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Journal of Applied Science, Engineering, Technology, and Education
Published by PT. Mattawang Mediatama Solution
ISSN : -     EISSN : 26850591     DOI : https://doi.org/10.35877/454RI.asci1116
Core Subject : Science, Education, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other
Journal of Applied Science, Engineering, Technology, and Education (ASCI) is an international wide scope, peer-reviewed open access journal for the publication of original papers concerned with diverse aspects of science application, technology and engineering.
Arjuna Subject : Umum - Umum
Articles 299 Documents
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Needs Analysis of the CG2G Digital Module for ICT and Environmental Literacy Nor Khairunnisa Husna Binti Kamaruzaman; Kamisah Osman; Nurazidawati Mohamad Arsad
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 3 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4395

Abstract

The integration of ICT literacy and environmental education plays a vital role in fostering sustainable learning. Nevertheless, rural students continue to encounter barriers such as limited digital competence, unequal access to technology, and insufficient educational resources. This study examined the learning needs for developing the CG2G Digital Module, which integrates ICT and environmental literacy within the ASSURE instructional design framework. A quantitative survey was administered to 47 Year Six students from a rural primary school in Hulu Terengganu, Malaysia. Data obtained through a validated questionnaire based on the VAK Learning Styles Self-Assessment were analyzed descriptively to identify learners’ characteristics, competencies, and preferences. The findings indicated moderate ICT proficiency, particularly in basic computer and internet use, alongside strong environmental awareness in areas such as recycling and water conservation. The dominance of kinesthetic learning preferences suggests the need for multimodal and interactive instructional strategies. These insights provide a foundation for designing the CG2G module to enhance ICT and environmental literacy among rural learners.
From Digital Transformation to Human-Centric Leadership: Higher Education's Role in Preparing V-Shaped Graduates for Industry 4.0/5.0 Mohini Pooja Huggahalli; Divya Kirti Gupta
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 3 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4396

Abstract

The paper discusses the leadership skills of Industry 4.0 and 5.0 and how higher education institutions (HEIs) would help in enhancing these skills among engineering and management students. Nevertheless, existing research on the I4.0/I5.0 leadership development in HEIs is rather disjointed, with only a handful of studies centering on these population groups, so this review can be described as an exploration one. The PRISMA guidelines were used to conduct a systematic literature review (SLR), which analysed 61 peer-reviewed publications dating back to 2011. The review found three groups of invaluable competencies, including (a) technical (digital fluency, risk management), (b) human-centric (empathy, trustworthiness, ethical foresight), and (c) strategic (systems thinking, resilience). The current research points at the weakness of conventional theories of leadership and suggests a hybrid approach that combines technology, ethics, and sustainability. Unequal empirical focus within competency clusters is also evident in the synthesis and specifically in sustainability-oriented and student-specific leadership outcomes. The article is not oriented to determine the efficacy of the revealed leadership competencies or higher education practices but rather a synthesis of descriptive and conceptual information to provide insight on patterns, gaps, and the future of research in the still-new I4.0/I5.0 leadership conversation. The present paper builds on the upcoming V-shaped graduate construct that has been introduced in the new literature and compiles an idea of a conceptual framework of leadership preparedness, placing HEIs as knowledge management ecosystems facilitating sustainable leadership to transform digital-societal change. The conceptual propositions present a basis on which future empirical validation can be done in various HEI settings. The research provides practical recommendations to the HEIs and policymakers; it pertains to curriculum reorientation, hands-on learning, and collaboration between the industry and the academic field to equip the graduates to work within volatile, uncertain, complex, and ambiguous (VUCA) environments.
Improving Effects of Shilajit on Monosodium Glutamate Induced Testicular Changes in Male Mice Hadeel Khaleel; Saja Muzahem Thaker; Haneen Abdulsalam Ali
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 3 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4397

Abstract

Several health conditions are thought to be associated with monosodium glutamate (MS.G). Nowadays, a lot of products depend on monosodium glutamate as a flavor enhancer and food additive. The usage of MS.G is still debatable, even though it has been considered a safe food additive. A delayed excitotoxic and tissue-damaging dietary additive, MS.G, can produce large quantities of free radicals that cause metabolic disturbance and damage several organs. The study aimed to investigate the impress of monosodium glutamate in the testis of mice and the significance of Shilajit (Shi) in improving the damage. Fifty male albino mice weighing between 18–22g were used in the study. The animals were randomly selected into five groups. GroupA received distilled water as a control, groupB give (2g/ kg) from (MS.G), and group C gives (2g/ kg) of (MS.G) and (100mg\ kg) of Shilajit (Shi), group D gives (4g/ kg) of (MS.G), and groupE gives (4g/ kg) of (MS.G) and (200mg/ kg) of Shilajit (Shi), orally for 2 weeks. Blood collected for hormones biochemical examination. The mice were sacrificed at the end of the experiment, and the testes were separated for histological study. The results showed a significant decrease in testis weight in groups B, C, and D compared to the control, while no change in group E. The results indicated, a significant decrease at the FSH and LH. While, testosterone and progesterone levels showed a decrease in groups B, C, and D, but groupE was normal. The capsule thickness showed a significantly increased in the tested groups, except group E appeared relatively normal. Numerous histological alterations in the testis were discovered by the MS.G groups, including disarray of seminiferous tubules, decreased sperms in the lumen, Hyalinization with necrosis of Leydg’s cells and destruction of intermediate connective tissue cells, increased thickness of the testis capsule. Spermatogonia cells slouching in the seminiferous tubule lumens, and destruction of sertoli cells, necrosis of spermatogonia, and macrophage infilteration in the seminiferous tubule lumen, degeneration and destruction of intermediate connective tissue cells, disorganization of spermatogonia and spermatocytes, Slouching of spermatocytes in the seminiferous tubule lumen, Congestion of blood vessels, Infilteration of inflammatory cells in the lumen of tubules. While, the E groups exhibit extremely typical seminiferous tubules, hyalinization of interstitial tissue with destruction of Leydig’s cells, normal capsule, some vacuoles or spaces between spermatocytes, virtually typical spermatogonia, few amount of sperms and relatively normal spermatocyte. In conclusion, Shilajit (Shi) acts synergistically in reducing MSG-induced testicular changes via anti-inflammatory and anti-degeneration effects.
A Comparative Study of Bioinformatics Skills of Pre-service Biology Teachers with and without a Bioinformatics Course Indah Juwita Sari; R. Ahmad Zaky El Islami; Citra Maida; Diana Yustika; Muhammad Iman Santoso
Journal of Applied Science, Engineering, Technology, and Education Vol. 8 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

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Abstract

The rapid growth of bioinformatics has significantly influenced both the biological sciences and education, underscoring the importance of equipping future biology educators with relevant analytical and computational skills. This study aimed to compare the bioinformatics skills and biological knowledge of biology education students with and without experience in bioinformatics courses. A comparative descriptive design was employed, involving 49 students: 26 with no prior experience in bioinformatics and 23 with experience in bioinformatics. Data were collected using an instrument consisting of three opinion-based items and three cognitive test items. Qualitative data were analyzed thematically and visualized through diagrams, while cognitive performance was analyzed using descriptive statistics. The outcomes show that students who took the bioinformatics course demonstrated a broader use of tools, more reflective thinking about their own learning, and encountered more difficult problems, along with greater gain in scores on biology knowledge (70.29%) compared to students who did not take the course (63.63%). These findings support the need to include bioinformatics in the biology education program to better prepare pre-service biology teachers with the science and technology advancements in the future
A Real-Time Intelligent Traffic Controller at Signalized Intersections in Samawah City Ameera Mohamad Awad; Noorance Al-Mukaram; Salah Alheejawi; Ameer W. Abdul Sattar
Journal of Applied Science, Engineering, Technology, and Education Vol. 8 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

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Abstract

This study aims to develop algorithms of real-time traffic control system based on computer vision. In addition, the developed system is capable to analyze vehicles in a traffic stream at a traffic-controlled junction. The video recording sequence was installed near an intersection to control traffic lights during traffic congestion situations. This project's scope is limited to analyzing real-time traffic feeds and developing methods that count and track moving and stopping vehicles that approach a traffic junction. The project was designed using SIMULINK, which MathWorks created. Four algorithms were proposed to analyze the video signal inputs and estimate the number of vehicles detected. Gaussian mixture model and edge detection with frame differencing method were used to detect and track arrived vehicles. An optical flow-based approach was used to determine the number of stopped vehicles. Additionally, a vehicle classification algorithm was used to detect certain types of vehicles. In the Gaussian mixture model algorithm, implementing trained mask and geometric transform on each frame improved the perception of the outputs, which is defined by counting 1100 vehicles on the approach. Also, by using color detection, more control over traffic flow was obtained by prioritizing certain cars. The obtained results showed good representation of vehicle classification for the data detected in the developed system compared with the empirical data. The estimated errors were determined by achieving RMSPE < 15%, GHE < 5 and Um < 1.
Data-Driven Insights of the Ecotheology Implementation at Islamic Schools in Indonesia using Machine Learning Dian Sa'adillah Maylawati; Cepy Slamet; Muhammad Khalifa Umana; Akhmad Ridlo Rifa'i; Rohmat Mulyana; Muhammad Ali Ramdhani; Syafi’i Syafi’i
Journal of Applied Science, Engineering, Technology, and Education Vol. 8 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

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Abstract

Ecotheology is an integration of religious values towards awareness of environmental preservation. Indonesia’s Ministry of Religious Affairs has identified ecotheology as a strategic program, including Islamic school students. Therefore, this study aims to reveal the understanding, implementation, challenges, and opportunities of ecotheology in Islamic schools. This research applies data science and machine learning algorithms to analyze a large student dataset, 22,933 data from 32 provinces, with a 41-question validated questionnaire (Cronbach’s Alpha = 0.765, Kappa = 0.791). This research uses K-Means and PCA for clustering to group students by ecotheology awareness and implementation, Association Rules with Apriori algorithm to identify knowledge sources, obstacles, and program linkages, classification using ensemble learning with CatBoost as the best model with 98.71% accuracy, and sentiment analysis using RoBERTa-based Indonesian model on open responses. This research found that students’ understanding of ecotheology is high, with most learning from teachers and others gaining knowledge from social media and books, while implementation remains moderate due to limited programs, policies, and subject integration. In accordance with student’s understanding, the sentiment analysis revealed neutral tones in suggestions but mostly positive expectations, with students desiring more practical, Quran-linked, and community-based activities.
Explainable Dynamic Weighted Ensemble Learning for Depression Risk Stratification and Tiered Intervention in University Students Youhao Wang; Wirapong Chansanam; Lan Thi Nguyen
Journal of Applied Science, Engineering, Technology, and Education Vol. 8 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

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Abstract

Depression among college students is a growing public health concern, with existing screening methods often limited in sensitivity, scalability, and interpretability. This study developed and validated an explainable machine learning framework for early depression risk identification and tiered intervention planning in universities. We propose a Dynamic Weighted Ensemble Model (DWEM) that integrates five tree-based algorithms, with weights optimized via Bayesian search and cost-sensitive learning. Informed by the diathesis–stress framework, features were engineered and interpreted using SHAP to provide global and local explanations. The model was evaluated using stratified five-fold cross-validation with careful control of data leakage. The DWEM achieved an accuracy of 94.96% and an AUC of 98.95%, with balanced sensitivity and specificity, outperforming both single-model benchmarks and traditional questionnaire-based screening. SHAP analysis stably identified academic performance, stress-burnout, sleep problems, and protective factors as key risk determinants. Based on these outputs, a probability-based three-tier intervention framework was designed to translate risk stratification into actionable clinical support. This study demonstrates that an optimized ensemble approach, combined with theory-driven features and robust explainability, can provide a reliable, transparent, and practical tool for scalable mental health screening, supporting a shift toward proactive, data-driven prevention and efficient resource allocation in campus settings.
Developing a Bilingual English-Arabic Dataset for Textbook Question Answering: A Hybrid Translation and Validation Approach Amani Jamal
Journal of Applied Science, Engineering, Technology, and Education Vol. 8 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

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Abstract

Textbook Question Answering has been a central feature of educational artificial intelligence enabling curriculumaligned machine reading to support personalized learning and diagnostic testing. While there is significant advancement in English-language TQA datasets, there is still a lag in Arabic because of a lack of sufficient highquality domain-specific resources. A new bilingual English-Arabic TQA data is presented in this paper, and it was created using a hybrid translation and validation method. It combines machine translation of CK12-QA dataset with Google sheet translator. Semantic consistency was evaluated using automated metrics based on multilingual sentence embeddings and translation quality scores. Cosine similarity (0.87) and BLEU score (38.5) confirmed strong semantic equivalence and translation reliability across the bilingual dataset. These results demonstrate robust linguistic alignment and completeness. This approach is a balance between conflicting scalability and accuracy in long-standing semantic drift, morphological variation and in context misalignment issues in Arabic education datasets compared to previous efforts to use machine translation or mini-batch annotation only. Output dataset has a parallel format structure of English-Arabic question-answer pair that facilitates simple cross-lingual research in multiple-choice and textbook conditions. By focusing on K-12 science curriculum in specific subject areas, this contribution can enable improved monolingual and cross-lingual educational QA applications model training and testing. This does not only make AI-based learning more inclusive among Arabic students but also provides impetus to creation of cross-lingual transfer learning and benchmarking in TQA. The sources and information are openly published in an attempt to further increase the reproducibility, verifiable peer cooperation and further promote the development of AI in multilingual education
Machine Learning and Statistical Model Hybrid Approach to Optimizing Financial Data Prediction Abdelgalal Abaker
Journal of Applied Science, Engineering, Technology, and Education Vol. 8 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

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Abstract

Accurate price volatility prediction is a cornerstone of sound investment decisions and effective dynamic risk management in financial markets. This study addresses a significant research gap: the limited number of studies exploring the systematic integration of traditional statistical models and artificial intelligence techniques within emerging financial markets, despite their high levels of instability and volatility. The research aims to develop a hybrid predictive framework that combines the flexibility of linear models, specifically ARIMA, with the ability of machine learning algorithms to grasp the complex, nonlinear patterns inherent in financial time series. Furthermore, the study highlights an application gap: the underutilization of advanced volatility estimators. The Garman-Class estimator was adopted as a more efficient and accurate alternative to traditional estimators for measuring daily volatility, due to its reliance on four-part price information (open, close, high, and low). The proposed framework was applied to data from Savola Group, listed on the TASI. The results demonstrated the superiority of the proposed hybrid model in improving forecast accuracy and reducing predictive error measures, particularly the MAE, and RMSE, compared to traditional single-model models. The scientific value of this research lies in its contribution to bridging the knowledge gap related to the integration of statistical models and artificial intelligence techniques in the emerging markets environment. Furthermore, it provides an advanced analytical tool that can enhance asset allocation efficiency and support decision-makers and portfolio managers in navigating the dynamics of highly volatile markets.

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