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International Journal of Engineering, Science and Information Technology
Published by Universitas Malikussaleh
ISSN : -     EISSN : 27752674     DOI : -
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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 73 Documents
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Search results for , issue "Vol 5, No 4 (2025)" : 73 Documents clear
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.
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.
Global Citizens, Language Issues, and Digital Economy: An In-quiry of Financial Technology Adoption among International Students Moe, Sithu; Mukminin, Amirul; Marzulina, Lenny; Harto, Kasinyo; Erlina, Dian; Fithriani, Rahmah; Fridiyanto, Fridiyanto; Holandyah, Muhamad; Kamil, Dairabi; Mohd Ali, Fatin Aliya; Alshehari, Azzam
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.v5i4.1284

Abstract

Financial Technology (FinTech) has significantly changed the landscape of Indonesia’s digital economy. On the other hand, the increasing non-domestic population in Indonesia, especially the student age group, also reflects the broadening demographic dynamics. This group plays a crucial role as a consumer in national economic growth. Despite the increasing growth of the national digital economy, the non-domestic population, especially the student age group, is doubtful about the acceptance of using FinTech. This paper investigated non-domestic students’ intention to use FinTech in Indonesia. The authors employed mixed methodology with an explanatory sequential design. Thus, this paper explored this narrative based on the technology acceptance model (TAM) and external factors such as Performance Expectancy (PE) and Social Influence (SI). Using the partial least squares (PLS) approach, data from the 75 non-domestic students in Indonesia were analyzed. In addition, this paper also utilized in-depth interviews to gather further information from participants. The thematic analysis of the semi-structured interviews was conducted to explore the non-domestic students’ experience of using Indonesian FinTech. 
Efficient Deep Learning Ensemble of Lightweight CNNs and Vision Transformers for Real-Time Plant Disease Diagnosis Dubey, Mruna; P.S.G., Aruna Sri; Jha, Suresh Kumar; Nupur, Nupur; Bhiogade, Girish; Kumar, Neeraj
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.v5i4.1347

Abstract

Timely identification of plant diseases plays a vital role in protecting crop yield and supporting effective decision-making in precision agriculture. Conventional computer vision models achieve high recognition accuracy but often require substantial computing power, making them impractical for low-cost edge hardware widely used in rural areas. In this work, a compact deep learning ensemble is presented, combining three lightweight convolutional neural networks—MobileNetV3-Small, EfficientNet-B0, and ShuffleNetV2—with a Vision Transformer (ViT-B/16). The models operate in parallel, and their outputs are merged using a weighted late-fusion approach, with fusion weights determined through systematic grid search to achieve the best trade-off between predictive performance and processing speed. The Plant Village dataset, consisting of 54,303 images from 38 healthy and diseased leaf categories, was used for evaluation. To improve robustness, the training data were augmented through geometric transformations, contrast adjustment, and controlled noise addition. When tested on a Raspberry Pi 4 device, the ensemble reached an accuracy of 97.85%, precision of 97.67%, recall of 97.92%, and F1-score of 97.79%, with an average inference time of 20.5 ms and a total size of 14.6 MB. These results surpassed those of all individual models and conventional machine-learning baselines. Statistical testing using McNemar’s method confirmed the significance of the improvement (p 0.05). Precision–Recall analysis indicated strong resistance to false positives, while accuracy–latency assessment confirmed suitability for real-time field operation. The proposed system offers a practical, resource-efficient framework for on-site plant disease diagnosis in areas with limited connectivity and computing resources. Further development will focus on adaptation to field-captured imagery, hardware-aware model compression, and the integration of additional sensing modalities such as hyperspectral and thermal imaging.
An Empirical Investigation of Portfolio Optimisation Using the Markowitz Model Ni, Yixi
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.v5i4.959

Abstract

In finance, portfolio optimisation involves an essential concept that requires determining the ideal combination of assets to optimise returns by lowering the return risk. The concept of efficient portfolios, which aims to attain the maximum return for a given level of risk or the minimum risk for a given level of return, was initially suggested by Markowitz's model. Considering an emphasis on the Shanghai Stock Exchange (SSE), this research explores portfolio optimisation using Markowitz's Portfolio Theory about the Chinese stock market. The objective is to identify the optimal stock portfolio from a selection of various companies listed on the SSE for the 2020-2024 periods, balancing risk and expected return. A purposive sampling method is used to select various stocks based on their historical performance, with stocks screened through a two-level process: first by correlation coefficients, and by their coefficient of variation to assess risk-return trade-offs. Weekly return rates of selected stocks from the SSE over four years are used for the analysis. Using the mean-variance optimisation approach, the ideal weights for each stock in the portfolio are determined using the expected return effect. The results show that the optimized portfolio, consisting of various stocks (Industrial and Commercial Bank of China (ICBC), GD Power Development Co., Ltd, Beiqi Foton Motor Co., Ltd., Shanghai Automotive Industry Corporation (SAIC Motor), China Life Insurance Company (LIC)), performs more effectively with the return in trading days. The portfolio includes companies with diversified sectors, ensuring a balanced risk and return profile.
Synergizing Oil Palm Landscapes, Agroforestry, Eco-Tourism, and Rural Economy: A Review on Pathways to Sustainable Development Judijanto, Loso
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.v5i4.1180

Abstract

The rapid expansion of oil palm monocultures in tropical regions has been unfairly accused of being the sole cause of environmental degradation, socio-economic imbalances, and diminished biodiversity. As sustainability and ecotourism gain global momentum, agroforestry within oil palm landscapes emerges as a promising solution to reconcile ecological conservation with rural economic development. This study investigates the potential of agroforestry-based systems in oil palm plantations as sustainable eco-tourism destinations. The objective of this research is to explore how agroforestry practices can restore ecological function, diversify local economies, and enhance community participation through eco-tourism integration. This qualitative study adopts a Systematic Literature Review (SLR) methodology guided by the PRISMA protocol. The data were collected from the ScienceDirect database using refined Boolean keyword combinations. A total of 1,005 articles were initially identified, narrowed down to 31 relevant peer-reviewed research articles published between 2020 and 2025 after applying specific inclusion and exclusion criteria related to topic, time frame, document type, and open access availability. Thematic analysis was applied to synthesize findings from the selected articles. Results indicate that agroforestry significantly enhances biodiversity, improves microclimates, sequesters carbon, diversifies income sources, and strengthens community governance within tourism ventures. Ecological, economic, and social synergies suggest that agroforestry in oil palm systems is a viable model for regenerative tourism. In conclusion, agroforestry has strong potential to transform oil palm landscapes into inclusive, resilient, and ecologically sound eco-tourism destinations. Future studies should expand interdisciplinary evaluation frameworks and assess long-term impacts through longitudinal data.
UX Matters: Unlocking QRIS Adoption among MSMEs in the Greater Jakarta Area Ramadhan, Muhammad Daffa; Fajar, Ahmad Nurul
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.v5i4.1337

Abstract

This study investigates the influence of User Experience (UX) dimensions, integrated with the Technology Acceptance Model (TAM), on the adoption intention of Micro, Small, and Medium Enterprises (MSMEs) in the Greater Jakarta area toward the Quick Response Code Indonesian Standard (QRIS). The research examines functional qualities, which consist of Efficiency, Perspicuity, and Dependability, alongside hedonic qualities, represented by Stimulation and Novelty, as well as Trust, which serves as an essential construct in the adoption process of financial technologies. These factors were evaluated as direct predictors of adoption behaviour, while Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) were employed as mediating variables to capture the mechanisms underlying the relationships, consistent with TAM’s theoretical framework. Data were collected from 400 MSMEs across various industries in the region, and analysis was conducted using Partial Least Squares–Structural Equation Modelling (PLS-SEM). The empirical results demonstrate that Efficiency strongly drives PU, emphasising the critical role of task performance and functional reliability in shaping perceptions of usefulness. Dependability and Trust significantly improve PEOU, highlighting that stable system performance and confidence in technology providers reduce complexity and foster ease of use. Interestingly, while Stimulation shows a positive and direct impact on Intention to Use, Perspicuity and Novelty yield unexpected negative effects, suggesting that overly simple or overly unfamiliar experiences may hinder rather than encourage adoption. Furthermore, PU and PEOU are shown to mediate several causal paths, reinforcing TAM’s theoretical assumptions and underscoring the value of integrating UX considerations into classical acceptance models. The final structural model exhibits strong explanatory power, with an R² of 0.903 for Intention to Use, indicating the robustness of the integrated framework and confirming the effectiveness of combining UX dimensions with TAM in explaining QRIS adoption behaviour among MSMEs.
A Dual-Microcontroller IoT Platform for Integrated Flood and Air Quality Monitoring: Performance and Integration Challenges Syam, Rafiuddin; Maruddani, Baso; Pramono, Eko Kuncoro; Kartika, Irma Ratna; Irsyad, Daffa Ihsanullah
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.v5i4.1539

Abstract

Jakarta faces escalating environmental challenges, including heightened flood risk and deteriorating air quality, driven by rising rainfall intensity and increasing pollution levels. Conventional monitoring systems for these hazards often operate in isolation, lacking the integration, realtime capability, and accessibility offered by modern Internet of Things (IoT) technology. To address this gap, this study designed and developed a unified, dual-microcontroller IoT platform for the simultaneous and integrated monitoring of flood potential and air pollution. The research followed an Experimental Development methodology, involving systematic hardware design, firmware development, system integration, and rigorous performance testing. The prototype hardware architecture strategically separates data acquisition and network communication by utilizing an Arduino Uno for data acquisition and an ESP32 microcontroller for network communication, respectively. The system incorporates an HC-SR04 ultrasonic sensor for water level detection, a DHT22 sensor for temperature and humidity measurement, and an MQ-135 gas sensor for assessing air quality. Data is displayed locally on a 20×4 LCD and transmitted to a cloud server. A critical finding from the integration phase was a 2.3% data loss rate attributable to serial communication instability between the microcontrollers, highlighting a significant challenge in multi-processor IoT architectures and underscoring the necessity for robust inter-processor protocols. During a comprehensive 24-hour endurance test with measurements taken at ten-minute intervals, the system demonstrated high accuracy in individual sensor readings. It successfully transmitted 97.7% of the data in realtime to a web application built on the Firebase platform. The study concludes that while the integrated dual-microcontroller approach is highly viable for holistic environmental monitoring, future iterations must prioritize enhanced communication reliability through hardware flow control and error-checking mechanisms to achieve the robustness required for mission-critical deployments.
Futuristic Design Based on Sustainable Culture and Creative Economy: Material Technology Innovation in Commercial Buildings Pranajaya, I Kadek; Mahadipta, Ngurah Gede Dwi; Nutrisia Dewi, Ni Made Emmi; Wijaya, Made Eka
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.v5i4.1371

Abstract

 The integration of futuristic design with sustainable culture and the creative economy offers a transformative paradigm in the development of commercial architecture. This study examines the Dekkson Knowledge Shop as a case study to explore how material technology innovation can address environmental challenges while reinforcing cultural identity and strengthening market competitiveness. Employing a qualitative descriptive method, the research utilizes field observations, in-depth interviews, and documentation analysis to investigate the application of high-performance materials and their interpretation within architectural narratives. The findings highlight that the use of eco-friendly composite panels, fiber-cement boards, and adaptive lighting systems not only enhances energy efficiency and durability but also embeds Balinese cultural values within a futuristic aesthetic framework. A key novelty of this research lies in positioning material technology as a narrative medium that connects modern innovation with cultural sustainability, rather than perceiving it solely as a structural element. This integration enriches user experience, strengthens brand identity, and supports the creative economy by transforming architectural design into a cultural and economic asset. Furthermore, the study proposes a replicable design model for future commercial projects that harmonizes sustainable material innovation with local narratives. The model provides both theoretical contributions to architectural discourse and practical strategies for sustainable design practices applicable in a global context. Through this approach, the research underscores the importance of balancing technological advancement with cultural preservation, thereby establishing commercial architecture as a medium for sustainable innovation, cultural continuity, and economic resilience.
Vision Transformer-Based Multi-Head Self-Attention for Early Recognition and Classification of Paddy Leaf Diseases in Rice Fields Palaniappan, M; Saravanan, A
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.v5i4.1410

Abstract

Rice has become an essential food source for a large portion of the world's population, greatly enhancing global food security. One of the fundamental staple crops, paddy, is especially susceptible to diseases primarily caused by bacteria and viruses. The source of the rice blast, Magnaporthe oryzae, poses a severe danger to the world's rice supply, mainly in South India. Both yield and quality are at risk due to the continuous threat of different diseases. However, a few diseases can drastically lower crop yields and quality, making agricultural productivity extremely vulnerable. Therefore, it is crucial to detect diseases at an early stage to effectively manage these risks. Scalable and effective solutions are required because conventional approaches are laborious, expensive, and frequently inaccessible to smallholder farmers. Data-driven strategies like machine learning (ML) and deep learning (DL), can assist in addressing these issues and increasing agricultural sustainability and crop yield. This study presents a new Vision Transformer-based hyperparameter optimization approach for the classification and detection of paddy leaf diseases in rice crops field (VTMHSA-RCPRF). The VTMHSA-RCPRF model comprises data preprocessing, ViT multi-head self-attention-based feature extraction, MLP-based Focal Loss for classification and detection, and Population-Based Training (PBT) as hyperparameter tuning. A wide range of experiments have been carried out to exhibit the promising performance of the VTMHSA-RCPRF method. The simulation outcomes highlighted that the VTH-RCPFRF approach reaches better performance over its recent approaches in terms of distinct measures.

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