Authors Statistics from 8 Countries
| Country | Count of Articles |
|---|---|
| Indonesia | 9 |
| Malaysia | 3 |
| Spain | 1 |
| Japan | 1 |
| Uzbekistan | 2 |
| Sudan | 1 |
| Saudi Arabia | 4 |
| Egypt | 1 |
Articles
Sustainable Competitive Advantage in Indonesia’s Bioethanol Industry: Key Variables and Ecosystem Model
Abstract
The target for bioethanol fuel-grade blending in Indonesia has yet to be achieved, indicating significant barriers to the development of a sustainable industry. This study aims to identify key variables and develop an ecosystem model that supports Sustainable Competitive Advantage (SCA) in the Fuel-Grade Bioethanol Industry (FGBI) in Indonesia. Adopting the Resource-Based View (RBV) and Institutional Theory (IT) approaches, this study analyzes the interaction between internal and external factors influencing industry sustainability. Utilizing an exploratory inductive qualitative method covering the period 2006 to 2024, the research identifies three key RBV variables: (1) demand, (2) raw materials, and (3) technology as well as three coercive IT variables: (1) price formulation, (2) trade structure, and (3) tariffs and incentives as critical elements in achieving SCA. The findings indicate that coercive IT alone is ineffective in achieving SCA without integration with RBV. As both an academic and practical contribution, this study proposes a sustainable ecosystem model for FGBI in Indonesia. Further quantitative testing is needed to examine inter-element dynamics and evaluate the model’s long-term effectiveness. The implications for stakeholders highlight the importance of integrating policy with RBV principles to ensure the sustainability of the industry.
Read full articleA Bibliometric Analysis of Complex Problem-Solving Approaches in Engineering Education
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
Read full articleTime Series Innovation: Leveraging BetaSutte Models to Enhance Indonesia's Export Price Forecasting
Abstract
This study introduces a novel application of the Modified Trend-Augmented ?-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.
Read full articlePaper vs. Digital Assessments: Evaluating Critical Thinking on Ecological Issues in Indonesian Madrasahs
Abstract
This research aims to determine the difference in the assessment results in the critical thinking skills of State Madrasah Ibtidaiyah (MIN) students or State Islamic schools in Indonesia using paper-based and digital-based tests. Data was collected from 314 participants representing MIN on four large islands in Indonesia, namely MIN Bogor Regency, West Java, MIN Sinjai Regency, South Sulawesi, MIN Palembang City, South Sumatra, and MIN Balikpapan City, East Kalimantan. The research method used a quantitative survey. A total of 24 valid Critical Thinking Skills Items with the Facione Critical Thinking Skills indicator as an instrument for measuring critical thinking skills. Factors such as motivation, technological accessibility, and effectiveness of test formats contribute to differences in results. This study provides new insights into the effectiveness of technology-based assessment in Madrasa education, especially in evaluating critical thinking skills. These findings lead to policy recommendations for improving the Madrasa assessment system in the digital era.
Read full articleFrom 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
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
Read full articleCryptocurrency Risk Management through Decision Engineering: Evaluating XRPUSD and ADAUSD Portfolio Performance
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.
Read full articleThe Validity of the Malaysian Teachers’ Global Competency Level Instrument Using Cohen Kappa, Content Validity Ratio and Content Validity Index Analyses
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.
Read full articleApplication of Digital Image Processing for Orchid Image Segmentation in Morphological Plant Analysis
Abstract
The deployment of digital image processing in orchid image segmentation for plant morphological analysis is investigated in this study. The goal of this study is to increase the accuracy of orchid species identification using color-based segmentation approaches using 90 photos of three different orchid species—Cattleya, Dendrobium, and Onchidium—that were retrieved from Kaggle. Pre-processing is the first step in the process, which involves shrinking the size of the photos, separating them into RGB components, and converting them to HSV color space for additional analysis. Segmentation is done using the K-Means technique, which clusters pixels according to the color features that have been retrieved. Centroid updates are made until convergence is reached. With an identification accuracy of 92%, the binary and RGB segmentation results show how well this method works to distinguish the flower item from the backdrop. By advancing image processing methods in botany, this study aids in the identification of rare orchid species and conservation initiatives.
Read full articleTowards Integrated E-Government Services: Key Contributing Factors from a Whole-of-Government Perspective
Abstract
Despite various initiatives introduced to ensure electronic government (e-government) services remain relevant, there are still unresolved issues in its implementation that bring negative perceptions towards the government. One common issue is the existence of duplicated as well as unintegrated services between agencies that mislead citizens and cause bureaucratic red tape. This issue could be resolved by establishing integrated e-government services, which nevertheless require strong collaboration from multiple agencies. One strategy to strengthen cross-agency collaboration is through the Whole-of-Government (WoG) approach. However, studies concerning the WoG approach that specifically focus on collaboration strategies to address service integration issues in e-government are scarce. This study therefore aimed to identify the WoG factors that are important for strengthening cross-agency collaboration, as the effort for establishing integrated e-government services. It adopted the qualitative method approach, consisting of theoretical and empirical studies. The theoretical study was conducted through literature reviews, whereas the empirical study involved semi-structured interviews with relevant informants from public and private sectors as well as statutory body. Both theoretical and empirical data were analysed using thematic analysis. The identified WoG factors were categorised into people, process and technology aspects, which can act as a guideline towards integrated e-government services through cross-agency collaboration.
Read full articleA Decision-Centric Approach to Risk Management in Aviation Stock Investments Using Value at Risk and Portfolio Optimization
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.
Read full articleImplementation of Machine Learning Algorithm with Extreme Gradient Boosting (XGBoost) Method In Hypertension Level Classification
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.
Read full articleARDL-Based Investigation of the Relationship between Monetary Policy and Inflation Mitigation
Abstract
This study aims to estimate the efficiency of monetary policy in reducing inflation rates in Sudan for the years 2000 - 2022. The data of the study were collected from the Central Bank of Sudan and the Central Bureau of Statistics. To estimate the relationship between the variables, the study used statistical methods and econometric tools, including co integration error correction, and the Augmented Dickey-Fuller test. According to the results, there is a cointegration connection between the variables, and the variables are first order integrated. It was concluded that the estimated model is significant, and therefore it can be used to forecast, and that inflation is inversely related to both the exchange rate and the cost of financing, while the inflation is directly related to both bank credit and the money supply. To control inflation and stabilize exchange rates, a contractionary monetary policy was recommended
Read full articleExploring Nano Simply Open Sets in Nano Topology: Applications and Insights
Abstract
In recent times, nano set theory has shown broad applications in addressing practical challenges in various domains, including engineering, social sciences, and healthcare sciences. The notion of nano topological spaces, along with associated concepts like nano near open sets, has greatly developed because of its practical usefulness. The idea of nano continuity extends the idea of continuity. Likewise, nano open sets extend the notion of open sets within topological spaces. This paper presents a new class of nano near open sets, termed "nano simply open sets" and "nano delta sets," which expands and alters the current concepts of nano open sets (and, in certain instances, nano near open sets). We introduce a novel category of nano simply open sets, explore their characteristics, and analyze their connections with other sets. Additionally, we investigate new ideas like "nano simply continuous functions” and also, we introduce new notions of continuity based on the new notions. We shall study some of their properties and we will study the relationship between the new concepts and various other nano open sets.
Read full articlePre-Trained Transformer-Based Approach for Arabic Question Answering: A Comparative Study
Abstract
Question answering (QA) is one of the most challenging yet widely investigated problems in Natural Language Processing (NLP). Question-answering (QA) systems try to produce answers for given questions. These answers can be generated from unstructured or structured text. Hence, QA is considered an important research area that can be used in evaluating text understanding systems. A large volume of QA studies was devoted to the English language, investigating the most advanced techniques and achieving state-of-the-art results. However, research efforts in the Arabic questionanswering progress at a considerably slower pace due to the scarcity of research efforts in Arabic QA and the lack of large benchmark datasets. Recently many pre-trained language models provided high performance in many Arabic NLP problems. In this work, we evaluate the state-of-the-art pre-trained transformers models for Arabic QA using four reading comprehension datasets which are Arabic-SQuAD), ARCD, AQAD, and TyDiQA-GoldP datasets. We fine-tuned and compared the performance of the AraBERTv2-base model, AraBERTv0.2-large model, and AraELECTRA model. In the last, we provide an analysis to understand and interpret the low-performance results obtained by some models.
Read full articleAnalysis of the Factors Affecting the Financial Performance of Insurance Companies using Statistical Modeling
Abstract
The insurance industry is fundamental to the global economy, accounting for about 7% of the gross domestic product (GDP) in numerous developed nations and serving a crucial function in risk management and financial stability. Recent years have seen escalating economic pressures that have adversely affected the profitability of insurance firms. These Difficulties encompass escalating inflation rates, a surge in claims, and losses attributable to natural disasters, with swings in interest rates that have impacted investment returns and the valuation of financial portfolios. This study aims to examine the determinants influencing the financial performance of insurance businesses through precise statistical models, with a particular emphasis on return on equity (ROE) as a principal metric. The research utilized real-time data encompassing characteristics such as insurance density, interest rates, underwriting capacity, and insurance expenditures, among others. Statistical modeling was employed to ensure the degree to which these factors influence profitability. The project seeks to establish an analytical framework to improve the efficiency of underwriting and pricing decisions. It further advances academic literature by utilizing sophisticated analytical tools to understand profitability dynamics inside the insurance sector.
Read full article