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International Journal of Engineering, Science and Information Technology
Published by Universitas Malikussaleh
ISSN : -     EISSN : 27752674     DOI : -
Core Subject : Science, Education, Social, Engineering,
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation
The journal covers all aspects of applied engineering, applied Science and information technology, that is: Engineering: Energy Mechanical Engineering Computing and Artificial Intelligence Applied Biosciences and Bioengineering Environmental and Sustainable Science and Technology Quantum Science and Technology Applied Physics Earth Sciences and Geography Civil Engineering Electrical, Electronics and Communications Engineering Robotics and Automation Marine Engineering Aerospace Science and Engineering Architecture Chemical & Process Structural, Geological & Mining Engineering Industrial Mechanical & Materials Science: Bioscience & Biotechnology Chemistry Food Technology Applied Biosciences and Bioengineering Environmental Health Science Mathematics Statistics Applied Physics Biology Pharmaceutical Science Information Technology: Artificial Intelligence Computer Science Computer Network Data Mining Web Language Programming E-Learning & Multimedia Information System Internet & Mobile Computing Database Data Warehouse Big Data Machine Learning Operating System Algorithm Computer Architecture Computer Security Embedded system Coud Computing Internet of Thing Robotics Computer Hardware Information System Geographical Information System Virtual Reality, Augmented Reality Multimedia Computer Vision Computer Graphics Pattern & Speech Recognition Image processing ICT interaction with society, ICT application in social science, ICT as a social research tool, ICT in education
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 593 Documents
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Operationalizing No-Code AI: Cross-Functional Implementation and Organizational Impact Mukesh Shah, Binita; Bansal, Rishab
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1190

Abstract

This paper explores how non-technical teams can be the form of organizational adoption and quantifiable results of the so-called no-code AI platforms. Through the sequential mixed-method design, 32 organizations in the six industries supplied data complemented by large-volume data sets such as the Stack Overflow Developer Survey (n = 73,268) and Kaggle Data Science Skills dataset (n = 25,973). Hierarchic clustering produced the following three cases of adopters: early adopters in marketing and operations, pragmatic adopters in customer service and HR, and conservative adopters in finance and legal with high adoption differences (37.82-fold asymptotic, p = 0.001). Regression analysis identified functional success predictors like, MarTech integrations of the marketing system-based system (= 0.43, P = 0.001) integration of the operations systems-based system (= 0.52, P = 0.001) and privacy protection-based HR system (= 0.56, P = 0.001). Productivity analysis showed that initial implementation cost decreased output by -7 percentage in the first month, but was compensated in 2-3 and 4-6 months on marketing/operation and other functions respectively. In twelve months, long-term returns amounted to 37 per cent marketing, 31 per cent operations and 26 customer service. Three clusters were verified by calculation of ROI: high ROI in marketing/operations (143%-217%), moderate ROI in customer service (87% -112%), delayed ROI in HR, finance, and legal (31% -64%). A tested implementation model has been constructed, which relies on the use of functional approaches, levels of governance, capability-building and integration methods with good predictive validity (R 2 = 0.71, error rate = 12%). The evidence shows that the democratization of AI can be achieved through strategic alignment, risk-sensitive governance, and role-specific training that would optimize the use of AI and its long-term organizational value.
Beyond 5G: Exploring AI-Driven Network Optimisation for 6G Communications Meher, Kunal; Karthikeyan, S.; Ranjan Sahu, Bharat Jyoti; Sunil, M.P.; Mishra, Smita; Singh, Amanveer; Tejesh, Kukatla
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1305

Abstract

This research consists of various features of 5G networks; the vision for 6G networks promises significant advancements, including ultra-high data rates, sub-millisecond latency, highly intelligent network operations, and exceptional device interconnectivity, among others.  Artificial Intelligence (AI) meets these requirements, which act as a fundamental base in self-organising and proactive adaptive network management. In the scope of this paper, AI integration with core 6G network functions is considered, including AI techniques such as machine learning, deep learning, federated learning, and reinforcement learning. Focus is on the AI-driven optimisation of spectrum utilisation, user experience, traffic pattern prediction, dynamic network slicing, robust QoS, and responsive QoS retention. Advancing edge computing, reconfigurable intelligent surfaces (RIS), and digital twins are also discussed. The study also discusses the lack of AI governance in 6G infrastructure, which includes data privacy, transparency of the algorithms, energy expenses, and global standardisation. This research focus reveals the highlights of the primary gaps in design and governance rationale that emerge through the lack of AI-integrated structural frameworks, resigns through the absence of a designed fabric needed to supplant the transcending potential of 6G enabled autonomous communication systems AI will irrevocably purge and define the naivety behind detonating the boundless potential AI entrenched paradigms will deliver.
EEG-Based Focus Analysis to Evaluate the Effectiveness of Active Learning Approaches Udayana, I Putu Agus Eka Darma; Sudarma, Made; Putra, I Ketut Gede Darma; Sukarsa, I Made; Jo, Minho
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1068

Abstract

Electroencephalography (EEG) has emerged as a non-invasive and objective technique for monitoring brain activity in real time, widely applied to measure cognitive states such as concentration and alertness. Its ability to capture brain responses during learning processes makes EEG a promising tool to evaluate student engagement more accurately than conventional methods. This study investigates the effectiveness of two active learning methods, Project-Based Learning (PjBL) and Problem-Based Learning (PBL), in the context of English tutoring for elementary students using EEG signals as a cognitive indicator. A total of 20 students aged 8–12 years from ThinkerBee Learning Centre Bali participated in the study. EEG data were recorded using the Muse 2 Headband while students completed test-based tasks designed for each learning method. The EEG signals were preprocessed using bandpass filtering, Continuous Wavelet Transform (CWT), and frequency band decomposition. Concentration scores were then calculated using two approaches: a heuristic method based on the Beta/(Theta + Alpha) ratio and a Long Short-Term Memory (LSTM) model. The heuristic method produced average scores of 0.3991 (PjBL) and 0.3822 (PBL), with a 4.42% difference, while the LSTM model showed a more substantial difference, with scores of 0.5454 (PjBL) and 0.4265 (PBL). A Spearman correlation test between EEG-derived scores and students’ academic results yielded a perfect correlation value of 1.0000, indicating a strong relationship between cognitive engagement and learning outcomes. These results demonstrate the potential of EEG as a reliable tool for objectively assessing learning effectiveness in primary education contexts.
Toward Ultra-Reliable Low-Latency V2X: A Hybrid Deep Learning Approach for Intelligent Vehicular Networks Jiang, Yi; Bin Ariffin, Shamsul Arrieya
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1536

Abstract

Safe and efficient vehicular networks in contemporary intelligent transportation systems necessitate ultra-reliable and low-latency communication (URLLC) requirements acting as the base foundation. Researchers combined Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks for creating their Hybrid Deep Learning-Based V2X Framework to improve V2X real-time decision-making abilities. The system's first operation phase acquires diverse Vehicle-to-Everything data from V2V, V2I, V2P and V2N sources which contain GPS locations and vehicle speed readings side by side with Received Signal Strength Indicator (RSSI) measurements along with channel status data. The preprocessing method applies normalization strategies (Min-Max Scaling and Sliding Window Method) together with data reduction methods and time-series transformations to create ready-to-use modelling inputs. Through traffic data sources CNN modules decode road layout features and vehicle distributions next to detecting signal interference sequences but LSTM modules analyze signal variations and handover delay effects and identify congested area evolutions. Processor layers integrate both spatial and temporal elements to produce a unified representation that enables predictions for optimal communication standards. The system maintains dependable communication in dense and mobile environments by enabling adaptive routing and dynamic power control along with stable link selection mechanics. The proposed hybrid framework will benefit the next-generation V2X network by achieving computational efficiency alongside predictive accuracy for autonomous driving and smart traffic management functionalities. The proposed hybrid framework boosts the V2X network by ensuring both computational efficiency and predictive accuracy for autonomous driving, enabling improved traffic management. This integration enhances vehicle coordination, real-time safety, and congestion forecasting for future transportation systems.
Synthetic Data for Business Intelligence: A New Paradigm for Privacy-Preserving Machine Learning in Enterprise Environments Barot, Deep; Najeeb Shaik, Kamal Mohammed; Haque Mukit, Mohammad Mushfiqul; Melath, Vinesh; Nair, Rithesh
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1442

Abstract

The growing demand for data-driven decision-making in the enterprise context poses a conflict between the utilisation of machine learning (ML) and data privacy. The paper examines the feasibility of using synthetic data to replace actual enterprise data in business intelligence (BI) applications. Synthetic datasets were created using the CTGAN, Variational Autoencoders (VAE), and diffusion models and were successfully assessed in fraud detection and customer segmentation tasks. Empirical findings indicate that XGBoost with synthetic data as training data achieved an accuracy value of 97 percent, with an ROC AUC of 0.94, which is relatively close to the achievable accuracy with real data. CTGAN was found to have high fidelity as the Wasserstein distances were less than 0.15, and the Jensen-Shannon divergence was less than 0.08. The visualisations of dimensionality reductions ensured that the real and synthetic data had a substantial structural similarity. Privacy analyses revealed that the Nearest Neighbour Adversarial Distance (NNAD) scores differed between CTGAN and diffusion models, with values of 0.38 and 0.36, respectively. Corresponding Membership Inference Attack (MIA) success rates were 51-52%, which is significantly lower than the 68% success rate of the anonymised real data. These findings confirm the consideration that synthetic data can maintain analytical value and diminish privacy risks, providing an effective approach to the safe and scalable implementation of ML in businesses.
Real-time Image Processing in Embedded Vision Systems for Autonomous Vehicles Venugopal, Vedanarayanan; Mohanty, Monalisa; Boregowda, Vinay Kumar Sadolalu; Singh, Suraj; Singh, Manpreet; Deepthi, Pochampalli; Deepak, Shashikant
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1495

Abstract

The most recent studies show that the benefits of the IT improvements associated to ads were substantial. These days, there is a lot of controversy over the optimal way to store, retrieve, and access personal and other data.Since systems are developing so quickly and clients may now access information virtually from anywhere, delivering personal or official information on a physical device has become outdated. This is how distributed computing has emerged and expanded to meet the needs of efficiency, security, unwavering quality, and laziness. The widespread use of Internet of Things devices promises to alter some aspects of our lifestyle. The delivery of human services services is being transformed by other individual Internet of Things devices, such as wearable wellness, wellness monitoring devices, and system-powered restorative devices. This invention promises to benefit the elderly and others with disabilities, enabling higher degrees of independence and personal fulfilment at an affordable price. According to the legally enforceable claim, the Internet of Things connects everything to the Internet, conducts data exchange, and transmits information via data-detecting devices including sensors, RFID, and global positioning systems. The Web of Things must be designed to detect, guide, and filter objects in order to provide clients with a variety of innovative data management services. The effects on transportation planning of autonomous cars, often known as self-driving, driverless, or robotic vehicles. Based on past vehicle technology experience, it examines the likelihood of such vehicles developing and being used quickly, their potential costs and benefits, how they will impact travel behaviour, and how they will influence planning choices like the best parking, roads, and public transportation options.
Cognitive Analysis of Village Potential Basic Integration in BUMDEs Managers in North Aceh District, Lhokseumawe City and Bireun District Likdanawati, Likdanawati; Hamdiah, Hamdiah; Sutriani, Sutriani; Ilham, Rico Nur; Multazam, Muhammad
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1114

Abstract

Currently, the government continues to strive to build and develop the rural economy through community empowerment programs to increase productivity, business diversity, and regional potential, as well as improve the economy and welfare of rural communities. One of the government's programs is to provide a budget for funds in the field of community empowerment, which will be used to support the capital for establishing Village-Owned Enterprises (BUMDEs). This follows the objectives of BUMDEs, namely optimising the management of village assets and existing village potentials to support the village economy and improve the welfare of rural communities. But unfortunately, until now, the role and function of BUMDEs have not been effective or felt by the community. This is due to the inability and lack of professionalism of BUMDEs' management human resources, and the lack of integration of businesses managed by BUMDEs with existing village potentials, so that it seems as if BUMDEs stands only as a formality without supporting the economic potential of the village community. This study aims to create a strategic concept in BUMDEs management so that it runs more effectively and on target to support the village economy through the development of human resource knowledge in BUMDEs management, integrated with the economic potential of the village community. So, BUMDEs and Basic Pontesial village are integrated into a business institution that supports the village economy for the welfare of the village community. This solution can help village communities strengthen their economy and provide a view of knowledge and open insights to BUMDEs managers so that they can develop BUMDEs into a business that synergises with the community. This study uses primary data from questionnaires and interviews aimed at BUMDEs management employees to see the extent of their abilities, expertise, and knowledge to evaluate and innovate in the business. In analysing the data, this study uses the Maslahah Scorecard measurement method, which refers explicitly to performance measurements oriented towards the welfare of the people (society). The output of this study is the publication of a reputable national journal and a reference book of research results certified by an ISBN with an initial TKT level of TKT 1. A Final TKT Target of TKT 3, namely this study, will focus on the Integration of Basic Village Potential in BUMDEs Managers in North Aceh Regency, Lhokseumawe City and Bireun Regency.
Mobile Learning Applications and Their Impact on Students' Academic Performance in Rural Schools Muhammedova, Farog‘at; Ergashev, Mirkomil; Imamova, Nilufar; Ashirova, Anorgul; Urishev, Adham; Kuvvatova, Mokhira; Sattorova, Shalola
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1412

Abstract

Mobile learning is a recognised methodology due to its numerous advantages, including the ability to access educational content at any time and place, customisation of content to meet students' needs, and prompt feedback. This study aims to demonstrate that learning facilitated by a customised smartphone application can successfully improve the academic achievement of Rural School (RS) students by implementing periodic evaluations via the mobile application. This study proposed Mobile Learning Applications and their Impact on Students' Academic Performance (MLA-SAP) in RS. The study subjects were students in RS, Uzbekistan. An MLA-based approach was implemented in the test group (n?=?20), whereas the control group engaged in a lecture-based traditional classroom setting (n?=?35). An outlook scale has been employed to assess students' perceptions of mobile learning, while a test of success was utilised to evaluate the impact of MLA on student academic performance. Interviews have been conducted with RS students and teachers for a qualitative analysis. The results indicate that MLA may facilitate SAP. Both groups exhibited markedly elevated scores regarding MLA.
Failure of Preventive Security Controls in Cloud-Native Systems: Revisiting Governance Enforcement Ramadhan, Muhammad Daffa; Fajar, Ahmad Nurul
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1294

Abstract

Cloud-native architectures have introduced a fundamental shift in how security and governance are applied within modern IT environments. While traditional preventive IT General Controls (ITGCs) were designed for static, centralised systems, their application in dynamic, decentralised, and automated cloud-native systems remains ambiguous and often ineffective. This study investigates the patterns of failure in preventive controls across cloud-native environments and analyses the extent to which governance frameworks fail to enforce security proactively. Employing a meta-synthetic approach, this research reviews documented cloud breach incidents from 2021 to 2024 to extract recurring failure patterns. These incidents were analysed and mapped against major security control domains, including identity and access management, configuration hardening, and observability. The findings highlight systemic gaps in the implementation of preventive measures, particularly in areas where infrastructure is governed as code, and runtime dynamics alter control effectiveness. Furthermore, the study examines how existing governance frameworks such as ISO 27001, COBIT, and NIST CSF are often too abstract or outdated to directly translate into executable policies within CI/CD pipelines and cloud-native infrastructures. The study reveals that misconfigurations, inadequate identity management, and runtime blind spots are among the most common contributors to control failures. These issues are compounded by the lack of real-time enforcement mechanisms and the misalignment between policy design and operational realities. Based on these findings, the paper proposes a shift toward Governance-as-Code and continuous control validation as critical strategies for modern preventive governance. In conclusion, the paper demonstrates that traditional ITGCs, while still conceptually relevant, require operational reengineering to remain effective in cloud-native ecosystems. A governance model that is executable, context-aware, and runtime-integrated is essential for proactive security and sustained compliance in modern digital infrastructure.
The Effect of Technology Training on Increasing MSME Productivity: Case Analysis of Digital Training Programs for Local Craftsmen Lukita, Chandra; Purnama, Ika Yuni; Rahardja, Untung; Natasya, Ersa Aura; Sanjaya, Yulia Putri Ayu
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1087

Abstract

This research aims to explore the impact of technology training on increasing the productivity of micro, small and medium enterprises (MSMEs), focusing on local artisans. The Partial Least Squares structural analysis method (PLS-SEM) tests the proposed hypothesis based on survey data from MSMEs participating in digital training programs. The research results show that active participation in technology training programs significantly increases the application of technology in MSME business operations. Applying this technology will then have a positive impact on improving the productivity of MSMEs. Additionally, consistency in construct measurement, such as reliability and validity, is vital in explaining variation in the dependent variable. These findings provide an essential contribution to understanding the role of technology in increasing the productivity of MSMEs and highlight the importance of consistency in construct measurement in the context of this research.

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