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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 67 Documents
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Search results for , issue "Vol 5, No 4 (2025)" : 67 Documents clear
Information Technology Challenges: Developments and Potential Impact on Socio Economics in the Next Two Decades Amri, Irman; Windiarti, Ika Safitri
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.v5i4.917

Abstract

This study aims to analyze the challenges, developments, and potential impacts of information technology (IT) on socio-economic aspects in the next two decades. Using the Systematic Literature Review (SLR) method that follows PRISMA guidelines, this study examines scientific articles from the Scopus database over the past two decades. The results of the study show that technological developments such as artificial intelligence (AI), big data, Internet of Things (IoT), and digitalization have brought significant changes in various sectors, increasing efficiency, productivity, and innovation. Projections for the next two decades indicate that this trend will continue and evolve, with a major impact on the job market, productivity, quality of life, privacy, and social interaction. The study's conclusions emphasize the need to invest in education and skills training to cope with job market changes, as well as the implementation of strict regulations to protect data privacy and security. Research recommendations include increased collaboration between the public and private sectors, equitable development of digital infrastructure, and public education on the healthy use of technology. Thus, this study provides comprehensive insights to optimize the benefits of information technology while anticipating and overcoming its negative impacts.
Predictive Analysis of Potential Fraud in the Distribution of The Program Indonesia Pintar (PIP) Funds Using the Naïve Bayes and SVM Methods Gumay, Rizki Izandi; Anggai, Sajarwo; Tukiyat, Tukiyat
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.v5i4.982

Abstract

The distribution of funds for The Indonesia Smart Program (Program Indonesia Pintar, or PIP), as a national education assistance program, faces serious challenges related to the potential for fraud that can harm the state and hinder the goal of equitable access to education. This study aims to develop a machine learning-based predictive model to detect potential fraud in the distribution of PIP funds by comparing two main algorithms, Naive Bayes and Support Vector Machine (SVM). The dataset used is the result of the integration of PIP and DAPODIK data in 2023, as well as additional features of engineering results based on the pattern of audit findings. All data, through preprocessing, normalization, and balancing processes, uses SMOTE to overcome class imbalances. The model was evaluated using accuracy, precision, recall, and F1-score metrics, both on internal and external test data from Banten Province. The results showed that SVMs with RBF kernel and optimal parameter tuning provided the best performance with an accuracy of up to 98.5% on test data. At the same time, Naive Bayes tended to be more sensitive to changes in data distribution in new data. Features such as recipient differences, budget checks, and stakeholder proposals have proven to be the leading indicators in detecting fraud. This study emphasizes the importance of external validation and regular model updates so that fraud detection systems remain adaptive to data dynamics in the field. The resulting model can be used as a tool for supervision and decision-making to prevent fraud in distributing education funds.
Proposed Model for Navigating Digital Learning and Examining the Stress of EdTech Das, Shampa Rani; Jhanjhi, NZ; Asfaq, Farzeen; Alqahtani, Haitham; Khan, Azeem
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.v5i4.924

Abstract

The swift integration of information and communication technology (ICT) in education has introduced various tools for teaching, giving rise to concerns about technological stress (technostress) among teachers. While prior research has acknowledged the potential correlation between ICT and stress, a comprehensive investigation into the specific stressors affecting teachers is lacking. This exploratory research aimed to put forth the intricate relationship between educational technologies (EdTech) usage and stress among teaching professionals, shedding light on factors influencing technostress and its impact on teachers' individual lives. The comprehension of the wider ramifications associated with the integration of technologies in the field of education. The proposed model determines various stress reasons caused by digital learning platforms, which can help with the remedy measures based on the model findings. The methodology was explicit, multifaceted, and quantitative. Data from 152 teaching professionals were rigorously analyzed, with demographic questionnaire frequencies calculated using SPSS version 22.0 and hypotheses assessed using SmartPLS version 4.0. A Cronbach's alpha of 0.915 indicated that the questionnaire's queries exhibited high reliability. The findings revealed a robust correlation between stressors and their substantial effect on teachers' overall well-being, job satisfaction, and commitment to their respective organizations, emphasizing the significance of addressing technostress in the education sector.
Performance of PTFE-Based Adaptive Building Facades for Climate Resilience: A Simulation-Driven Analysis Kuda, Antima; Yadav, Madhura; Ali, Syed Moazzam
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.v5i4.1071

Abstract

As an aesthetic architectural thermal barrier, the building envelope is considered vital and contributes substantially in improving the overall building performance. Responsive Facades bring in a revolutionary transformation to the static building skins by changing it into an adaptive façade that responds to the external climatic conditions like solar heat gain, light and temperature variations. The key objective of the paper is to evaluate the potential of PTFE (Polytetrafluoroethylene) in enhancing the building energy efficiency and thermal comfort index of the users in comparison to a static, Energy Conservation Building Code (ECBC) compliant base case test model, under identical environmental conditions. Evaluation is based on the simulation analysis conducted on the highrise office building in Jaipur, India a region characterized by a composite climate with hot summers and cold winters. The complete assessment is derived by using DesignBuilder V7.0 with Energyplus engine. This research focuses on the performance of PTFE as a climate responsive material when used in adaptive building envelopes. Performance metrics include annual heating, ventilation, and air conditioning HVAC energy consumption (kWh/m²), thermal discomfort hours, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfied (PPD). Results demonstrate that the ECBC-compliant static facade recorded an annual HVAC energy use of 96 kWh/m², 1,588 discomfort hours, a PPD of 25.3%, and PMV of +0.82. In comparison, the PTFE kinetic facade achieved an energy use reduction to 95 kWh/m² (1.3% lower), reduced discomfort hours to 1,532 and improved thermal comfort with a PPD of 24.1% and PMV of +0.76. These findings have highlighted the uniqueness of Responsive facades while analysing their capability in enhancing the thermal comfort index and lowering energy consumption, supporting sustainable and climate-responsive building design.
Comparison of LSTM and TCN Models for Customer Churn Prediction Based on Sentiment and Transaction Data Dharmasaguna, Made Bayu Brahmanda; Retnowardhani, Astari
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.v5i4.979

Abstract

This study investigates the combined use of customer review sentiment analysis and transaction history to predict customer churn on the Balimall Market e-commerce platform. The dataset includes 41,519 reviews labeled with positive and negative sentiments and 48 transaction samples labeled as churn or non-churn based on RFM method. Two deep learning models, Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN), are applied in parallel for each analysis path. Data pre-processing includes filtering, cleaning, tokenizing, normalization, sentiment labeling, as well as feature engineering and churn labeling. Evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics shows that TCN excels with 91.55% accuracy on sentiment analysis and 91.67% on churn prediction, while LSTM achieves 86.35% and 86.67% respectively. Segment analysis shows that 47.30 % of users express negative sentiment yet remain active, 51.69 % express positive sentiment and remain active , 0.54 % express negative sentiment and churn, and 0.48 % express positive sentiment and churn. This finding demonstrates that negative sentiment alone does not necessarily lead to churn; instead, the greatest churn risk arises in negative sentiment churners and positive sentiment churners. Expert validation confirmed the reliability of both models, with the recommendation of using a hybrid to combine the advantages of each architecture. The results of this study are expected to help Baliyoni Group design a more targeted customer retention strategy and improve customer satisfaction by examining these segment conditions.
Contextual Relevance-Driven Question Answering Generation: Experimental Insights Using Transformer-Based Models Suryanto, Tri Lathif Mardi; Wibawa, Aji Prasetya; Hariyono, Hariyono; Shili, Hechmi
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.v5i4.989

Abstract

This study investigates the impact of contextual relevance and hyperparameter tuning on the performance of Transformer-based models in Question-Answer Generation (QAG). Utilising the FlanT5 model, experiments were conducted on a domain-specific dataset to assess how variations in learning rate and training epochs affect model accuracy and generalisation. Six QAG models were developed (QAG-A to QAG-F), each evaluated using ROUGE metrics to measure the quality of generated question-answer pairs. Results show that QAG-F and QAG-D achieved the highest performance, with QAG-F reaching a ROUGE-LSum of 0.4985. The findings highlight that careful tuning of learning rates and training duration significantly improves model performance, enabling more accurate and contextually appropriate question generation. Furthermore, the ability to generate both questions and answers from a single input enhances the interactivity and utility of NLP systems, particularly in knowledge-intensive domains. This study underscores the importance of contextual modelling and hyperparameter optimisation in generative NLP tasks, offering practical insights for improving chatbot development, educational tools, and digital heritage applications.
Quantum-Enabled Secure and Energy-Efficient Protocols for Smart Grid Communication Systems Al-Qaraghuli, Sara; Jameel, Sarah Haitham; Majid, Mohammed Nouri; Jawad, Aqeel Mahmood; Saeed, Matai Nagi; Batumalay, M
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.v5i4.1193

Abstract

The development and evolution of the smart grid into complex, cyber-physical energy systems make it essential to secure?communication among the distributed components. The rise of quantum computing has made it even more pressing to develop protocols that?are secure outside the limitations of classical cryptosystems. In this paper, it proposes a quantum-assisting secure communication scheme (QASCP) to?boost the security and energy for smart grid communication systems. The proposed protocol combines quantum key distribution with lightweight entropy-based mutual authentication and dynamic session management. It is designed to defend grid assets such as control centers, smart meters, and distribute energy?resources from sophisticated adversarial models, including quantum-capable threats. The approach consists of system level simulation utilizing a?co-simulation framework customized for quantum smart grid communication. The performance of this scheme was compared?against classical and PQ lattice-based schemes in terms of the authentication latency, energy consumption, entropy preservation, and scalability to handle the load and delay effects, under the assumptions of different loading and delay scenarios. Simulation results show that QASCP is able to reduce the energy consumption and authenticity latency, simultaneously it keeps the high throughput and leaves strong entropy?under attack scenarios. The protocol is also shown to?remain robust with varying quantum bit error rates as well as having a smaller memory footprint on popular network topologies. The results provide evidence for the practical integration of?quantum-secure communication in smart grid architectures. By addressing security and performance simultaneously, the protocol?provides a path to future-proof energy networks which can support dependable operations in a quantum-enhanced environment. This could be future enhance for energy efficiency.
Deep Reinforcement Learning-Based Control Architectures for Autonomous Maritime Renewable Energy Platforms Sabah, Sura; Hussain, Refat Taleb; Mohammed, Ismail Abdulaziz; Jawad, Haider Mahmood; Abbas, Intesar; Hariguna, Taqwa
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.v5i4.1209

Abstract

Autonomous vessels driven by renewable energy are increasingly envisioned as vital for sustainable ocean?operations such as environmental monitoring, offshore power generation, and long-haul unmanned surface vehicles. Implementing fine-scale control of these systems has proven challenging however,?due to time-varying sea-state dynamics, sporadic energy inputs, the possibility of failure at the component level, and the requirement for coordination between multiple agents. In the article, an end-to-end deep reinforcement learning-based hierarchical control solution with real-time navigation and?its synthesis for energy optimization is proposed. It combines high-level energy regulation with low-level actuator scheduling so as to react to the variations of?the environment and internal perturbations. Simulations using actual wave realizations, sensor failures, actuator outages, and network communication variation were used?to demonstrate the performance of the control system in the following 5 performance aspects: energy saving, navigation accuracy, communication reliability, fault tolerant and multi-agent coordination. Results indicate that the architecture sustained over 80% of the performance and achieved energy efficiencies up to 54.5% in the?best case under failure scenarios. Performance-measures demonstrated reasonable scalability?up to 5–7 agents without significant communication overhead. The findings support the applicability of deep reinforcement learning for real-time maritime control under uncertainty, offering a viable alternative to conventional rule-based or predictive control strategies. The framework’s modular design allows for future integration with federated learning, hybrid control models, or autonomous deployment. The article contributes to the growing field of intelligent marine systems by providing a robust and adaptable control strategy for sustainable and scalable operations in autonomous maritime environments.
Effects of Curing Conditions and Combined Pozzolanic Material on Compressive Strength of Reactive Powder Concrete Jalalul Akbar, Said; Alkhaly, Yulius Rief; Maizuar, Maizuar; F Harahap, M Ibnu H
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.v5i4.1107

Abstract

Reactive Powder Concrete (RPC) is a type of concrete with an extremely dense matrix and high compressive strength. The compressive strength of RPC was examined in this study to evaluate the effects of the combination of silica fume (SF) and rice husk ash (RHA) with up to 50% by weight of cement, which provided the highest compressive strength and low cement content under normal curing and steam curing methods. The results showed that the combination of 5% SF or 10% SF with 25% - 45% RHA reaches compressive strength over 100 MPa at the age of 28 days with a low cement content of about 650 kg/m3 under both curing conditions and maintains the slump flow more than 200 mm. This study demonstrates that SF and RHA can be used up to 50% by weight of cement to produce RPC with a compressive strength of over 100 MPa.
Mapping and Analysis of the Effect of Noise on Auditory and Non-Auditory Disorders Among Workers at the PMKS Produc-tion Station of PT. Sisirau Amri, Amri; Erliana, Cut Ita; Nurjannah, Nurjannah
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.v5i4.991

Abstract

PT. Sisirau Palm Oil Mill Company is engaged in the production of crude palm oil (CPO) and kernel. In its production processes, the company continuously operates heavy machinery around the clock. These machines generate high noise levels, potentially causing both auditory (hearing-related) and non?auditory (communication, physiological, psychological, and work?productivity) disturbances among workers. This study aims to map the noise levels and analyse their impact on auditory and non?auditory disorders among workers at the production workstations of PT. Sisirau’s palm oil mill. Measurements were taken at 74 points across five production workstations: the kernel station, boiler station, engine room, clarification station, and press station. Using a Sound Level Meter, noise measurements were converted into equivalent continuous sound levels, followed by regression analysis employing the t?test to determine the relationship between noise exposure and worker disturbances. The results show that most measurement points at the production workstations exceeded the established Threshold Limit Value (TLV), with an average noise level of 98?dB. This indicates that noise levels in production areas are very high and require immediate reduction measures. Moreover, the statistical analysis revealed a significant correlation between noise levels and both auditory and non?auditory disturbances among workers (P-value = 0.002 0.05). In other words, as noise exposure increases, so does the risk of hearing impairment, communication problems, physiological and psychological effects, and reduced work productivity. These findings underscore the urgent need for noise control efforts, improvements to the working environment, and the implementation of more effective and consistent occupational health and safety policies to safeguard the health and safety of workers at PT. Sisirau’s palm oil mill.

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