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This study is motivated by the low ability of seventh-grade students to understand and write procedural texts, particularly in terms of linguistic features and the logical sequencing of steps. These conditions indicate the need for a learning model that actively engages students and enhances their analytical thinking skills. This research aims to examine the effect of the Problem Based Learning model on students’ ability to analyze and write procedural texts at SMP Negeri 1 Warungkondang. The study employed a quantitative approach using a quasi-experimental design with a pretest–posttest control group. The research population consisted of all seventh-grade students, with a sample of 32 students divided into an experimental class and a control class. Data were collected through comprehension tests, procedural text writing assessments, and classroom observations of student learning activities. Statistical analysis was conducted to identify differences and improvements in students’ abilities following the implementation of the learning model. The findings reveal that students’ understanding of procedural text structure was at a moderate level, with the highest achievement in identifying text goals and the lowest in mastering linguistic features. Students’ writing ability was predominantly categorized as moderate, indicating that while they understood the basic structure of procedural texts, they encountered difficulties in organizing steps logically and in detail. Furthermore, the use of audiovisual media within problem-based learning significantly increased student engagement and learning enthusiasm, although oral communication skills remained limited. These results suggest that Problem Based Learning is effective in improving procedural text learning outcomes; however, it should be supported by focused linguistic guidance and staged writing practice to optimize students’ overall writing performance.  

This study, titled “analysis of the influence of brand image and online promotion on generation z’s decision to purchase bsi product at bsi merr surabaya”, examines the effect of brand image (X1) and online promotion (X2) on the purchasing decisions (Y) of Generation Z consumers at the BSI Merr Surabaya branch. Using a quantitative research approach, data were collected from 100 respondents aged 20–27 years through purposive sampling. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the assistance of WarpPLS 7.0 software. The results indicate that both brand image (X1) and online promotion (X2) have a significant and positive influence on Generation Z’s decision to purchase BSI products. Among the two variables, online promotion (X2) shows a stronger impact, highlighting its importance in engaging digitally savvy consumers. These findings emphasize the need for companies to strengthen digital marketing strategies while maintaining a solid brand image to effectively influence purchasing behavior. The study underscores the strategic relevance of optimizing both factors to enhance consumer engagement and drive purchase decisions among Generation Z.

Dropshipping is essentially a buying and selling process between a seller and a buyer using the internet. Dropshipping is a product sale that allows dropshippers to sell goods to customers using photos from suppliers or stores and then sell them to customers at a price determined by the dropshipper. The questions to be answered through this research are: a) How does the buying and selling process work using a dropship system? b) How does the buying and selling process work using a dropship system from a sharia economic perspective. The method used in this research includes descriptive research with a qualitative approach. The data used are primary and secondary data obtained from verses of the Qur'an and the Hadith, books and journals, articles, and mass media, both print and electronic, related to the focus of the problem being studied in this research. The data analysis technique used in this research is the Miles and Huberman analysis model, using data reduction, data presentation, and drawing conclusions. The results of the study indicate that the Samsarah contract is a solution to dropship transactions. The original law is permissible or permissible if carried out in accordance with Islamic provisions.

This study aims to analyze the effect of product innovation, brand image, after-sales service, experiential marketing, and product quality on purchase intention and its impact on purchasing decisions of Toyota Avanza consumers in Semarang City. The research was conducted using a quantitative approach with primary data collected through questionnaires distributed to 120 respondents selected by purposive sampling. Data analysis employed the Structural Equation Modeling (SEM) technique using AMOS 18 software. The results indicate that all five independent variables—product innovation, brand image, after- sales service, experiential marketing, and product quality—have a positive and significant effect on purchase intention. Furthermore, purchase intention significantly affects purchasing decisions. Among the tested variables, product quality and product innovation were found to have the greatest influence on purchase intention, followed by brand image, experiential marketing, and after-sales service. These findings suggest that Toyota’s success in maintaining consumer interest and purchasing decisions largely depends on its ability to provide superior product quality, continuous innovation, and memorable customer experiences. The study contributes to marketing management literature by highlighting the integrated role of innovation and experiential marketing in shaping consumer behavior in the automotive sector.

This study aims to analyze the influence of leadership style, work environment, and workload on employee performance at PT BFI Finance Indonesia Tbk, Semarang Branch, using the Job Demands–Resources (JD-R) Theory as its underlying framework. The JD-R model posits that employee performance and well-being depend on the balance between job demands and job resources. The research employed a quantitative explanatory approach involving 56 employees from the Central Operation division, selected through purposive sampling. Data were collected using a Likert-scale questionnaire and analyzed using multiple linear regression with SPSS version 21. The results reveal that leadership style and work environment have positive and significant effects on employee performance, while workload has a significant negative effect. The coefficient of determination (R²) of 0.673 indicates that the three variables collectively explain 67.3% of employee performance variation. Respondents perceived that communication and support from leaders were strong, and social relations in the workplace were positive, although workload and performance targets were considered relatively high. These findings reinforce the JD-R Theory (Bakker & Demerouti, 2017), showing that leadership and work environment function as job resources that enhance motivation and performance, whereas excessive workload represents job demands that may hinder employee outcomes if not properly managed. Practically, organizations in the financial services sector should maintain a balance between job demands and job resources to achieve sustainable employee performance improvement.

The transformation of the retail industry in Indonesia is accelerating in line with increasing digital penetration and changes in consumer shopping behavior. This condition requires small-scale retail, including fresh marts, to be able to optimize effective marketing stimulus to maintain and increase consumer buying interest. This study aims to analyze the role of marketing stimulus consisting of price, product variety, and promotion as a predictor of consumer buying interest using the Stimulus Organism Response (SOR) theoretical approach. This study involved 105 consumer respondents of Rizky Fresh Mart Semarang, who were selected through accidental sampling techniques. Data were collected using a Likert scale questionnaire and analyzed through multiple linear regression. The results showed that product variations had a positive and significant effect on buying interest, while prices and promotions showed a positive but insignificant influence. Simultaneously, the three marketing stimuli have a significant effect on consumer buying interest. These findings confirm that in the context of small-scale retail in Indonesia, product variety is the most powerful stimulus in shaping consumer perception before generating a response in the form of buying interest, according to the SOR model. This research provides managerial implications for retailers to prioritize completeness and product diversity, in addition to more strategic price management and promotion.

This study aims to examine the effect of employee competence, behavioral control, and trust on employee performance at the Bojonegoro District Health Office with Organizational Citizenship Behavior (OCB) as an intervening variable. The background of this study is related to the importance of employee performance in achieving public service organizational goals, particularly in the health sector. This study uses an explanatory quantitative method with a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The research sample consisted of 100 employees at the Bojonegoro District Health Office. The results of the analysis show that the influence of employee competence on employee performance is not significant, but employee competence has a significant influence on OCB. Behavioral control has a significant effect on employee performance, but its effect on OCB is not significant. Trust has a significant effect on OCB, but its direct effect on employee performance does not. OCB is proven to have a significant effect on employee performance. Therefore, OCB plays an important role as a mediator in improving employee performance. This study provides a theoretical contribution by clarifying the relationship between variables in the context of public service-based government organizations. In practical terms, the results of this study are expected to form the basis for recommendations to improve the quality of human resources and develop managerial strategies at the Bojonegoro District Health Office in order to support the effectiveness and efficiency of public services.

This study aims to analyze the influence of Innovative Work Behaviour, Upskilling, and Work Ethic on the Health Workers Performance with Intention to Stay as an intervening variable at RSUD Sumberrejo. The background of this research stems from challenges in improving service quality, high workload, and the need to strengthen competency and retention among health workers. The study employs a quantitative approach using a survey method through the distribution of questionnaires, with data analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) through SmartPLS version 4.0. The population consists of 216 employees, and the sampling technique used is non-probability sampling, resulting in 140 respondents. The findings reveal that Innovative Work Behaviour, Work Ethic, and Intention to Stay have a significant positive effect on the performance of health workers. In addition, Innovative Work Behaviour and Work Ethic significantly influence Intention to Stay. However, Upskilling shows a positive but non-significant effect on both Intention to Stay and Performance, indicating that skill enhancement requires managerial support and motivation to contribute effectively to employee performance. Intention to Stay serves as a mediating variable in several relationships among the constructs. Strengthening innovative behaviour, work ethic, and competency development, accompanied by appropriate retention strategies, is essential for improving the performance of health workers in regional hospitals. Future studies are recommended to develop the research model by incorporating additional variables that may have stronger effects on Intention to Stay and Performance. Keywords: Innovative Work Behaviour; Upskilling, Work Ethic; Intention to Stay; Health Workers Performance

This study aims to analyze the effect of global diversification, exchange rates, and interest rates on the performance of mixed mutual funds in Indonesia during the period 2020–2024. The method used is a quantitative approach with the Partial Least Squares–Structural Equation Modeling (PLS-SEM) technique, using secondary data from the Financial Services Authority, Bank Indonesia, and Bareksa. The sample consists of three mixed mutual fund products that meet the criteria of portfolio data completeness, net asset value, and performance report publication. The results show that exchange rates have a positive and significant effect on mutual fund performance, indicating that exchange rate fluctuations play an important role in determining changes in portfolio returns. The global diversification variable proved to have no significant effect, illustrating that exposure to international markets has not provided stable benefits in improving the performance of mixed mutual funds. Interest rates also did not show a significant effect because the composition of mixed portfolios was able to withstand the impact of monetary policy changes. Simultaneously, the three independent variables were able to explain 66.7 percent of the variation in mixed mutual fund performance, indicating that macroeconomic dynamics and portfolio strategies have an important contribution in influencing the performance of this collective investment instrument.

The performance of the Indonesian National Team (Timnas) in the 2026 World Cup qualifications has triggered massive and diverse responses on social media, particularly on platform X. This study aims to identify and classify public sentiment regarding Timnas Indonesia's performance into positive, negative, and neutral categories using a data mining approach. Text data was processed through pre-processing stages, term weighting using TF-IDF, and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class distribution imbalance. The classification algorithm employed was Multinomial Naïve Bayes. Model performance evaluation was conducted by comparing two training-testing data split scenarios: 90:10 and 80:20 ratios. The results indicate that public opinion is dominated by negative sentiment at 73.2%, reflecting public disappointment. In terms of model performance, the 90:10 ratio scenario yielded the best accuracy of 80%, outperforming the 80:20 ratio which recorded an accuracy of 75%. These findings demonstrate that combining Multinomial Naïve Bayes with the SMOTE technique is effective in handling imbalanced text data and is capable of accurately mapping public perception.

This study aims to evaluate the effectiveness of the Artificial Intelligence-Based Learning Media Development Program (P3MP-AI) in enhancing teachers’ technological and pedagogical competencies at SMK Muhammadiyah 2 Malang. The program employed a descriptive approach using both quantitative and qualitative methods, including pre-test and post-test assessments, as well as direct observation of the training process. A total of 30 teachers from various disciplines actively participated in the program conducted on August 12, 2025. The evaluation results revealed an increase in the participants’ average scores from 100 to 130 out of a maximum of 150, indicating a significant improvement in their understanding of AI concepts and applications in education. Beyond competency enhancement, the training also fostered teachers’ confidence, creativity, and ability to integrate AI-based tools into interactive learning media. However, several challenges were identified, such as limited technological resources and time constraints in classroom implementation. Overall, this program has made a tangible contribution to strengthening teachers’ digital literacy and can serve as a replicable professional development model for other vocational schools seeking to advance AI-based educational transformation.

Credit scoring is an important element in decision-making for providing financing, especially for microfinance institutions. Several methods for predicting credit scoring include Decession Tree, Gradient Boosted, Neural Network, K-NN, and Rule Induction. This study aims to improve the accuracy of financing risk prediction by efficiently integrating historical data. The Neural Network (NN) algorithm is a machine learning algorithm consisting of neurons (nodes) connected to each other in several layers (input, hidden, and output). NN is used for pattern recognition, classification, regression, and complex non-linear modeling. The NN algorithm has the advantage of working well on large and diverse data and unstructured data. However, the NN algorithm has weaknesses such as overfitting and data dependence. In this study, the integration of the Sample Bootstrapping and Weighted Principal Component Analysis (PCA) methods is proposed to improve optimal accuracy in the NN algorithm. The Sample Bootstrapping method is used to reduce the amount of training data to be processed. The Weighted PCA method is used to reduce attributes. This study uses a financing customer dataset. The results of the study show that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA resulted in an accuracy increase of 1-3% (97%-99%) compared to other algorithms. Therefore, it can be concluded that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA produces better accuracy than other algorithms

Credit card fraud is a critical issue in digital financial transactions. This study aims to develop and evaluate fraud detection models using Logistic Regression and Gradient Boosting on an imbalanced dataset, where fraudulent transactions constitute only a small portion of the data. To address this imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. Logistic Regression, used as a baseline model, achieved 95% accuracy, 78.6% precision, 55.9% recall, and a 65.3% F1-score. After applying class weighting and SMOTE, recall improved to 88.7%, but precision dropped to 52%, indicating that the model became overly sensitive and prone to false positives. Gradient Boosting initially produced better results, with 98% accuracy, 95.5% precision, 84.3% recall, and an 89.5% F1-score. After hyperparameter tuning and resampling, its performance improved further to 96.7% precision, 86.1% recall, and a 91.1% F1-score. These results indicate that Gradient Boosting is more effective in handling imbalanced data and offers greater reliability in detecting fraudulent transactions. The findings support the growing evidence in favor of ensemble learning techniques in fraud detection applications. This research contributes practical insights into improving the accuracy and security of machine learning-based fraud detection systems in financial services.

This quasi-experimental study examines the effectiveness of Artificial Intelligence (AI)-assisted learning in enhancing critical thinking skills among undergraduate history students. The study involved 60 students divided into experimental and control groups. The experimental group received AI-supported instruction integrating adaptive learning modules, scaffolded source-analysis prompts, and guided argumentative discussions facilitated by conversational AI tools, while the control group followed conventional lecture-based instruction. Data were collected using a validated critical thinking test, classroom observation protocols, and semi-structured interviews. Quantitative data were analyzed using paired and independent sample t-tests, while qualitative data were examined through Miles and Huberman’s interactive analysis model. Results indicate statistically significant improvements in critical thinking scores in the experimental group compared to the control group. Thematic findings reveal enhanced sourcing, contextualization, corroboration, and evidence-based argumentation skills. However, minor risks of over-reliance on AI highlight the need for instructional scaffolding and ethical guidance. The findings suggest that AI can function as a cognitive scaffold that strengthens historical thinking and metacognitive awareness when implemented within a structured pedagogical framework.

In this paper, we study the use of linear programming for the performance enhancement of fortified queues systems in which resources are unreliable in terms of variation and a case study on Marjan Hospital is considered. The quality management maturity level in the hospital was measured by an integrated inspection tool; senior management was found to be concerned for quality of service improvement and implementation of modern technology systems. Strengths and weaknesses in that activity are shown, and it is evidenced the need of boosting the quality of service activities and promoting the coordination between the different areas. From the findings, the study recommends the use of linear programming technique,Orgers should as well enhance proper training and development and Acquisition of modern day technology for effective performance improvement and quality of service. The study demonstrates that the adoption of linear programming techniques could be a viable solution to optimize resource allocation and enhance performance in the hospital's queue systems. Additionally, the research underscores the importance of investing in staff training and development programs, as well as the acquisition of state-of-the-art technology, to further improve service delivery and ensure sustainable quality improvement in the long term.

This study applies the Mean Variance model, which aims to form an optimal portfolio composition in the health, property, and cyclical consumer sectors and combine the three sectors into one portfolio, then visualize its efficient frontier. This study analyzes the return profiles and compares the risks of each portfolio using alternative risk measures such as the Coefficient of Variation (CV), Value at Risk (VaR), and Conditional Value at Risk (CVaR). Daily closing price data for the three sectors listed on the Indonesia Stock Exchange (IDX) from March 2, 2020, to March 3, 2025, were used in this study. Stock selection was conducted using purposive sampling, followed by selecting seven stocks for optimization based on the lowest Coefficient of Variation (CV) value. Portfolio optimization analysis was conducted using the Python programming language with Visual Studio Code software. The findings of this study indicate that the combined portfolio incorporating the three sectors is the most efficient, with an expected return of 0.104%, standard deviation of 0.007, and alternative risk measures such as Coefficient of Variation (CV) 6.9328, Value at Risk (VaR) of -0.99%, and Conditional Value at Risk (CVaR) of -1.44%, which are lower than those of single-sector portfolios. Visualization of the efficient frontier curve confirms that the combined portfolio offers better results in terms of risk and return. The results of this study indicate that cross-sector diversification can significantly reduce risk and prevent significant losses.

The digital age of the 21st century has brought significant transformations to the spiritual life of Christians. Technology expands access to the word, worship, and faith community, but at the same time presents challenges in the form of distractions, individualism, and the shallowness of spiritual reflection. This research highlights the main problem of how Christian spirituality can be reinterpreted in the midst of digitalization, especially by placing hope as the core that sustains faith. The goal is to formulate a pedagogical model that is able to integrate digital technology with faith education so that it is relevant for the digital generation.The method used is a qualitative approach through library research with thematic analysis of contemporary theological and pedagogical literature. The study focuses on Runggeari & Deak's thoughts on the digital church and Boiliu's on faith education in the family, as well as the theories of hope from Moltmann, McGrath, and Hebblethwaite. The results of the study show that digital spirituality can thrive when it is rooted in hope, which serves to foster steadfastness of faith, shape the ethics of using technology, and expand digital solidarity. Christian Hope encourages churches, educators, and families to not only produce spiritual content, but to present transformative communities of love. The synthesis of the main idea emphasizes the importance of a pedagogy of faith that redeems technology: using it not just as a medium of information, but as a participatory space to form character, faith, and togetherness.

Background: In the domain of corporate governance, the separation of ownership and control generates significant agency conflicts, primarily manifesting as Earnings Management (EM). Traditional reactive auditing methods fail to detect manipulation concealed within unstructured data, leading to high agency costs and diminished stakeholder trust. Objective: This study proposes an "AI Proactive Monitoring Model" utilizing Generative Artificial Intelligence to fundamentally enhance the monitoring mechanisms of Agency Theory. Methods: The research employs a qualitative conceptual framework analysis. It synthesizes Agency Theory with the Technology Acceptance Model (TAM) and Systemic Risk Theory to construct a novel strategic governance model. Results: The proposed model shifts governance from periodic sampling to real-time, continuous analysis of total data populations. By cross-referencing structured financial data with unstructured communications (e.g., emails, contracts), the system generates "Risk Narratives" that contextualize anomalies and flag opportunistic behavior immediately. Conclusion: The integration of AI significantly reduces information asymmetry and moral hazard by creating a "panopticon" effect. However, successful implementation requires distinct regulatory frameworks to manage the systemic risks associated with algorithmic reliance.

This study aims to determine how the implementation of the free meal program improves the welfare of the elderly in Paokmotong Village, Masbagik District. The research method used is quantitative descriptive with a survey method. The sample consisted of 92 elderly beneficiaries of the program. Data were collected through a questionnaire and analyzed descriptively and inferentially using a one-sample t-test. The results showed that the average total welfare of the elderly was 64.58 out of a maximum score of 80, which is included in the good to very good category. The results of the t-test produced a value of t = 75.749 p-value 0.000 <0.05, which means there is a significant difference between the welfare score of the elderly and the minimum expected value. This indicates that the implementation of the free meal program has a positive impact on the quality of life of the elderly. The implementation of the free meal program has been proven to have a significant positive impact on improving the quality of life of the elderly, especially in aspects of physical and mental health and reducing their economic difficulties. This program can be a model for improving the welfare of the elderly in other areas.

Penelitian ini dilatarbelakangi oleh krisis ekologis yang semakin nyata, khususnya persoalan sampah, yang tidak hanya bersifat teknis tetapi mencerminkan krisis spiritual dan moral manusia terhadap ciptaan. Gereja, sebagai komunitas iman, dipanggil untuk merespons krisis ini secara teologis dan praksis. Tujuan penelitian ini adalah mengkaji bagaimana paradigma ekoteologi regeneratif dapat dioperasionalkan dalam kehidupan gereja sebagai bentuk kesaksian iman yang konkret melalui pengelolaan sampah. Penelitian ini menggunakan metode kualitatif-deskriptif dengan pendekatan teologis-kontekstual, melalui studi literatur, observasi lapangan, dan wawancara mendalam dengan pelayan gereja serta umat yang terlibat dalam praksis ekologis. Novelty penelitian ini terletak pada pengembangan ekoteologi regeneratif bukan hanya sebagai kerangka normatif-teoretis, tetapi sebagai praksis iman gerejawi yang mengintegrasikan dimensi liturgis, edukatif, dan sosial-transformasional dalam pengelolaan sampah. Hasil penelitian menunjukkan bahwa pengelolaan sampah berbasis gereja dapat menjadi locus teologis yang menumbuhkan metanoia ekologis dan memperkuat relasi antara iman, etika, dan tanggung jawab ekologis. Paradigma ekoteologi regeneratif menuntun gereja menjadi agen transformasi ekologis yang berkelanjutan dan kontekstual dalam karya pemulihan ciptaan.