Publication Search

The development of artificial intelligence (AI) in learning media presents challenges related to the uneven readiness of human resources and infrastructure, thus affecting the effectiveness of its implementation. This study aims to examine the effect of AI implementation in learning media development on learning outcomes of students of Faculty of Economics, University of Balikpapan. The method used is quantitative with survey design and Structural Equation Modeling (SEM) analysis using AMOS. The sample consists of 113 students selected by simple random sampling from the population of 376 active students. The results of the analysis showed that the readiness of lecturers and the quality of AI-based learning media had a significant effect on improving student learning outcomes. However, the success of AI implementation is also strongly influenced by infrastructure support and educator training. The findings provide important implications for learning media developers and policy makers to strengthen lecturers' capacity and improve technology infrastructure to support inclusive and sustainable digital transformation of education. In addition, ethical aspects and data privacy should be the main concerns in the development of AI-based learning media.

This study analyzes integrated modeling for renewable energy development in Gorontalo Province, Indonesia, focusing on enhancing sustainable energy systems. The projection for renewable energy usage is expected to reach 23% by 2025. This research identifies various challenges, including dependence on fossil fuels and energy shortages due to inadequate infrastructure. The study employs a quantitative descriptive approach and dynamic system modeling to evaluate the potential of renewable energy sources such as solar, wind, and biomass. The findings indicate that strategic interventions can significantly increase the penetration of renewable energy from 1-2% to 30-40% by 2040, while also reducing carbon emissions and improving energy security. The implications of this research highlight the importance of supportive policies and investments in renewable energy technologies to achieve a sustainable energy transition.

This study examines the influence of e-commerce platforms on the growth of MSMEs in the post-pandemic era, focusing on the implementation of entrepreneurial marketing strategies in the digital ecosystem. This research aims to understand how e-commerce platforms help MSMEs in developing more adaptive and effective marketing strategies amid changes in consumer behavior post-pandemic. The method used is a quantitative approach with Structural Equation Modeling (SEM) to analyze the direct and indirect relationship between the use of e-commerce platforms, entrepreneurial marketing strategies, and the growth of MSMEs. The main findings show that the use of e-commerce platforms has a significant effect on entrepreneurial marketing strategies, which in turn encourages the growth of MSMEs. This research contributes to the literature on MSME digitalization, particularly in the context of post-pandemic recovery, and shows how e-commerce platforms can be an important enabler for MSMEs to survive and thrive amid changing market challenges. The urgency of this research lies in its contribution in providing insights into the implementation of innovative digital marketing strategies, which is highly relevant for MSMEs trying to adapt to the post-pandemic business landscape. Keywords: ,

Micro, Small and Medium Enterprises (MSMEs) are the backbone of the Indonesian economy, but only 15.7% have successfully penetrated the global market due to limited human resources (HR) and cross-sector collaboration (BPS, 2023). This study aims to analyse the role of collaborative HR initiatives in opening global market access for MSMEs in East Java, using a quantitative approach with AMOS-based Structural Equation Modeling (SEM) on a sample of 150 MSMEs. The results of the analysis show that HR collaboration across sectors (government, private sector, academia) has a significant effect on improving global market access , while self-training is not significant. The findings strengthen the Resource-Based View theory by emphasising collaborative HR as a strategic asset of MSMEs. Practical implications include integrated policy recommendations and partnerships with global platforms (e.g., Amazon, Alibaba), as well as ethical considerations related to programme inclusivity and MSME data protection. This research makes a theoretical contribution to the development of an adaptive HR collaboration model for MSMEs in developing countries.

Nonlinear dynamical systems represent a fundamental area of study in applied mathematics due to their relevance across various disciplines, including physics, biology, and engineering. Their inherent complexity, characterized by phenomena such as bifurcation, chaos, and sensitivity to parameter variations, often limits the effectiveness of traditional manual analysis, particularly when addressing high-dimensional or computationally intensive models. This study aims to address these challenges by applying computational modeling and numerical simulation techniques to analyze the stability of nonlinear dynamical systems. The research employs analytical methods, including equilibrium point identification and linearization, which are then validated and extended through the fourth-order Runge-Kutta numerical method. Simulations were conducted to visualize equilibrium points, phase portraits, and parameter-driven bifurcation phenomena. The findings demonstrate a strong correspondence between analytical and numerical approaches, with minimal error margins (≤1%) observed in equilibrium point estimation, thus confirming the reliability of computational methods. Moreover, the bifurcation analysis revealed critical transitions such as pitchfork and Hopf bifurcations, which indicate sudden shifts from stability to instability behaviors that are difficult to capture through manual calculations alone. The integration of computational approaches provides clear advantages, offering systematic exploration of parameter spaces and detailed visualizations of system dynamics, thereby expanding the scope of stability analysis. In conclusion, this study emphasizes that computational modeling is not only an effective complement to analytical methods but also a necessary strategy for advancing the understanding of nonlinear dynamical systems in applied mathematics.  

Character development is an important part of the learning process in AUD. The study was conducted using qualitative descriptive techniques with sources from interview results, documentation, and observation . The purpose of the study focused on the development of children's character through classroom management strategies located in Pertiwi DWP Kindergarten, Wonosobo Regency. The learning process is not limited to the level of knowledge, but is also optimal in conveying character values in AUD. The good or bad character-based classroom management has an impact on the effectiveness of character development carried out by teachers. The results of the study showed that teachers carried out classroom management strategies by setting class rules , building positive relationships , modeling good behavior, giving appreciation to children , carrying out reflection activities. Based on the results of the implementation of classroom management, teachers are able to develop positive characters, namely discipline, responsibility, empathy, honesty and self-confidence.

This study aims to apply integral calculus methods to calculate the volume and arc length of an ice cream cone design shaped like a flower petal. The cone design is modeled using a quadratic function derived from three reference points on the petal curve. Using the solid of revolution method around the y-axis, the calculated petal volume is 150.8 cm³, and the arc length is 7.14 cm. The results demonstrate that calculus-based modeling supports efficient material usage while enhancing aesthetic and functional aspects of packaging. This research highlights the connection between mathematical concepts and practical product design in the food industry

The rapid development of artificial intelligence (AI) presents new challenges and opportunities in the practice of financial auditing, especially regarding the efficiency and accuracy of audit results, which are still the main problems. This study aims to examine the impact of financial audit transformation through the application of AI using a mixed methods approach. Quantitative data were collected from 100-150 internal and external auditors in medium to large companies in East Java Province who have been using AI for at least one year, and analysed using Structural Equation Modeling (SEM) with AMOS. Qualitative data was obtained through in-depth interviews to explore perceptions and challenges of AI implementation. The results showed that AI significantly improved the efficiency of the audit process and the accuracy of risk and fraud detection, despite barriers such as change resistance and limited auditor competence. This research makes important contributions to the development of modern audit theory and offers strategic recommendations for practitioners and regulators to optimise AI integration in financial auditing. Practical implications include the need for auditor training and strengthening technology infrastructure to support sustainable digital transformation. 

The development of digital technology has encouraged organisations to adopt big data analytics in human resource management (HRM), but there are still challenges in leveraging it for effective decision-making. This study aims to investigate the effect of big data utilisation on the quality of HR decision-making and identify the supporting and inhibiting factors for its implementation. A quantitative method with purposive sampling survey was conducted on 100-150 respondents from companies that have implemented big data-based management information systems and HR analytics. Data were analysed using Structural Equation Modeling (SEM) with AMOS software. The results showed that big data analytics significantly improved the quality of HR decision-making, with HR digital competencies and organisational culture as important mediating factors. However, challenges related to cultural resistance and limited expertise were found to affect the effectiveness of implementation. The practical implications of this research emphasise the importance of HR digital capacity building and ethical data governance to support the transformation of HRM into an adaptive strategic function in the digital era. This research also contributes to the development of data-driven management theory with a holistic approach that integrates technical, human, and ethical aspects.

In today’s global economy, accurately predicting foreign exchange rates or estimating their trends correctly is crucial for informed investment decisions. Despite the success of standalone models like ARIMA and deep learning models like LSTM, challenges persist in capturing both linear and nonlinear dynamics in highly volatile exchange rate environments. Motivated by the limitations of these individual models and the need for more robust forecasting tools, this study proposes a hybrid ARIMA-LSTM model that integrates ARIMA’s strength in modeling linear trends with LSTM’s capability to capture nonlinear dependencies, using historical USD/NGN exchange rate data from the Central Bank of Nigeria (CBN) spanning 2001 to 2024. The research hypothesis posits that the hybrid ARIMA-LSTM model will significantly outperform standalone models in forecasting accuracy. By comparing these models against state-of-the-art approaches, the study highlights the advantages of hybridizing statistical and deep learning methods. The findings demonstrate that the hybrid model achieved the lowest Root Mean Squared Error (RMSE) of 2.216 and the highest R² of 0.998, indicating superior forecasting performance. This study fills a critical research gap by demonstrating the effectiveness of hybrid deep learning in financial time series forecasting, providing valuable insights for investors, policymakers, and financial analysts. Future research will extend this work by incorporating the latest dataset and evaluating model robustness during the recent surge in the Naira/Dollar exchange rate from 2023 to 2024.

This article discusses the Direct Instruction strategy, a teacher-centered learning approach that emphasizes systematic and structured delivery of material. The purpose of this study is to address the following research questions: (1) What is meant by direct instruction strategy? (2) What are the characteristics of the direct instruction strategy? (3) What are its strengths and weaknesses? and (4) How is the direct instruction strategy applied in Islamic Religious Education (PAI) subjects? This article uses a library research method by examining various relevant theoretical sources. The findings indicate that direct instruction is an effective approach for delivering factual and procedural knowledge, particularly when learning objectives focus on mastering basic skills and understanding concepts in stages. This strategy is characterized by clear teacher guidance, structured learning steps, and continuous assessment. Its advantages include time efficiency and ease of classroom control, while its drawbacks involve limited student participation and less development of critical thinking skills. In the context of PAI learning, this strategy can be effectively applied to memorization materials, basic conceptual understanding, and character formation through concrete examples and teacher modeling.      

In the digital era, the ability to utilize social networks and digital platforms to gain financial education and make financial decisions in accordance with sharia principles is increasingly essential. This study aims to analyze the influence of social capital and digital literacy on Islamic financial literacy among Generation Z. A quantitative approach was employed, with primary data collected from 209 respondents through purposive sampling, consisting of Generation Z individuals residing in Yogyakarta. Data analysis was conducted using path analysis with the Structural Equation Modeling approach based on Partial Least Squares (SEM-PLS), utilizing the SmartPLS 3.0 software. Construct reliability testing, discriminant validity, and hypothesis testing were carried out to evaluate the relationships between variables. The results indicate that social capital and digital literacy have a positive and significant effect on Islamic financial literacy. Digital literacy enhances individuals’ access to Islamic financial information through digital media, while social capital strengthens knowledge sharing and trust within communities. These findings highlight the importance of leveraging digital technology and reinforcing community-based approaches to improve Islamic financial literacy. This study contributes to the growing body of literature on the interaction between social capital, digital literacy, and Islamic financial literacy in the digital age and offers strategic insights for educational institutions and financial service providers to promote sharia-based financial literacy.

The beauty industry is experiencing significant growth due to digital transformation, particularly through the use of brand ambassadors and social media marketing. This study investigates the impact of brand ambassadors and social media marketing on purchase intention, with brand image as the mediating variable. This research focuses on Azarine, the skincare from Indonesia and targets potential consumers in Riau Islands aged 18-44 who follow the Instagram account @azarinecosmeticofficial and recognize Prilly Latuconsina, Syifa Hadju, Angga Yunanda, and Lee Min Ho as brand ambassadors. A quantitative approach was applied using Structural Equation Modeling (SEM) with SmartPLS software. The research shows that brand ambassadors and social media marketing positively and significantly influence purchase intentions, with a higher direct impact compared to an indirect one through brand image. This study highlights the effectiveness of digital brand strategies in shaping consumer decisions and offers insights for marketing professionals in the beauty sector. These results emphasize the strategic role of celebrity endorsements and content-based engagement in influencing consumer perceptions and purchasing behavior.

Rainfall potential analysis plays a critical role in the management of air resources, mitigation of hydrometeorological disasters, and agricultural activity planning. Accurate estimation of rainfall patterns is essential to ensure effective decision-making in irrigation systems, water resource management, and disaster risk reduction strategies. This study aims to model the probability of rainfall occurrence using a statistical approach based on historical data obtained from the Bureau of Meteorology. The data spans a multi-year period and captures seasonal and regional variability in rainfall events. To characterize rainfall patterns, various probability distributions are tested, including the exponential distribution and the Weibull distribution, which are commonly applied in hydrological studies. Furthermore, the Markov chain method is employed to assess the likelihood of rainfall occurrence on a given day based on the conditions of the preceding day, thereby capturing temporal dependencies. Parameter estimation is conducted using Maximum Likelihood Estimation (MLE), a robust statistical method that enhances the precision of the model. The suitability of each probability distribution in representing the observed rainfall data is evaluated through goodness-of-fit tests such as the Kolmogorov-Smirnov test. The findings reveal that certain distributions align more closely with the local rainfall characteristics, demonstrating the importance of regional analysis in climate modeling. The combination of probabilistic modeling, Markov analysis, and rigorous statistical testing provides a reliable framework for forecasting rainfall. These results are expected to serve as a scientific basis for stakeholders in agriculture, environmental planning, and disaster preparedness, offering insights that support sustainable water resource utilization and risk management.

Land cover change has a significant impact on the hydrology of watershed areas, including increasing flood risk. This study aims to analyze land cover changes in the Siborgonyi and Acai sub-watersheds between 2013 and 2022 and their impact on flood potential. The methods used include spatial analysis with GIS, flood modeling using HEC-RAS, and Curve Number (CN) calculations to identify changes in soil infiltration capacity. The results show that land cover changes have a substantial effect on the increase in flood risk. The reduction of forest areas and the expansion of built-up land indicate a large-scale conversion of natural vegetation into residential and infrastructure areas. CN values in 2022 increased across most areas, indicating reduced soil infiltration capacity and increased surface runoff. Flood modeling shows that both the extent and depth of inundation significantly increased in 2022, particularly in downstream areas with basin-like topography.

Research aims: This study aims to examine the impact of Competency-Based Human Resource Management (CBHRM) on the performance of millennial employees in the Internet Service Provider (ISP) sector in Batam, Indonesia. Additionally, the research investigates the role of personal values as a moderating variable in the relationship between CBHRM and employee performance. Design/Methodology/Approach: A quantitative research method was used, collecting data from 86 millennial employees through structured questionnaires. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). Research findings: CBHRM significantly improves millennial employee performance and influences personal values. However, personal values do not directly affect performance but strengthen the impact of CBHRM. This highlights the importance of integrating CBHRM with value-based HR policies to optimize workforce productivity. Theoretical Contribution/Originality: This study provides empirical evidence of CBHRM’s effectiveness and expands knowledge on how personal values interact with HRM practices to enhance employee performance. Practitioners/Policy Implications: Organizations should integrate CBHRM with value-driven leadership and training programs to maximize employee engagement. Policymakers should promote CBHRM adoption to address workforce skill gaps in technology-driven industries. Research Limitations/Implications: This study is limited to a single ISP company in Indonesia, which may restrict the generalizability of the findings. Future research should expand the sample size and explore other industries to validate the results. Additionally, further studies should investigate the influence of other moderating variables such as leadership style and organizational culture on the CBHRM-performance relationship

This study explores the application of the normal distribution in analyzing the height data of Mathematics Education students at FMIPA Universitas Negeri Medan in 2024. Employing a quantitative descriptive-analytic methodology, the research involved collecting primary data from 10 randomly selected students through a questionnaire-based survey. Descriptive statistical analysis revealed a mean height of 161.4 cm with a standard deviation of 8.79 cm. The median height was found to be 164 cm, while the mode was 150 cm, indicating a slightly skewed distribution. To assess the suitability of the normal distribution model, the Shapiro-Wilk test was applied, resulting in a W value of 0.921 and a p-value of 0.361, which exceeds the 0.05 significance level. This confirms that the sample data follow a normal distribution pattern. The findings were further supported through visual representation using histograms and analysis based on the empirical rule, which showed that approximately 68% of the students' heights fall within one standard deviation of the mean (152.81–169.99 cm). Additionally, probability calculations demonstrated that the likelihood of a student being 160 cm tall or shorter is approximately 43.64%. These results validate the effectiveness of the normal distribution as a tool for analyzing biological or physical characteristics, even in small sample sizes. However, the study acknowledges its limitation in terms of sample size and suggests that future research involve larger and more diverse populations to enhance generalizability. The study highlights the relevance of normal distribution in statistical modeling, particularly for educational and health-related data interpretation and decision-making processes.

Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset with preprocessing steps, including emoji handling, slang normalization, and lemmatization. It achieves a peak training accuracy of 99.78% at epoch 138 with early stopping. The model delivers a strong performance on the Amazon test set across four key aspects: price, quality, service, and delivery, with F1 scores ranging from 0.90 to 0.92. The model was also evaluated on the SemEval 2014 ABSA dataset to assess generalizability. Results on the restaurant domain achieved an F1-score of 88.78% and 83.66% on the laptop domain, outperforming several state-of-the-art baselines. These findings confirm the effectiveness of the BiGRU-BiDAF architecture in modeling aspect-specific sentiment across diverse domains.

Many students find it difficult to connect abstract mathematical concepts with real-world applications, leading to limited problem-solving and modeling skills. This study aimed to develop a real-world problem-based mathematics module designed to enhance students’ mathematical modeling abilities. Using a Research and Development (R&D) approach with the 4D model Define, Design, Develop, and Disseminate the research involved secondary school students enrolled in mathematics classes. The Define stage identified learning difficulties and needs, while the Design and Develop stages focused on creating, validating, and refining a contextual mathematics module based on expert feedback. Quantitative data were collected through expert validation sheets, mathematical modeling tests, and student response questionnaires. The results demonstrated that the developed module achieved a high level of content and design validity, confirming its appropriateness for classroom use. Student responses indicated strong practicality and engagement during implementation. The effectiveness analysis showed a 28% improvement in mathematical modeling skills after the intervention, as evidenced by increased post-test scores and positive student feedback. Compared to traditional textbook-based instruction, the module proved more effective in fostering conceptual understanding, analytical reasoning, and real-world application of mathematics. In conclusion, the developed real-world problem-based module successfully bridges the gap between theoretical mathematics and authentic contexts, providing an innovative framework to cultivate modeling competence and critical thinking among students.

This study examines the relationship between corporate financial disclosure and investment decisions by shareholders and investors in capital markets. Using a comprehensive dataset of 486 publicly listed companies from multiple stock exchanges over a five-year period (2018-2022), we investigate how the quality, scope, and timing of financial disclosures influence investment behaviors, pricing efficiency, and capital allocation. Through multiple regression analysis, structural equation modeling, and panel data techniques, we find that higher disclosure quality is significantly associated with increased trading volumes (β=0.42, p<0.01), lower bid-ask spreads (β=-0.38, p<0.01), and reduced stock price volatility (β=-0.31, p<0.01). Our analysis reveals that voluntary disclosures beyond regulatory requirements have a stronger impact on institutional investor decisions compared to retail investors. Additionally, the study documents that forward-looking financial information and segment reporting have particularly strong effects on investment decisions during periods of market uncertainty. The findings contribute to disclosure theory and provide empirical evidence for regulators considering disclosure policy reforms, corporate executives formulating communication strategies, and investors developing investment frameworks that incorporate disclosure quality assessment. The study addresses the causality challenge through instrumental variable estimation and difference-in-differences analysis of regulatory changes, enhancing the robustness of the identified relationships.