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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 5 Documents
 1 
Search results for , issue "Vol 6, No 1 (2026)" : 5 Documents clear
Integrating E-Learning into The Vocational High School Curriculum: Strategies, Challenges, and Future Directions Hakiki, Muhammad; Kartika, Rani; Hamid, Mustofa Abi; Utami, Resti; Suprapto, Yuni; Subhanadri, Subhanadri
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1523

Abstract

This study investigates the integration of e-learning into the curricula of Vocational High Schools, emphasising strategies, challenges, and prospective avenues for enhancing the quality of teaching and learning. E-learning plays a crucial role in enhancing vocational education within the framework of rapid digital transformation, facilitating flexibility, interactivity, and access to a variety of learning resources. A qualitative approach was employed to collect data through in-depth interviews, surveys, and participatory observation at 39 Vocational High Schools in Padang, Indonesia, which offer Computer and Network Engineering and Software Engineering programs. The research included 73 teachers and 195 students who participated in e-learning activities. The findings indicate that both teachers and students possess favourable views regarding the integration of e-learning, recognising its capacity to improve conceptual understanding, promote learner autonomy, and create more engaging educational environments. Educators indicated that the implementation of learning management systems, online discussions, multimedia resources, and virtual simulations enhances student engagement and fosters collaboration. Data triangulation confirmed that teacher competence and engagement in professional development significantly impact e-learning success. Challenges remain, such as insufficient digital literacy, inadequate infrastructure, and diminished direct interaction. The research highlights the necessity of ongoing teacher training, dependable technological infrastructure, and curriculum redesign in accordance with industry standards to facilitate effective e-learning integration. It is advisable to establish collaborative partnerships among educational institutions, industry stakeholders, and policymakers to improve curriculum relevance and learning outcomes. Ongoing monitoring and evaluation are crucial for sustaining dynamic and adaptive e-learning environments. This study presents a framework aimed at enhancing e-learning implementation in Vocational High Schools, thereby facilitating students' preparedness for the digital and industrial workforce.
Application of Singular Value Decomposition for Image Compression of Yogyakarta Cosmological Axis in Digital Learning in Vocational Education Sahria, Yoga; Sudira, Putu; Salim, Mohamad Hidir Mhd
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1732

Abstract

This study examines the application of the Singular Value Decomposition (SVD) method as a digital image compression technique on the Yogyakarta Cosmological Axis object which is used as a digital learning medium in vocational education. The background of this study is based on the need for high-quality visual media with efficient file sizes for easy storage, transmission, and access through digital-based learning systems. The study uses an experimental quantitative approach with data in the form of high-resolution digital images processed through SVD-based compression stages. The research procedure includes image transformation into matrix form, matrix decomposition using SVD, selection of a number of dominant singular values (ranks), and reconstruction of the compressed image. The research data were analyzed using image quality evaluation parameters, namely Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Compression Ratio (CR). The results show that an increase in the rank value is directly proportional to an increase in the quality of the reconstructed image, as indicated by a decrease in the MSE value and an increase in the PSNR and SSIM values. Conversely, a decrease in the rank value results in a higher compression rate but is followed by a degradation in the visual quality of the image. Experimental data also shows that most of the visual information of an image can be represented by a small number of principal singular values, thus allowing for significant file size reduction without losing the important visual structure of the image object. Visually, the compressed image at a medium rank value is still considered suitable for use as a learning medium because the main details, object contours, and visual characteristics of the Yogyakarta Cosmological Axis can still be recognized well. These findings prove that the SVD method is effective as a mathematical-based image compression technique to support the development of efficient, informative, and contextual digital learning media based on local wisdom in vocational education
An Integration of Multicultural Perspective in Musical Theatre Education Using Pattern Mining Li, Jialin; Kim, Hyuntai
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1084

Abstract

Musical theatre education has traditionally focused on Western techniques, limiting students' exposure to the cultural diversity inherent in the art form. Current curricula frequently lack systematic methods for incorporating non-Western traditions, resulting in an underrepresentation of practices like Chinese musical theatre. To address this limitation, this study suggests developing a culturally inclusive curriculum framework based on the Multicultural Musical Theatre Dataset (MMTD) with the Hybrid Rule-Based Similarity Matching Algorithm (HRSMA). The HRSMA combines rule-based pattern extraction through frequent pattern mining with similarity-based classification via Jaccard Similarity. This hybrid approach allows for accurate categorization of various musical theatre styles into structured curriculum themes, ensuring balanced cultural representation. Experimental results validate the model's effectiveness, with 92.5% accuracy, 91.8% precision, 93.2% recall, along with an F1-score of 92.5%, indicating both efficiency and robustness in style classification. Beyond classification, the framework promotes pedagogical inclusiveness by systematically incorporating multicultural practices into theatre education. This approach goes beyond traditional Western-centric models to boost student creativity, broaden cultural understanding, and encourage critical thinking. The combination of frequent pattern mining with similar measures demonstrates creativity in balancing cultural inclusion with data-driven curriculum design. Furthermore, the findings demonstrate the practical advantages of this approach for both educators and students. At the same time, students are exposed to a broader range of cultural expressions, which fosters a deeper appreciation for global heritage and strengthens their artistic adaptability. This dual focus ensures that the curriculum remains both academically rigorous and culturally inclusive. To summarize, the proposed HRSMA-based framework offers a reproducible, methodical, and innovative approach to broadening the scope of musical theatre education. Incorporating global cultural practices improves classification accuracy while also promoting a cosmopolitan learning environment that values diversity, encourages engagement, and strengthens appreciation for world cultural heritage in performing arts training.
Evaluation of Weld Joint Quality in Drill Pipe Based on Heat Input Variation in Bending and Microstructure Test Sebayang, Alexander; Tarigan, Efrata; Kamil, Idham; Sutrisno, Joko
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1795

Abstract

Drill pipes are critical components in oil, gas, and geothermal drilling operations, serving as conduits for circulating drilling fluids from the surface to the borehole while simultaneously transmitting mechanical loads. Due to severe operating conditions, including high pressure, cyclic loading, and corrosive environments, the integrity of welded joints in drill pipes must be ensured through appropriate welding methods and quality evaluation. This study investigates the effects of heat input variations on the mechanical properties and microstructural characteristics of welded joints in ASTM A106 Grade B steel pipes produced using Shielded Metal Arc Welding (SMAW) and Gas Metal Arc Welding (GMAW). Welding was performed using a single V-groove butt joint configuration in the 1G position with a constant welding current of 140 A. SMAW welding employed E7018 electrodes, while GMAW utilized ER70S-6 filler wire with a shielding gas mixture of 70% argon and 30% CO?. Mechanical performance was evaluated through bending tests, including root and face bends, in accordance with applicable standards. In addition, microstructural analysis of the weld metal and Heat Affected Zone (HAZ) was conducted using optical microscopy after chemical etching to identify phase distribution and grain morphology. The bending test results revealed that SMAW-welded joints exhibited higher ultimate stress (?u) values than GMAW-welded joints. The highest ?u value of 1327.14 MPa was obtained from the SMAW face bend specimen, while the lowest value of 922.10 MPa was recorded for the GMAW root bend specimen. Furthermore, face-bend tests consistently yielded higher ?u values than root-bend tests for both welding methods, indicating superior mechanical resistance at the weld surface. Microstructural observations showed that SMAW welds exhibited coarser ferrite–pearlite grain structures due to higher heat input and slower cooling rates associated with flux-covered electrodes. In contrast, GMAW welds exhibited finer, more homogeneous microstructures due to lower heat input and faster cooling rates. These differences in microstructure significantly influenced the mechanical behaviour of the welded joints. The findings of this study provide valuable insights into selecting welding methods for ASTM A106 Grade B pipes, particularly in applications that require a balance between mechanical strength and microstructural stability
Comparison of Support Vector Machine and Naïve Bayes Algorithms Based on TF-IDF in Online Gambling Website Detection Refianti, Rina; Alhafiz, Husein
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1794

Abstract

The rapid growth of digital technology has significantly accelerated the spread of illegal online content, particularly gambling websites, which threaten social stability and regulatory enforcement. To address this issue, this study develops an automated detection system for online gambling sites using text classification with the Term Frequency–Inverse Document Frequency (TF-IDF) approach. A total of 1,225 website URLs were collected through web scraping, and after preprocessing, 1,166 valid entries were manually labeled into two classes: gambling and normal. The preprocessing steps included cleaning, tokenizing, stopword removal, stemming, and domain parsing, followed by feature extraction using TF-IDF, which generated 2,426 numerical features. To mitigate class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to the training dataset. Two machine learning algorithms were implemented and compared: Support Vector Machine (SVM) with multiple kernels (Linear, RBF, Polynomial, and Sigmoid) and Multinomial Naïve Bayes (MNB). Experimental evaluation was conducted using accuracy, precision, recall, specificity, and F1-score metrics. Results demonstrate that SVM with the RBF kernel achieved the best performance, with an accuracy of 91.88% and an F1-score of 93.70%, while MNB obtained an accuracy of 88.46% and an F1-score of 91.00%. These findings confirm that SVM, particularly with the RBF kernel, delivers more stable and accurate performance in distinguishing gambling websites from normal ones. The proposed system offers a reliable foundation for the development of automated tools to monitor, detect, and block illegal online gambling content, thereby supporting regulatory enforcement and reducing the negative societal impacts of online gambling.

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