cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Dahlan Abdullah
Contact Email
dahlan@unimal.ac.id
Phone
+62811672332
Journal Mail Official
ijestyjournal@gmail.com
Editorial Address
Jl. Tgk. Chik Ditiro, Lancang Garam, Lhokseumawe, Aceh - Indonesia, 24351
Location
Kota lhokseumawe,
Aceh
INDONESIA
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 73 Documents
 1   2   3   4   5 
Search results for , issue "Vol 5, No 4 (2025)" : 73 Documents clear
Artificial Intelligence Driven Skin Cancer Detection Using R-FCN Enhanced Deep Convolutional Neural Networks with SMOTE Balancing Doni, A Ronald; Shieh, Chin-Shiuh; S, Siva Shankar; Chakrabarti, Prasun; Nagarajan, G; Murugan, S
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.v5i4.1196

Abstract

Skin cancer is a serious worldwide health issue, and earlier diagnosis is crucial for patient outcomes and efficient treatment.  However, due to the variety of skin cancer types and the complexity of medical imaging, making an accurate diagnosis can be challenging.  This study tackles this issue by introducing a new deep learning (DL) algorithm that is specifically designed for skin tumor diagnosis and employs the Convolutional Neural Network (CNN) technology. This study offers a novel approach that makes use of Region-based Fully Convolutional Networks (R-FCN) to address the crucial problem of skin cancer lesion categorization. The suggested system seeks to increase classification efficiency by using region-based detection which improves classification accuracy and localization. The HAM10000 and ISIC-2020 datasets, which are difficult and unbalanced, were used to thoroughly evaluate the created Deep CNN (DCNN) architecture. The Synthetic Minority Over-sampling Technique (SMOTE) was purposefully used as the method of random sampling in order to lessen the imbalanced datasets. This greatly enhanced the suggested models generalization and robustness. The results demonstrate the remarkable efficacy of the research contribution, yielding performance metrics consistently above 98% for F1-score, specificity, sensitivity, recall, accuracy, precision, and the area under the ROC curve (AUC). In terms of balancing speed and accuracy the suggested approach also performs better than traditional methods like R-CNN and YOLOv8. The study demonstrates that a strong framework for automatic skin cancer detection and classification is provided by combining R-FCN with SMOTE and CNN techniques. This framework facilitates early diagnosis and aids dermatologists in clinical decision-making.
Analysis of Patient Service Quality Using the Importance Performance Analysis Method at Community Health Center Bakhtiar, Bakhtiar; Trisna, Trisna; Andriani, Adila
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.v5i4.1052

Abstract

Community Health Centres, as primary-level health service units, are responsible for providing a range of services, including health promotion, disease prevention, treatment, and rehabilitation.  This study aimed to determine the level of significance and patient satisfaction with the performance of the Community Health Centre's services, as well as the priority of attributes in meeting patient needs for quality of service at the community health centre. We used the Importance Performance Analysis method to identify service attributes considered important by patients and evaluate how well these attributes are implemented. The problem in this study was patient complaints about services, such as in terms of patient examinations, incomplete information to patients, and a lack of complete medicines. The stages of this research include identifying service quality attributes, developing a questionnaire, collecting data, analysing the data, interpreting the results, and making recommendations for service improvements. The results showed 20 attributes found in the needs of community health centre patients, which were manifested in five dimensions of service, namely tangibles, reliability, responsiveness, assurance, and empathy, which were distributed to 71 predetermined samples. Based on the results of data analysis, it was found that there were six primary priority sequences in improving the quality that needed to be carried out by the community health center based on the Importance Performance Analysis method, namely, the pharmacy has a complete collection of drugs, patient experts examine patients seriously, community health center medical personnel provide the required data effectively, officers receive and serve well, Health center officers are always patient in handling patient complaints, officers’ readiness to apologize for what happened.
Empirical Performance Analysis of Hyperledger Fabric Blockchain Network for Healthcare Das, Shampa Rani; Jhanjhi, NZ; Ashfaq, Farzeen; Ahmed, Husham M.; 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.v5i4.928

Abstract

The most prevalent blockchain-enabled systems have several apparent advantages, but scaling remains a technical difficulty that results in performance deficiencies in latency and throughput.  The foremost concern is that a thorough performance analysis is required to determine their viability and efficiency. The prospective impact of transaction delay on blockchain networks is an acute issue for e-healthcare-related services since it can jeopardize the patient’s life safety. A benchmarking tool Hyperledger Caliper is utilized to measure performance parameters in the Hyperledger Fabric network. The effects of workload fluctuation in 6 rounds with up to 3000 Transactions Per Second (TPS) are demonstrated when four organizations are put up in the network. Significant findings include a noteworthy decrease of 27.11% in open latency and 26.27% in query latency, and an increase of 3.13% in query throughput and 3.44% in open throughput demonstrating enhancements in adaptability and operational efficiency over the recent existing approaches proposed. It demonstrates an ongoing increase in CPU and memory consumption, peaking at 5.49% and 528.23 MB for 3000 TPS, respectively. Inbound and outbound traffic indicate relatively even utilization, with variations falling within a moderate range.
AI-Driven Framework for Location-Aware Sentiment Analysis and Topic Classification of Public Social Media Data in West Malaysia Faisal, Amna; Jhanjhi, NZ; Ashfaq, Farzeen; M. Ahmed, Husham; 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.v5i4.926

Abstract

While social media has facilitated communication, it has also amplified collective attitudes, often leading to polarized opinions and negative emotional expressions that can disrupt social harmony. Consequently, monitoring public sentiments on social media, and identifying thematic trends across regions has become crucial for understanding collective emotions and opinions. Despite advancements in sentiment analysis and topic classification, very little research has been done to integrate geospatial analysis with these techniques, limiting their ability to provide location-aware insights into public sentiments and discussion trends. This study develops an AI-driven framework that leverages social media data to analyze public sentiments and classify discussions into relevant topics. Specifically, this research focuses on understanding the emotions and conversations of Peninsular Malaysia citizens using a self-collected dataset of public Facebook posts, analyzed at the state level to provide location-aware insights. Using VADER for sentiment analysis and zero-shot transformer for topic classification, this study categorizes posts into five predefined topics: politics, religion, tragedy, tourism, and food. The proposed architecture achieves a sentiment classification accuracy of 97% and a topic classification accuracy of 89%. Findings reveal that the Peninsular Malaysian population generally maintains a positive online environment, though some states showed a dominant negative sentiment. Patterns of dissatisfaction were largely related to political issues and local incidents, while positive emotions were primarily associated with tourism, religious festivities, and food-related news. This research not only identifies areas with dissatisfied publics but also explores the topics contributing to this sentiment. By emphasizing location-aware sentiment and topic trends, this framework offers insights to help policymakers and sociologists address region-specific issues, potentially reducing dissatisfaction and fostering a more harmonious society.
Smart Campus Dropout Prediction: Hybrid Features and Ensemble Approach Safii, M; Nababan, Adli Abdillah; Husain, Husain
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.v5i4.1183

Abstract

The issue of the high number of students dropping out of college is a major concern in higher education, especially in the smart campus ecosystem. This research aims to design a prediction system for students who are at risk of dropping out by integrating hybrid feature selection methods and ensemble learning that leverage academic data and students' digital footprints. The initial process of model development involves data cleaning and the selection of important features through a combination approach using filter-based methods (mutual information) and recursive feature elimination. A classification model is then designed using the XGBoost and Random Forest algorithms. The testing was conducted using a secondary dataset that included variables such as participation in discussions, attendance rates, interaction with learning materials, and academic achievement. The results of testing with the XGBoost model showed a satisfactory accuracy level, with an F1 score of 0.77 and a ROC AUC of 0.89. The confusion matrix recorded 67 correct predictions for students who graduated and 17 correct predictions for students who dropped out, with a total of 12 misclassifications. These findings suggest that the combination of hybrid feature selection strategies and XGBoost can produce sufficiently accurate predictions of student dropouts and has the potential to be utilized as an early warning system in the governance of a more flexible and responsive smart campus.
Technology Adoption and Change Management in Smart Manu-facturing Industries Rakhmanovich, Ibragimov Ulmas; Sachdeva, Lalit
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.v5i4.1216

Abstract

The benefits of the Internet of Things are widely acknowledged.  Nonetheless, a number of articles and white papers continue to uncover security flaws in IoT, which is a major worry that the industry finds unacceptable.  It can be challenging to safely connect these systems to external networks.  Since the study focuses on technological acceptance, it's critical to comprehend additional factors that may influence IoT adoption.  To identify the elements that impact behavioural intentions to use technology to control user behaviour, a number of theoretical models have been examined. Since academics have provided a variety of explanations for why the adoption of IoT has been hampered in various works of literature, a comprehensive study that examines all of the factors at once and presents accurate data is required.  Therefore, determining the factors limiting the adoption of IoT in the manufacturing sector in and around Mumbai is the issue this study attempts to solve.  The study examines supportive conditions, performance expectancy, effort expectancy, and security awareness.  The study will investigate how these constructions are affected by large, medium, and small organisations.  The final study will assist company managers, IoT manufacturers, and service providers in boosting IoT adoption.
A Review of Palm Oil Valorization Technologies 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.v5i4.1171

Abstract

The accelerating expansion of palm oil cultivation has triggered a substantial rise in the volume of biomass waste, notably empty fruit bunches (EFB), palm kernel shells (PKS), and palm oil mill effluent (POME), which pose environmental challenges if unmanaged. In response to growing sustainability concerns, this study explores how technological innovations have enabled the valorization of palm oil waste streams within the framework of the circular economy (CE). This research aims to identify and evaluate the range of technologies developed to convert palm-based waste into value-added products and assess their comparative performance in terms of scalability, environmental benefits, and CE alignment. This study adopts a qualitative research approach using the Systematic Literature Review (SLR) method, structured according to the PRISMA protocol. Data were collected through a focused search on the ScienceDirect database using refined Boolean combinations relevant to CE, palm oil biomass, and valorization technologies. A multi-stage screening process involving relevance, article type, publication year (2021–2025), and open-access availability yielded 37 peer-reviewed research articles for in-depth analysis. Data were analyzed thematically and synthesized qualitatively. Findings reveal a diversification of valorization pathways, including anaerobic digestion, pyrolysis, hydrothermal liquefaction, nanomaterial extraction, and catalytic upgrading, each offering distinct advantages and trade-offs. Technologies varied significantly in scalability, environmental impact, and their contribution to CE objectives. The review concludes that integrated and decentralized valorization systems hold great promise for closing resource loops and reducing emissions. Future research should focus on region-specific lifecycle assessments and the techno-economic feasibility of hybrid technologies.
Stega Care: Securing Virtual Therapy Images with AI-Driven Image Forensics R, Manasa; Jayanthiladevi, A; Ahamed Ayoobkhan, Mohamed Uvaze
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.v5i4.1039

Abstract

With the rapid evolution of digital healthcare, virtual therapy platforms have become essential tools for delivering mental health services remotely. These platforms enhance accessibility, especially for individuals in remote or underserved areas. However, the transmission of therapeutic images—such as visual assessments or expressive content generated during sessions—raises significant cybersecurity concerns. Given the sensitive nature of such data, robust protection mechanisms are required to ensure privacy, integrity, and patient trust. This research proposes an artificial intelligence-driven framework designed to enhance the security of virtual therapeutic images by integrating image forensics and steganography techniques. Central to this approach is a deep learning-based steganalysis classifier capable of detecting hidden alterations and unauthorised data embedding in medical images. By leveraging convolutional neural networks (CNNs), the classifier accurately identifies covert manipulations while maintaining image fidelity and confidentiality. The system is trained and evaluated using benchmark steganographic image datasets, demonstrating high effectiveness in identifying steganographic threats and detecting tampered content in real-time. Experimental results indicate that the proposed model performs well even in complex attack scenarios involving sophisticated data-hiding techniques. The framework offers a scalable and proactive solution for safeguarding sensitive therapeutic content in telehealth environments. By embedding this AI-powered detection capability into virtual therapy platforms, healthcare providers can significantly enhance their cybersecurity posture.
The Implementation of CIPP Model To Evaluate the Illiteracy Eradication Program for the Baduy Traditional Community in Banten Province Wicaksana, Harits Hijrah; Tannady, Hendy; Gunawan, Indra
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.v5i4.1276

Abstract

The purpose of this study is to use the CIPP (Context, Input, Process, Product) evaluation model to assess the literacy eradication program among the indigenous Baduy community. Interviews, document analyses, and program implementation observations were used to gather data. This study used a descriptive approach and a qualitative technique. The researcher explained how the literacy eradication effort for the indigenous Baduy population in Lebak Regency was put into action. In addition to discussing the context, input, process, and results of the implementation of the literacy eradication program for the Baduy indigenous community, the study participants also talked about the perceptions and roles of the Lebak Regency government in the education sector and community leaders in the implementation. In addition, observations in the Baduy area (Leuwidamar) and examinations of policy documents and literature were incorporated in the data-gathering process. According to the evaluation's findings, there are gaps in data across agencies, no clear rules, and difficulties implementing them because of local knowledge and the values of the Baduy community. As a foundation for policy development, this study suggests a non-formal education strategy grounded in local culture and more precise data collection. Without making any attempts at adaptive planning, the administration seems to be blaming the Baduy culture for its failure. Especially in indigenous areas, the program's execution has not yet met its full potential. The Baduy community does not have any unambiguous quantitative or qualitative measures of the program's effectiveness. Success evaluations are less substantial and more administrative. Furthermore, the notion that the Baduy group rejects education has been strengthened by false impressions about them.
Improving the Classification Performance of SVM, KNN, and Random Forest for Detecting Stress Conditions in Autistic Children Melinda, Melinda; Yunidar, Yunidar; Miftahujjannah, Rizka; Rusdiana, Siti; Amalia, Amalia; Qadri Zakaria, Lailatul
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.v5i4.1206

Abstract

This paper addresses the critical challenges of managing stress in autistic children by introducing an innovative deployable system designed to detect signs of stress through continuous monitoring of physiological and environmental indicators. The system, implemented as a convenient portable detection system, measures key parameters such as heart rate, body temperature and skin conductance. The data is accessed in real-time and displayed on the Blynk application with an IoT system and viewed remotely via an Android device, allowing caregivers to receive instant notifications upon detection of potential stress symptoms. This timely alert system enables rapid intervention, potentially reducing stress intensity and providing peace of mind to caregivers. The study further compares three powerful data analysis methods namely Support Vector Machine (SVM), K-nearest neighbors (KNN) and Random Forest (RF) in interpreting the collected sensor data. The SVM-based system achieved a fairly good detection accuracy of 90%, KNN also showed excellent results of 92% while the Random Forest-based system showed superior performance with an impressive accuracy of 95%. These findings suggest that the Random Forest method exhibits a superior level of effectiveness in accurately predicting the onset of stress conditions., providing the importance for technological advancements that can be applied in supporting better management of autism-related behavioral defenses.

Page 2 of 8 | Total Record : 73

 1   2   3   4   5 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 6, No 1 (2026) Vol 5, No 4 (2025) Vol 5, No 3 (2025) Vol 5, No 2 (2025) Vol 5, No 1 (2025) Vol 4, No 4 (2024) Vol 4, No 3 (2024) Vol 4, No 2 (2024) Vol 4, No 1 (2024) Vol 3, No 4 (2023) Vol 3, No 3 (2023) Vol 3, No 2 (2023) Vol 3, No 1 (2023) Vol 2, No 4 (2022) Vol 2, No 3 (2022) Vol 2, No 2 (2022) Vol 2, No 1 (2022) Vol 1, No 4 (2021) Vol 1, No 3 (2021) Vol 1, No 2 (2021) Vol 1, No 1 (2021) More Issue