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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 285 Documents
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From Ethics to Impact: Modeling the Role of AI Perception Dynamics in the Relationship Between Ethics AI Practices, AI-Driven Societal Impact, and AI Behavioral Analysis Fakhri, M. Miftach; Jannah, Devi Miftahul; Isma, Andika; Dewantara, Hajar; Nirmala S., Aprilianti
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3802

Abstract

The rapid evolution of Artificial Intelligence (AI) has brought significant changes across various sectors, including healthcare, finance, and criminal justice, presenting both remarkable opportunities and complex ethical challenges. As AI becomes increasingly embedded in decision-making processes, concerns about individual rights, social equity, and public trust are growing, especially in high-stakes contexts. These ethical implications underscore the critical need for robust frameworks that emphasize AI transparency, accountability, and fairness to mitigate risks such as bias and ensure responsible usage. Despite the increased focus on ethical AI practices, there remains a considerable gap in understanding how these frameworks impact societal perceptions and behaviors toward AI. This study seeks to address this gap by investigating the effects of ethical AI practices—specifically transparency, accountability, and fairness—on public perceptions and behaviors. The study employs a quantitative approach, using purposive sampling to select a sample of AI-knowledgeable participants and analyzing the data with Partial Least Squares Structural Equation Modeling (PLS-SEM). This methodological approach allows for a detailed exploration of the relationships between ethical AI practices and societal impacts. Additionally, the study examines the mediated pathways through which these ethical practices influence AI’s societal and behavioral impacts, hypothesizing that transparency and accountability foster trust and positive engagement. By developing a framework that aligns ethical AI practices with societal values, this study aims to advance the broader goals of societal trust, public acceptance, and sustainable social integration of AI technologies. These insights contribute to the growing body of knowledge on responsible AI deployment, supporting ethical alignment in diverse AI applications and promoting trustworthiness in AI-driven systems
Time Series Innovation: Leveraging BetaSutte Models to Enhance Indonesia's Export Price Forecasting Ahmar, Ansari Saleh; Boj, Eva
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3831

Abstract

This study introduces a novel application of the Modified Trend-Augmented α-Sutte Indicator (BetaSutte) model for forecasting Indonesia's export prices and compares its performance with the traditional ARIMA approach. Accurate export price forecasting is crucial for economic planning, trade policy formulation, and business strategy development in Indonesia's dynamic and globally connected economy. Using monthly export value data from January 2022 to September 2024 obtained from Indonesia's Central Bureau of Statistics (BPS), we examined whether the BetaSutte model's decomposition of trend and residual components offers enhanced predictive accuracy over the conventional ARIMA methodology. Results show that while the ARIMA(0,1,0) model demonstrated superior in-sample performance (Training MAPE: 7.71% vs. 80.78%), the BetaSutte model achieved better out-of-sample forecasting accuracy (Testing MAPE: 11.22% vs. 11.61%). The BetaSutte model's linear trend component identified a negative slope (coefficient: -158.4), indicating a systematic decline in Indonesia's export values over the study period, which has important implications for trade policy. Furthermore, the model successfully captured the volatility in export prices through its residual forecasting component. These findings suggest that the BetaSutte model's explicit modeling of trend components provides meaningful advantages for export price forecasting, despite its more complex implementation. This research contributes to the growing literature on hybrid forecasting methodologies and offers practical guidance for stakeholders interested in Indonesia's international trade dynamics. For policymakers, the results highlight potential challenges for Indonesia's export competitiveness and suggest the need for targeted interventions to address the identified downward trend in export values.
A Bibliometric Analysis of Complex Problem-Solving Approaches in Engineering Education Azizan, Mohd Fikri; Mohd Matore, Mohd Effendi Ewan; Omar, Marlissa
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3846

Abstract

The evolving landscape of engineering education requires the development of advanced cognitive skills, particularly complex problem-solving (CPS). As engineering challenges grow in complexity, CPS has become a vital competency. However, research on CPS in engineering education remains scattered. This study presents a systematic bibliometric analysis to uncover trends, key contributors, and thematic focuses in CPS-related research. Using the Scopus database and VOSviewer 1.6.20, the study analyzed publications based on co-authorship, co-citation, and keyword co-occurrence. The five-stage bibliometric approach by Masitoh et al. (2021) and Bukar et al. (2023) was adopted, encompassing keyword selection, data retrieval, screening, analysis, and visualization. Findings show a notable rise in CPS publications after 2010, peaking between 2022 and 2024. Dominant keywords include “active learning,” “simulation,” “artificial intelligence,” and “project-based learning,” indicating a shift toward AI-driven, technology-enhanced approaches. China, the United States, and India lead in research output, reflecting global efforts in reforming engineering education. The study highlights the growing emphasis on interdisciplinary and problem-based learning. Despite this momentum, regional disparities remain. Insights from this analysis are valuable for curriculum developers, educators, and policymakers to enhance CPS integration and guide future research toward more holistic and inclusive approaches
The Validity of the Malaysian Teachers’ Global Competency Level Instrument Using Cohen Kappa, Content Validity Ratio and Content Validity Index Analyses Ibrahim, Siti Nurul Aqiedah; Mohd Matore, Mohd Effendi Ewan
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3848

Abstract

Highly competent teachers are vital in developing globally competent individuals. There is a lack of empirical evidence supporting the validity aspect of the Malaysian Teachers' Global Competency Level Instrument. This study aims to evaluate face and content validity. A survey research design with a quantitative approach was conducted. It involves two experts for face validity and eight experts for content validity using purposive sampling techniques. For face validity, the two experts appointed were Malay language teachers with more than five years of teaching experience. To assess content validity, eight experts, including four professionals in measurement, evaluation, and global competency, and four field practitioners: teacher educators, and outstanding teachers. The instrument consists of 73 items with four constructs: self-awareness, global awareness, attitudes & values, and skills. The analysis involved Cohen’s Kappa for face validity, while content validity involved CVR and CVI. The results showed that face validity was (N=2, k=0.640), while for content validity, (N=8, CVI=0.95, and CVR=2 items refined). This instrument demonstrates strong validity as a measurement tool for the global competency level of Malaysian teachers. Further studies are recommended to be conducted: employing an advanced statistical analysis, like the Rasch Model, to enhance higher-quality items.
A Decision-Centric Approach to Risk Management in Aviation Stock Investments Using Value at Risk and Portfolio Optimization Singagerda, Faurani Santi; Pratama, Muh. Riyaldi; Alfairus, M. Qodri; Iskandar, Akbar; Mamadiyarov, Zokir
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3870

Abstract

This study applies Monte Carlo simulation to analyze and compare the Value at Risk (VaR) of two Indonesian airline stocks—PT Garuda Indonesia (full-service carrier) and PT AirAsia Indonesia (low-cost carrier)—using daily return data from January to December 2023. The research examines risk-return characteristics at individual stock and portfolio levels across different confidence intervals (99%, 95%, and 90%). Results reveal that PT Garuda Indonesia exhibits higher expected returns (0.5168%) but also higher volatility (3.5980%) compared to PT AirAsia Indonesia (0.2412% return, 2.4868% volatility), reflecting their different business models. Remarkably, an equal-weight portfolio demonstrates extraordinary diversification benefits, with positive VaR values across all confidence levels, indicating robust downside protection even in adverse market conditions. At 99% confidence, the monetary VaR for a Rp100,000,000 investment shows potential maximum losses of Rp7,984,331 for Garuda and Rp5,460,951 for AirAsia, while the portfolio generates a minimum gain of Rp1,886,373. This study highlights the effectiveness of Monte Carlo VaR in capturing complex risk dynamics, demonstrates significant intra-sector diversification benefits challenging conventional diversification wisdom, and provides insights into how different airline business models translate into distinctive risk-return profiles. These findings have important implications for investment decision-making and risk management in specialized industry contexts, particularly in emerging markets.
Cryptocurrency Risk Management through Decision Engineering: Evaluating XRPUSD and ADAUSD Portfolio Performance Litamahuputty, Jacomina Vonny; Amiruddin, Erwin Gatot; Rahim, Robbi; Rahman, Abdul; Mamadiyarov, Zokir
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3871

Abstract

This research examines the risk profiles of XRPUSD and ADAUSD cryptocurrencies through Value at Risk (VaR) analysis with Monte Carlo simulation, providing quantitative risk assessments for both individual assets and a diversified portfolio. Analyzing historical price data from January 2016 to November 2024, the study identifies distinctive risk characteristics between these cryptocurrencies: ADAUSD exhibited marginally higher historical returns (1.44% monthly) compared to XRPUSD (1.42%), but with notably higher volatility (standard deviation of 5.41% versus 4.65%). The Monte Carlo simulation with 1,000 iterations generated VaR estimates at multiple confidence levels, revealing that XRPUSD consistently demonstrated lower downside risk than ADAUSD across all confidence thresholds. At the 99% confidence level, ADAUSD showed a Mean VaR of -10.97%, indicating potential monthly losses exceeding $10.97 million on a hypothetical $100 million investment, while XRPUSD's lower Mean VaR of -9.52% translated to potential losses of approximately $9.52 million. The most striking finding emerged from the portfolio analysis, which revealed dramatic risk reduction through diversification—the equally-weighted portfolio achieved a Mean VaR of merely -2.22% at the 99% confidence level, representing an approximately 80% reduction in potential losses compared to ADAUSD alone. These results demonstrate that cryptocurrency diversification can substantially mitigate extreme downside risk while maintaining exposure to the digital asset class. The significant risk reduction achieved through a simple two-asset allocation validates the application of modern portfolio theory principles to cryptocurrency investments despite their unique characteristics and underscores the critical importance of diversified approaches rather than concentrated positions for risk-conscious cryptocurrency investors. This research contributes to both theoretical understanding of cryptocurrency risk dynamics and practical portfolio construction approaches, providing quantitative evidence for the value of diversification strategies in navigating the substantial volatility inherent in digital asset markets.
Implementation of Machine Learning Algorithm with Extreme Gradient Boosting (XGBoost) Method In Hypertension Level Classification Rais, Zulkifli; Fahmuddin S, Muhammad; Saida, Saida; Triutomo, Agung
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4191

Abstract

The increasing number of hypertension patients and the threat of serious complications make hypertension one of the leading causes of death worldwide. Early prevention is currently considered one of the best solutions. Early prevention through early detection can be achieved by utilizing machine learning technology. XGBoost is a machine learning algorithm based on gradient boosting machines. XGBoost applies regularization techniques to reduce overfitting and has faster execution speed as well as better performance. The objective of this research is to classify hypertension levels using the XGBoost method and leveraging hyperparameter tuning for optimization. In this study, the hyperparameter optimization technique used is gridsearchCV. The evaluation results of the XGBoost classification method using the best combination of parameters show good performance, where the XGBoost model achieves an accuracy of 93.3%, Precision of 97%, Recall of 92%, F1-Score of 93%, and AUC value of 0.935. This implies that the classification of hypertension levels in patients at Pelamonia Makassar Hospital can be well or accurately classified using the XGBoost method.
Programming Evolution through Computational Thinking Using The Bibliometrics Analysis Mohd Rosli, Nurbaya; Mohd Matore, Mohd Effendi Ewan; Husnin, Hazrati
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 2 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3863

Abstract

The increasing significance of programming in various fields has made understanding its evolution a crucial academic pursuit. Despite the growing importance of programming, there is a lack of comprehensive analysis that integrates programming evolution with the nuances of computational thinking. This study explores an in-depth examination of the developmental trajectory of programming, contextualized within the broader framework of computational thinking. The aim of the paper is to decode the patterns, trends, and shifts in programming paradigms, tools, and education, contributing to the academic discourse on the subject. Using bibliometric analysis, the study examines a broad array of academic publications and data from the past decade. Advanced data mining in Scopus database and VOSviewer 1.6.20 are employed to trace the progression of programming concepts and their educational implications. Findings indicate a major transition from traditional paradigms to more inclusive, intuitive approaches that emphasize real-world problem-solving and interdisciplinary applications. The analysis reveals a significant shift from traditional programming paradigms towards more inclusive and intuitive approaches, emphasizing real-world problem-solving and interdisciplinary applications. While, educational trends show a gradual integration of computational thinking into curricula, reflecting the need to equip learners with relevant programming skills.
Artificial Intelligence Readiness in Malaysian Libraries: An Assessment Framework Masrek, Mohamad Noorman; Mohamad Rosman, Mohamad Rahimi; Mohamed Shuhidan, Shamila; Baharuddin, Mohammad Fazli; Mutia, Fitri; Soesantari, Tri; Yuwinanto, Helmy Prasetyo; Atmi, Ragil Tri
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 2 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3875

Abstract

This study investigates the readiness of Malaysian libraries to embrace Artificial Intelligence (AI) technologies, focusing on six key dimensions: infrastructure, technical skills, management support, policy and regulations, funding, and training. Utilizing a quantitative research approach, an online survey was conducted to collect data from library heads, providing insights into their preparedness for AI adoption. The survey instrument, developed based on preliminary interviews with librarians and refined through expert feedback and pilot testing, achieved good internal consistency. Findings reveal that while libraries exhibit moderate readiness in terms of internet infrastructure and management support, significant challenges remain in financial planning and policy and regulation formulation for AI integration. These challenges highlight critical areas that need addressing for successful AI adoption. The study provides valuable insights for library administrators and policymakers, emphasizing the need for comprehensive strategies to enhance AI readiness. The contributions of this study are threefold. Theoretically, it expands the understanding of AI readiness in the library sector, offering a validated framework for future research. Empirically, it fills a gap by providing data-driven insights into the current state of AI readiness among Malaysian libraries. Practically, it offers actionable recommendations to improve financial planning and policy development, essential for effective AI integration.
The Rasch Model Analysis of Secondary School Teachers' Acceptance of STEM Implementation in Teaching and Learning in Labuan Bin Amatan, Mohammad Azri; Han, Crispina Gregory K.; Pang, Vincent
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 2 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3884

Abstract

This study aims to assess the acceptance of the implementation of STEM approaches in teaching and learning by secondary school teachers in the Federal Territory of Labuan. This study uses a quantitative assessment approach of a sample survey type. Data were collected through the STEM Implementation Acceptance Questionnaire involving 314 teachers who were randomly selected from nine secondary schools. Rasch model data analysis using WINSTEPS Version 3.69.1 software was applied to produce a Wright Map to measure the acceptance aspects of STEM implementation through the study questionnaire items. The results of the study showed that items that were difficult to accept included making quantitative predictions (measure > 1 logit), reducing exam stress, and developing a problem-solving model, while items that were easy to accept were fostering positive values, early planning, and various teaching and learning methods (measure < -1 logit). The component of teacher belief in STEM recorded the highest mean measure value (0.43 logit), followed by attitude (-0.30 logit) and teacher commitment (-0.11 logit). Teachers were more likely to accept the aspects of attitude and commitment than belief in the importance of STEM. The findings of this study provide an overview of the strengths and weaknesses of teachers' acceptance of STEM implementation in teaching and learning, which should be taken into account to increase teachers' confidence, attitude, and commitment towards the implementation of STEM in secondary schools.

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