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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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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.
Smart Cropping Pattern: A Systematic Study of Sustainable Agriculture Optimization Model Mushthofa, Mushthofa; Suripin, Suripin; Wulandari, Dyah Ari; Qomaruddin, Mochammad
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.v5i3.1132

Abstract

Irrigated agriculture accounts for more than 40% of global food production despite covering only about 20% of the world's agricultural land. However, climate change, water constraints, and multisectoral pressures on natural resources demand greater efficiency in the management of agricultural systems. One key strategy is determining optimal cropping patterns under conditions of water and land constraints. This study aims to review mathematical approaches, especially Linear Programming (LP)-based optimization models, in developing efficient and sustainable cropping pattern strategies. This study was conducted through a systematic literature review of 185 scientific articles from the Scopus and ScienceDirect databases in the period 2014–2025. The analysis was carried out using the PRISMA method and visualization of research trends through VOS viewer software. The results of the review indicate that optimization models, especially Linear Programs, have been widely used to develop data-based land and water allocation strategies, considering agronomic, economic, and environmental aspects. The increasing number of publications in the last decade reflects the urgency of this theme and the shift towards quantitative-based decision-making in agricultural systems. This study provides a conceptual and applicative basis for the development of sustainable planting strategies that are adaptive to environmental changes.
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.
Emerging Carbon Capture Technologies in the Palm Oil Industry: A Review of Bioenergy and Carbon Capture Storage Approaches Judijanto, Loso
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.v5i3.1170

Abstract

The accelerating impacts of climate change have heightened global interest in technologies that reduce greenhouse gas emissions from high-emission sectors such as agriculture and agri-processing. The palm oil sector is notably both a significant emitter and a promising avenue for decarbonization efforts, particularly through the integration of bioenergy systems and carbon capture technologies. This study aims to explore the current state of technological development in carbon capture and storage (CCS) and bioenergy applications within the palm oil industry and to identify the major challenges and opportunities that shape their implementation between 2021 and 2025. This investigation employs a qualitative design through the SLR method, structured in accordance with the PRISMA framework for transparency and rigor in literature synthesis. Data were collected from the ScienceDirect database using a refined combination of Boolean search terms. A total of 1,088 articles were initially identified and screened through a multistage filtration process that included relevance checks, publication period constraints, research article type, and open-access availability. This process resulted in 36 articles that met all inclusion criteria and were analyzed further. Data were synthesized through thematic analysis to classify technological pathways, assess implementation trends, and evaluate technical, economic, and policy-related barriers. Findings reveal that while bioenergy from palm oil residues is widely adopted, CCS deployment remains minimal due to cost, infrastructure, and regulatory limitations. The study concludes that targeted policy support and innovation are essential to scaling up carbon management in this sector. Future research should prioritize pilot demonstrations and interdisciplinary assessments of CCS integration feasibility.
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.
Analysis of the Influence of Infinite Mindset Through Innovation and Learning Ability on Business Sustainability Aulia, Muhammad Reza; Salsabila, Cut; Nasution, Anisah; Fuqara, Fanthasir Awwal; Azrani, Utary; Pratiwi, Henny; Tannady, Hendy
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.v5i3.1181

Abstract

In today's era of globalisation, business development is growing rapidly and undergoing continuous metamorphosis. The coffee business itself has a long history of driving economic growth in Indonesia. Geographically, Indonesia's soil is ideal for the microclimate that supports coffee growth and production. According to the 2023 Indonesian Statistics report from the Central Statistics Agency (BPS), Indonesian coffee production reached 794.8 thousand tons in 2022, an increase of approximately 1.1% compared to the previous year. Methods and approaches in the current era of globalisation have accelerated business development and continued transformation. The coffee industry is a promising sector with significant economic potential, particularly in West Aceh Regency. This study aims to analyse the influence of an infinite mindset on the sustainability of coffee shop businesses, both directly and indirectly through innovation and learning ability as mediating variables. This study used an associative quantitative approach with a sample of 119 coffee shop owners or managers selected using purposive and convenience sampling techniques. Data were collected through questionnaires and analysed using the Partial Least Squares (PLS) method with the help of the SMART PLS 4 software. The results showed that an infinite mindset has a positive and significant effect on innovation and learning ability. Furthermore, both innovation and learning ability also have a positive and significant effect on business sustainability. An infinite mindset was also proven to have a significant indirect effect on business sustainability through these two mediating variables. These findings emphasise the importance of a long-term mindset and continuous learning in facing dynamic business competition.

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