lebih lanjut pada tahap implementasi dan pengujian sistem.
The Impact of Virtual Reality on Cognitive Load Among Senior Students at Middle Technical University: An Empirical Study Saif M Duhaim
Journal of Technology and System Information Vol. 3 No. 2 (2026): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i2.5930

Abstract

The adoption of Virtual Reality (VR) in higher education has gained increasing worldwide interest, however, its evaluation as an effect on workload and cognitive-load related learning outcomes for senior technical university students in Iraq is still an unaddressed issue. This study sought to fill the gap in the literature by investigating the effect of a VR-based instruction on the Nasa TlX workload indicators concerning the cognitive load, the effect of the presence and the intrinsic motivation on the learning performance of senior engineering students in a technical university in Baghdad. Employing a quasi-experimental pre-post control group design, 80 learners participated and were randomly assigned to either a VR group (n = 40) who experienced the interactive content through Meta Quest 2 HMDs or a control group (n = 40) who were taught in a traditional lecture style. The cognitive workload was assessed by NASA-TLX, and the analyses were performed with the use of independent-samples t-tests, MANOVA and exploratory Structural Equation modeling (SEM). Results showed that the VR group had significantly lower mental demand (M = 52.4vs. 61.7, p = .001) and frustration (M = 30.4vs. 44.8, p < .001), but the overall NASA-TLX workload index was not significantly different between the two groups (M = 49.65vs. 52.17, p = .184). The VR group outperformed in learning (MCQ: M = 23.4/30 vs. 19.7/30, d = 1.00), knowledge retention at one-week post-test (78.6% vs. 62.4%, d = 1.53), and intrinsic motivation scores. . Exploratory SEM analysis showed that the immersion in VR could promote learning indirectly by decreasing the extraneous-load-related workload (β = −.54) and enhancing presence (β = . 71). These results suggest that the VR-based intervention has the potential to improve certain aspects of the workload and learning outcomes in higher education in developing countries, although statements regarding an overall reduction in cognitive load should be considered with caution.
Novel Feature Selection Method for APT Detection Hasanain M. J. Alfouadi; Hiba Abdulrazzak Ahmed
Journal of Technology and System Information Vol. 3 No. 2 (2026): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i2.5931

Abstract

An Advanced Persistent Threat (APT) is a multistage, highly sophisticated, and covert form of cyber threat that gains unauthorized access to networks to either steal valuable data or disrupt the targeted network. These threats often remain undetected for extended periods, emphasizing the critical need for early detection in networks to mitigate potential APT consequences. In this work, we propose a feature selection method for developing a lightweight intrusion detection system capable of effectively identifying APTs at the initial compromise stage. Our approach leverages the XGBoost algorithm and Explainable Artificial Intelligence (XAI), specifically utilizing the SHAP (SHapley Additive exPlanations) method for identifying the most relevant features of the initial compromise stage. The results of our proposed method showed the ability to reduce the selected features of the SCVIC-APT-2021, dataset, a benchmark dataset for APT detection that contains both benign and malicious traffic records, from 77 to just four while maintaining consistent evaluation metrics for the suggested system. The estimated metrics values are 97% precision, 100% recall, and a 98% F1 score. The proposed method not only aids in preventing successful APT consequences but also enhances understanding of APT behavior at early stages.
Design and Analysis of Intelligent Control Systems for Power Distribution in Smart Grids Using Internet of Things (IoT) Ahmed Abdul Mahdi Alawsi
Journal of Technology and System Information Vol. 3 No. 2 (2026): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i2.6053

Abstract

The growing complexity of modern distribution networks, driven by renewable energy integration, electric vehicle adoption, and dynamic consumer demand, challenges conventional centralized grid control. Smart grids, supported by the Internet of Things (IoT), provide opportunities to enhance real-time monitoring, distributed decision-making, and adaptive energy management. This research presents the design and analysis of an IoT-enabled intelligent control system for power distribution in smart grids, focusing on a layered framework that integrates sensing, communication, and intelligent control. The proposed architecture consists of three main components: IoT data acquisition from smart meters, PV inverters, and EV chargers; a control layer employing Model Predictive Control (MPC) for system-wide optimization and Multi-Agent Reinforcement Learning (MARL) for local adaptability; and a supervisory layer for visualization and utility coordination. A co-simulation environment was developed using the, incorporating renewable and demand variability, as well as realistic communication latency and packet loss conditions. Performance was assessed through comparative analysis with conventional control strategies. Results show that the IoT-enabled hybrid framework improves voltage regulation by maintaining deviations within ±5%, reduces feeder losses by 12%, lowers peak transformer loading by 18%, and decreases renewable curtailment by 22%. Furthermore, the distributed architecture demonstrated resilience against 500 ms latency and 1% packet loss, outperforming centralized MPC-only solutions. This study provides a reproducible framework for integrating IoT with intelligent control in smart grids. The findings highlight the potential of hybrid MPC–MARL systems to enhance efficiency, scalability, and resilience in distribution networks, offering practical insights for utilities and policymakers toward achieving sustainable energy management.
The Effectiveness of Murf AI in Canva on Students' English Listening Comprehension Paquitta Anggreani Iswanto
Journal of Technology and System Information Vol. 3 No. 2 (2026): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i2.5799

Abstract

Listening  comprehension  is  one  of  the  essential  skills  in  English  learning,  yet  many  junior  high  school  students  still  face  difficulties  in  understanding  spoken  texts.  This  study  aims  to  examine  the  effectiveness  of  using  Murf  AI  in  Canva  to  improve  students’  listening  comprehension  skills.  A  quasi-experimental  design  was  employed  involving  two  groups:  Class  8B  as  the  experimental  group  and  Class  8A  as  the  control  group.  The  experimental  group  was  taught  using  Murf  AI  in  Canva,  while  the  control  group  received  conventional  instruction.  Data  were  collected  through  pre-tests  and  post-tests  and analyzed using the Mann–Whitney U test and effect size calculation. The  results  revealed  a  statistically  significant  difference  between  the  two  groups,  with  the  experimental  group  achieving  higher  mean  scores  than  the  control  group.  However,  the  effect  size  was  found  to  be  small,  indicating  a  limited magnitude of improvement. In  conclusion,  the  use  of  Murf  AI  in  Canva  has  a  significant  effect  on  students’  listening  comprehension  skills.  Although  the  impact  is  relatively  small,  this  tool  can  be  considered  an  alternative  strategy  to  support  listening  instruction  in
AdverShield-LLM: Adversarial Robustness Certification for IoT-Integrated Retrieval-Augmented Generation via Randomized Smoothing Yasser Samir Hadi
Journal of Technology and System Information Vol. 3 No. 2 (2026): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i2.6047

Abstract

The emergence of the Internet of Things (IoT) ecosystems, Retrieval-Augmented Generation (RAG) systems have become commonplace and provide a means for embedding dynamically retrieved external knowledge in the response from a Large Language Model (LLM). While potentially helpful, IoT-enabled RAG pipelines present significant adversarial threats such as poisoning passages into the IoT knowledge base, altering dense retrieval embeddings, and conducting indirect prompt injection attacks via the inputs through the IoT sensors, all of which can impact the fidelity of generated responses and compromise the trustworthiness of the system. Current defenses are based mostly on heuristic filtering or empirical adversarial training, and are not known to be robustly certified or are fragile under adaptive adversaries. In response to these challenges, this article introduces a new certified defense framework named AdverShield-LLM to combine the randomized smoothing technique with a multi-granular noise injection mechanism well-suited to the distributed and low latency requirements of RAG systems in the IoT domain. AdverShield-LLM consists of three synergistic modules: (i) Passage-Level Smoothed Aggregation (PLSA) module which certifies the robustness of RAG retrieval against bounded corpus poisoning under an isolate-then-smooth paradigm, (ii) Token-Adaptive Gaussian Defense (TAGD) layer that certifies LLM generation against indirect prompt injection by propagating l_2-norm perturbation bounds through the transformer attention stack, and (iii) IoT-Aware Certified Radius Scheduler (IACRS) that dynamically schedules noise budgets among constrained edge nodes while preserving the certified radius. AdverShield-LLM is evaluated on three IoT security benchmarks—MS-RAG-IoT, NQ-Adversarial and IoTQA-Poison—with extensive experiments showing its certified accuracy is 81.4% under l_2 perturbation radius σ=0.50 compared to the strongest baseline RobustRAG which reported +9.3% accuracy, and reduced the attack success rate from 74.2% to 8.6% against PoisonedRAG. Moreover, AdverShield-LLM ensures the accuracy of clean answers within 2.1% of the undefended RAG accuracy, proving that certified robustness does not compromise the utility of RAGs in resource-limited IoT environments.
Performance Improvement of f-OFDM Systems Using a Concatenated RS/LDPC Coding Scheme Ghasan Ali Hussain
Journal of Technology and System Information Vol. 3 No. 2 (2026): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i2.6091

Abstract

In wireless communication systems, transmission errors are inevitably generated due to interference and fading. Therefore, various approaches have been developed to enhance system reliability, including increasing the transmitted signal power and utilizing error detection and correction schemes. Among these approaches, channel coding is an important technique for reducing the Bit Error Rate (BER). Although LDPC codes are widely adopted in 5G wireless communication systems; achieving ultra-low BER values remains challenging in LDPC codes due to the error-floor phenomenon. To address this limitation, a modified concatenated RS/LDPC code is proposed for an f-OFDM system in this paper. It employs RS codes with LDPC codes followed by an interleaver. Unlike the conventional concatenated RS/LDPC codes that separated both codes by an interleaver. Simulation results demonstrate that the suggested system achieves superior BER performance compared with the conventional concatenated scheme and standalone RS and LDPC codes under both BPSK and QPSK. Furthermore, the suggested f-OFDM system provides superior Out-of-Band Emission (OOBE) suppression compared with the conventional OFDM system, while maintaining a Peak-to-Average Power Ratio (PAPR) performance comparable to that of the conventional OFDM system. Based on the results, the suggested f-OFDM system is considered a promising candidate for 5G and beyond systems.

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