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Dahlan Abdullah
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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 582 Documents
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Energy-Aware Multimodal Biometric Authentication Systems for Mobile Hamodi Aljanabi, Yaser Issam; Mahdi, Mohammed Fadhil; Hadi, Shahd Imad; Shnain, Saif Kamil; Abbas, Intesar; Maidin, Siti Sarah
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.v5i1.1356

Abstract

As smartphones become central to personal identity verification, the need for secure, efficient, and power-conscious authentication methods is paramount. While multimodal biometric systems, combining features like face and fingerprint recognition, offer superior accuracy over unimodal approaches, their adoption on mobile platforms is severely hindered by high energy consumption and hardware variability. This paper introduces an energy-aware multimodal biometric authentication framework designed for Android smartphones that directly confronts this challenge. Our system features a novel adaptive fusion mechanism that intelligently balances recognition accuracy with power consumption by dynamically adjusting the weights of biometric modalities in real-time based on battery level and ambient environmental conditions. To validate our framework, we conducted an extensive experimental study involving 46 participants across 460 authentication sessions on five different smartphone models. The results demonstrate that our adaptive system significantly outperforms both unimodal and static fusion baselines. It achieves a high True Acceptance Rate (TAR) and a low Equal Error Rate (EER) while substantially reducing the Energy-Delay Product (EDP). A key feature is the system's ability to gracefully degrade to a secure, fingerprint-only mode when the battery is critically low, ensuring continuous availability without compromising security. This research proves that intelligent, context-aware modality adaptation is a viable strategy for creating robust, efficient, and sustainable biometric authentication solutions suitable for long-term use in consumer electronics.
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. 
Multi-Hop Signal Transmission Patterns in Oracle APEX-Based Monitoring Systems with Dynamic IoT Feedback Loops Keshireddy, Srikanth Reddy
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.v5i1.1450

Abstract

This investigation maps how multi-hop signals travel through Oracle APEX monitoring architectures while absorbing real-time feedback from distributed IoT devices. It reproduces, in code, the relay of packets between nodes and sketches the tilt of delivery rates when loss, spacing, and retransmission limits shift with the network load. The platform weaves SAP telemetry with Honeycomb Oracle APEX dashboards and MQTT feedback loops, then pours those streams into a simulation engine that spits out curves for signal sag, loop latency, and feedback turnaround. Raw numbers show that tuning the retransmit count and watching hop-length can swing reliability figures almost one-to-one, while edge-smart loops clipped jitter delays by a claimed twenty-two percentage points. This work leaves behind a timed model for picking the quickest feedback paths in cloud-flavoured enterprise IoT setups, hoping to give digital overseers a snappier and more elastic toolkit than they had yesterday.
Study on Seismic Performance of Rebar Sleeve Grouting Connection in Prefabricated Concrete Buildings Syamsunur, Deprizon; Naiyuan, Cui; Surol, Salihah; Hisyam Jusoh, Muhammad Noor; Md Noh, Nur Ilya Farhana; Ardana, Putu Doddy Heka
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.v5i2.1530

Abstract

This research aims at assessing the reliability of rebar sleeve grouting connections used in P.C building under cyclic lateral loadings. Such connections are necessary for structural stability at the time of an earthquake. It is essential to determine their performance for improving the safety in prefabricated structures. For the research, three full scale column to foundation samples that used standardized construction materials and methods were used in testing. Rebar sleeve grouting connections were each confined and encased in high strength grout and had ribbed steel sleeves to enhance the mechanical interlocking. Specific performance factors like load transfer efficiency, deformability and energy absorption were recorded as lateral cyclic loads were progressively applied to simulate actual seismic actions. Measurements were made using load cells, displacement transducers, and strain gauges while videotaping of the experiment was done with normal and high-speed cameras. The analysis also showed that of all the factors, sleeve geometry, grout quality and bond strength means have larger impact on seismic performance. Energy dissipation and deformation capacity was captured by displaying that ductile failure modes included rebar yielding and controlled grout cracking. All these findings are relevant to understanding the learnings available for the prefabricated structure design in improving the construction practices and defining the standard tests required to enhance the Seismic performance of the structures.
AI-Powered Adaptive Metamaterials for Reconfigurable Optoelectronics Soy, Aakansha; Nayak, Ashu; Joshi, Praveen Kumar
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.v5i2.1491

Abstract

Coming from the breakthrough of AI-powered adaptive metamaterials (AI-AM), as reconfigurable optoelectronics, these represent a technology that allows real-time, autonomous optical and electronic control. This work presents an AI-AM framework based on machine learning, reinforcement learning, and neuromorphic computing, which aims to develop a new artificial intelligence that optimally dynamically modifies metamaterial behavior. In contrast to traditional metamaterials, the proposed system implements self-adjusting of the wavelength selectivity, polarization, and beam steering at the nanoscale using AI-driven control focused on environmental stimuli. It uses generative AI models to come up with the most optimal material configurations, reinforcement learning to adapt the tuning process, and edge AI processors for running optimised decisions in nanoseconds. For the evaluation and simulation, it is shown that active and passive integrated circuits are capable of significant improvements for response time, energy efficiency, and functional adaptability, compared to conventional approaches. Some key applications of smart lenses for augmented reality, beam steering for 5G/6G networks in AI mode, quantum-enhanced sensor and hardware configuration for neuromorphic photonic processors, etc. This work proposes a paradigm shift in the optoelectronic technology and bridges the gap between artificial intelligence and material science. Based on this study, the potential of using AI augmented metamaterials for revolutionizing photonics, communications, and quantum computing, and next-generation AI intelligent optoelectronic devices with highly reconfigurable, highly efficient, and highly multifunctional properties is demonstrated. The other two areas that future research will address will be scalability, advanced AI training models, and broader real-world applications.
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.
Towards Self-Healing Cloud Infrastructures: Predictive Maintenance with Reinforcement Learning and Generative Models Kunal Shah, Jyoti; Matam, Prashanthi
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.v5i3.1185

Abstract

Reinforcement Learning (RL) is quickly becoming a powerful way to predict failures and improve systems in large cloud environments before they happen. Unlike traditional reactive methods, RL lets smart agents learn the best actions by interacting with changing environments and using reward signals to improve system uptime, resource use, and reliability. As cloud-based big data systems get bigger and more complicated, they also become more likely to have problems that slow them down or cause them to fail at random times. To deal with these problems, we need more than just advanced failure prediction algorithms. We also need adaptive, explainable systems that help people understand what's going on and step in when necessary. This paper looks into how to use RL to help predict and manage failures in cloud-based big data systems. We suggest a layered architecture that uses RL agents and generative explanation models to predict failures and take steps to stop them. We focus on real-time feedback loops, autonomous learning, and outputs that can be understood. This is especially important in anomaly detection pipelines, where explanations need to be detailed but short. We show how reinforcement learning agents can find patterns of risk and take steps to avoid them by using examples from real-world hyperscale data centers. We also look at how generative models, like transformer-based language generators, can turn complicated telemetry data into information that people can understand. At the end of the paper, the authors suggest areas for future research, such as safe RL deployment, multi-agent coordination, and explainable policy design.
Leveraging Kafka for Event-Driven Architecture in Fintech Applications Modadugu, Jaya Krishna; Venkata, Ravi Teja Prabhala; Venkata, Karthik Prabhala
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.v5i3.1074

Abstract

 The volume of payment transactions has grown exponentially, creating a high demand for high-throughput payment processing systems. These systems must be capable of handling a large number of transactions with minimal delay while also being highly scalable and resilient to failures. To overcome this challenge, leveraging kafka for event-driven architecture in fintech applications (LK-EDA-FA-BSCNN) is proposed. At first, input data is gathered from kafka streams. Then, the input data are pre-processed using adaptive two-stage unscented kalman filter (ATSUKF is used to clean the data to ensure high-quality input for downstream analysis. Then, the pre-processed data are fed into binarized simplicial convolutional neural network (BSCNN) is used to predict the future transactions from historical trends. The proposed LK-EDA-FA-BSCNN method is implemented using python and the performance metrics like accuracy, precision, sensitivity, specificity, F1-score, and computational time. The LK-EDA-FA-BSCNN method achieves the best performance with 98.5% accuracy, 95.3% precision and 1.150 seconds runtime with existing methods, like a DRL-based adaptive consortium blockchain sharding framework for supply chain finance (DRL-ACSF-SCF), a blockchain-based secure storage and access control scheme for supply chain finance (BC-SS-ACS-SCF), and analysis of banking fraud detection methods through machine learning strategies in the era of digital transactions respectively.
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.

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