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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 10 Documents
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Search results for , issue "Vol. 8 No. 1 (2026)" : 10 Documents clear
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)
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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)
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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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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)
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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)
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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.
Exploring Support Activities of Retail Value Chain That Influence Retail Shoppers’ Behavior: Ordered Probit Model Approach Mahendra Reddy Bogala; Venkata Adinarayana Rao Uppu; V. V. Devi Prasad Kotni; Tanikella VNL Raghu Babu; S. Varalakshmi; Rajesh Vemula
Journal of Applied Science, Engineering, Technology, and Education Vol. 8 No. 1 (2026)
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The support activities of the value chain model include procurement, firm infrastructure, human resource management, and technology management. This study aims to assess the influence of retail value chain management support actions on customer satisfaction. This study is based on Michel Porter's (1985) generic value chain model, which strives to provide organizations with a competitive edge. This is an empirical study in which 500 retail customers provided primary data using a structured questionnaire covering factors related to retail value chain management support activities. These qualities were uncovered after focus group talks with merchants and retail specialists from a variety of organized retail locations. The ordered probit model was used to examine how major retail value chain management procedures affect consumer satisfaction. Based on the analysis done, the managerial and theoretical implications were discussed in detail, and a model of Support Activities of Retail Value Chain Management was proposed based on the evidence. This research outcome contributes towards SDG12 of the United Nations Sustainable Development Goals.
Machine Learning Approach for Ibing Penca Stance Recognition Using Landmark Detection and Angle-Based Classification Ratnadewi; Agus Prijono; Aan Darmawan Hangkawidjaja; Sri Rustiyanti; Deri Al Badri
Journal of Applied Science, Engineering, Technology, and Education Vol. 8 No. 1 (2026)
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The problem is how students can learn independently when no assistant teachers are present. The main objective of this research is to build an application system that can recognize the stances in Ibing Penca martial art. This research aims to help facilitate independent learning for martial arts practitioners, especially Ibing Penca, by developing a system that is able to recognize and classify the movements in 62 Ibing Penca stances. To achieve these goals, the research method used is to collect input data in the form of images or videos taken using an Orbbec camera. After the images are obtained, the next stage is data processing to detect important points on the body using landmark detection techniques. The next process is the identification of 33 keypoints on the body using the MediaPipe algorithm. From these keypoints, six important angles were calculated which included the right arm, left arm, right leg, left leg, right foot and left foot. This angle calculation is done using the angle method of the three relevant key points. The system is able to recognize the movements in Ibing Penca with a high degree of accuracy, which is very useful for learners who want to practice independently. The results of this study show that the system is able to classify 62 Ibing Penca moves with a success rate of 95.2% (58 moves), while the error rate is only 4.8% (4 moves). For future research, it is expected to develop this system by adding variations of movements and improving detection accuracy in more diverse environmental conditions.
Development of Bulletin-Integrated Character Education (BICE) Media to Enhance and Assess Scientific Literacy Muhammad Fath Azzajjad; Anang Wahid M. Diah; Afadil; Sitti Rahmawati; Astija; Dewi Satria Ahmar
Journal of Applied Science, Engineering, Technology, and Education Vol. 8 No. 1 (2026)
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In higher education, scientific literacy is still a significant difficulty, especially when instruction focuses on procedural knowledge without including moral principles. The purpose of this study was to create and examine Bulletin-Integrated Character Education (BICE) as a teaching tool to improve and gauge students' scientific literacy. The requirement for educational materials that simultaneously promote character development and higher-order thinking makes this research urgent. In order to produce verified educational media, this study used a Research and Development (R&D) approach based on the Hannafin and Peck paradigm. According to expert validation, the BICE media was highly reliable (Cronbach's ? = 0.88–0.92) and valid (CVI = 0.92–0.95). Strong dependability was also shown by the scientific literacy test (? = 0.92). Students who were taught PBL–BICE fared better than those who were taught PBL alone, according to inferential analysis, with a significant effect size (d = 1.12). Conceptual literacy was a strong predictor of multidimensional literacy, according to correlation and regression analyses (r = 0.71; ? = 0.48). These results suggest that in order to foster advanced scientific literacy in higher education, character-based media should be incorporated into problem-based learning.
Risk Identification and Control in Oman’s Construction Industry: A Systematic Review and Conceptual Framework ‪Rashid Salim
Journal of Applied Science, Engineering, Technology, and Education Vol. 8 No. 1 (2026)
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The construction industry in Oman faces significant risks that influence cost, time, and overall project performance. Although risk management has been widely studied globally, research focusing specifically on Oman and the Middle Eastern context remains limited. This study manages this gap by conducting a systematic literature review on risk identification and control strategies in construction projects. Eligible studies were selected by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. SCOPUS and ScienceDirect were selected as the leading journal databases. A total of 33 articles were selected for further analysis through this procedure. The review synthesises findings from recent studies, highlighting critical risk identification and classification, including financial, environmental, operational, logistical, regulatory, legal, health and safety, and market risks, as well as strategies for mitigating them. Several recommendations were also suggested to provide the essential knowledge and information for future research. Based on the insights gained, a conceptual framework is proposed to guide risk control practices in Oman’s construction industry.

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