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This study aims to analyze the development of regional financial performance and its effect on economic growth in Jambi Province during the 2019–2023 period. Regional financial performance is measured using the Regional Financial Independence Ratio and the Fiscal Decentralization Degree (FDD), while economic growth is proxied by Gross Regional Domestic Product (GRDP). This research employs a quantitative approach using secondary data obtained from the Central Statistics Agency (BPS), the Directorate General of Fiscal Balance (DJPK), and regional government financial reports covering 11 regencies/cities in Jambi Province. The analytical method applied is panel data regression using the Random Effect Model (REM). The results indicate that both regional financial performance and economic growth in Jambi Province experienced fluctuations throughout the study period. Partially, the Fiscal Decentralization Degree (FDD) has a positive and significant effect on GRDP, with a probability value of 0.0138. In contrast, the Regional Financial Independence Ratio does not have a significant effect on GRDP, as indicated by a probability value of 0.8843. Simultaneously, FDD and the Regional Financial Independence Ratio significantly affect GRDP, with an F-statistic probability value of 0.0390. The coefficient of determination (R²) is 0.1173, indicating that 11.73% of GRDP variation can be explained by the two variables, while the remaining 88.27% is influenced by other factors outside the research model. This study concludes that strengthening regional fiscal capacity through the optimization of local own-source revenue and fiscal decentralization policies is essential for promoting sustainable economic growth in Jambi Province.

Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Various machine learning and deep learning approaches, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), are employed to predict RUL using real-time sensor data from rotating machinery. XAI methods such as SHAP, LIME, and attention mechanisms are applied to provide human-understandable explanations of model predictions. The models are evaluated based on accuracy, Root Mean Square Error (RMSE), and interpretability scores. The results show that XAI-enhanced models outperform traditional approaches in predictive performance while offering greater transparency. These explanations help maintenance engineers better understand the factors influencing predictions, thereby improving decision-making and trust in the system. Nevertheless, the integration of XAI introduces additional computational complexity, which may pose challenges for large-scale industrial implementation. Overall, this study highlights the potential of combining XAI with RUL prediction to develop more reliable, transparent, and effective predictive maintenance solutions.

This study aim to examines effectiveness concerning feedback based upon motor model inside of improving free throw accuracy inside of basketball plus its impact upon retention concerning skill among female student. Significance concerning this research lie inside of essential role concerning feedback inside of motor learning, particular when supported by way of accurate performance model that help learner to builds clear mental representation concerning movement execution.Experimental design were employed, involving sample concerning female student random assigned into two group. Experimental group receive feedback supported by way of motor model, including video demonstration concerning ideal plus corrected performance, while control group follow traditional instruction relying primary upon verbal explanation. Post-test were then administered to measures improvement, along alongside delayed retention test to evaluates persistence concerning learning over time. Result show statistical significant difference inside of favor concerning experimental group inside of both post-test plus retention outcome. This indicate that motor model-based feedback effective enhance performance plus support long-term skill retention. Addition, student exposed for motor model demonstrate greater ability to recognizes plus corrects error plus show deeper understanding concerning correct technique.Study recommend integrating motor modeling strategy plus technology-based feedback into sport education to accelerates learning plus improves outcome across educational context.

This study aims to analyze and examine the effect of financial literacy and self-control on financial management behavior, with financial attitude as an intervening variable among accounting students at universities in Lamongan. This research employed a quantitative approach using a survey method through questionnaire distribution via Google Form. The population consisted of accounting students at universities in Lamongan, with a total sample of 232 respondents selected using probability sampling with a simple random sampling technique. Data analysis was conducted using Partial Least Square–Structural Equation Modeling (PLS-SEM) version 4.0. The results indicate that financial literacy does not have a significant effect on financial management behavior, while self-control has a significant effect on financial management behavior. Furthermore, financial literacy and self-control significantly influence financial attitude, and financial attitude significantly affects financial management behavior. Financial attitude is also proven to significantly mediate the relationship between financial literacy and self-control on financial management behavior.

This study aims to analyze the effect of governance, competence, and work culture on employee performance effectiveness at Perumda BPR Garut. The research method used is a quantitative approach with descriptive and verificative analysis. The research population includes all employees, with a total sample of 98 respondents determined using a random sampling technique. Data were collected through questionnaires and analyzed using path analysis with the assistance of SPSS software. The results show that descriptively, governance, competence, work culture, and employee performance effectiveness are in the moderately high to high category. Verificative analysis indicates that governance, competence, and work culture have a significant effect on employee performance effectiveness both partially and simultaneously. Governance is the most dominant variable, followed by work culture and competence. Simultaneously, these three variables contribute 80.0% to employee performance effectiveness, while the remaining 20.0% is influenced by other factors outside the research model. This study emphasizes that governance plays a very important role in improving employee performance effectiveness. Therefore, strengthening a transparent, accountable, and fair governance system, along with continuous development of competence and work culture, becomes a key factor in achieving optimal organizational performance.

The research aims to find out and lyze the influence of employee empowerment on goal orientation through work accurancy at the Gempolkrep Sugar Factory, Gedeg district, Mojokerto Regency. The research method used is a quantitative research method with the number of samples used as many as 117 respondents using probability sampling techniques with proportionate random sampling techniques. The method used in the collection of data this study is to use a questionaire method with a scale os assesment using the likert scale. The data analysis technique used in this study is using SEM (stuctural equation modeling) with analysis test tools using IBM SPSS AMOS Statistics Version 22 software. The results showed that : 1) Employee empowerment has a significant and positive effect on the work accurancy of the Gempolkrep Sugar Factory in Mojokerto Regency. 2) Eployee empowerment has an significant and negative effect on the goal orientation of the Gempolkrep Sugar Factory in Mojokerto Regency. 3) Work accurancy has an significant and positive effect on the goal orientation of the Gempolkrep Sugar Factory in Mojokerto Regency.

The study investigates the impact of governance on firm value, along with the moderating role of institutional ownership, among companies operating in the consumer cyclicals and consumer non-cyclicals sectors listed on the Indonesia Stock Exchange (IDX) over the 2021-2023 period. Drawing on a sample of 42 companies and 126 observations, a quantitative panel data approach was adopted, with data gathered through purposive sampling. The Bloomberg Governance Score was used to measure governance, Tobin's Q served as a proxy for firm value, institutional ownership was the moderating variable, and firm size, leverage, and profitability were included as control variables. Model selection was conducted using the Chow and Hausman Tests in EViews 14, which indicated that the Random Effects Model (REM) was the most suitable estimation approach. The findings reveal that governance does not exert a significant influence on firm value. Furthermore, institutional ownership is unable to strengthen the relationship between governance and firm value. These results suggest that ESG-based governance signals have not yet been optimally absorbed by investors in the Indonesian capital market; hence, reinforcing governance reporting standards and enhancing investor ESG literacy are considered essential steps toward integrating non-financial information into investment decision-making.

This study empirically investigates how the degree of transparency in sustainability reporting influences earnings management practices among listed companies in Indonesia's energy sector on the Indonesia Stock Exchange, covering the period from 2021 to 2024. Grounded in three theoretical perspectives Agency Theory, Stakeholder Theory, and Legitimacy Theory this study argues that when companies disclose their sustainability information more comprehensively, management's capacity to manipulate reported earnings becomes increasingly constrained, owing to reduced information asymmetry and heightened external scrutiny. The independent variable is measured through the Sustainability Reporting Disclosure Index (SRDI), constructed based on the GRI Standards 2021, while earnings management is proxied by the absolute value of discretionary accruals derived from the Modified Jones Model. Firm size and leverage are incorporated as control variables. Using purposive sampling, 32 companies were selected, generating a total of 128 observations. The data were analysed through panel data regression employing a Random Effects Model with White's cross-section robust standard errors. The results reveal that sustainability reporting transparency does not exert a statistically significant effect on earnings management. This finding suggests that, within Indonesia's energy sector, sustainability reporting tends to function more as a legitimacy-seeking tool rather than a genuine expression of corporate transparency. Firm size was found to have a significant negative effect on earnings management, whereas leverage did not demonstrate a meaningful influence. This study contributes to the growing body of literature examining the relationship between sustainability reporting and earnings management, with particular relevance to the energy sector in the Indonesian context.

This study aims to determine the effect of financial performance, capital structure, and company size on company value in healthcare companies listed on the Indonesia Stock Exchange (IDX) for the 2020-2024 period. This study uses an associative quantitative approach with secondary data obtained from the company's financial statements. The sample was determined using purposive sampling, resulting in 9 healthcare companies with 45 observations. Data analysis was performed using EViews 12. Panel data regression analysis was applied using the Random Effect Model (REM), selected based on the Chow test, the Hausman test, and the Lagrange multiplier test. Furthermore, classical assumption testing and hypothesis testing were carried out. The test results show that partially Financial Performance has a significant positive effect on Company Value with a calculated T value of 2.137061 > T table 2.01954 with a prob value of 0.0386 < 0.05, Capital Structure does not have a significant effect on Company Value with a calculated T value of 0.4770233 < T table 2.01954 with a prob value of 0.6407 > 0.05, Company Size has a significant positive effect on Company Value with a calculated T value of 2.134309 > T table 2.01954 with a prob value of 0.0388 < 0.05. Simultaneously, the three independent variables have a significant positive effect on Company Value with an Fcount value of 3.059588 > Ftable 2.83 with a prob value of 0.038758 < 0.05, with a contribution of 12.31% while the remaining 87.69% is influenced by other factors outside this study.

This study examines the application of Transparent Artificial Intelligence (AI) for fraud detection in public welfare programs using publicly available administrative data. Persistent challenges in welfare governance such as misallocation, fraud, and data inaccuracy necessitate analytical frameworks that are both effective and explainable. The research aims to design and evaluate an interpretable anomaly detection system capable of identifying irregularities in welfare distribution while maintaining transparency and accountability. Methodologically, the study employs two unsupervised models Isolation Forest and Local Outlier Factor (LOF) to detect anomalies in sub-district-level welfare data, incorporating features such as population size, number of beneficiaries, and coverage ratio. An Explainable AI (XAI) framework integrating surrogate Random Forests, Permutation Feature Importance (PFI), and local linear surrogates (LIME-like) is applied to ensure interpretability of both global and local model behaviors. Findings reveal that receivers per 1000 population and percentage coverage are dominant determinants of anomaly scores. Fifteen administrative units were flagged for potential inconsistencies suggesting over- or under-reporting of beneficiaries. Cross-validation between IF and LOF models confirmed consistency in identifying anomalous regions. The integrated XAI explanations enhance transparency, enabling policymakers and auditors to trace the rationale behind detected anomalies. In conclusion, the proposed Transparent AI framework demonstrates that combining anomaly detection with interpretability tools can strengthen accountability and fairness in welfare administration. It offers a reproducible, ethical, and data-driven approach to social program monitoring, reinforcing public trust and supporting responsible AI governance.

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.

The development of Digital Marketing and Electronic Word of Mouth (eWOM) has encouraged hospitals to adopt digital communication strategies to attract patients’ visit intentions. However, in public hospitals, the effectiveness of these strategies in influencing patient behavior still requires empirical validation. This study aims to analyze the influence of Digital Marketing and eWOM on patient visit intention, with Brand Awareness as an intervening variable at RSUD Cengkareng.This research employed a quantitative approach with an explanatory design. Data were collected from November to December 2025 from 100 general outpatient respondents selected using the Slovin formula and a simple random sampling technique. The data were gathered through structured questionnaires and analyzed using Structural Equation Modeling (SEM) with AMOS software.The results show that Digital Marketing and eWOM have a positive and significant effect on Brand Awareness. Both variables also significantly influence patient visit intention, with eWOM demonstrating the strongest effect. These findings indicate that information shared through digital platforms and patient recommendations plays an important role in shaping potential patients’ decisions to visit the hospital. However, Brand Awareness does not have a significant effect on patient visit intention and does not mediate the relationship between Digital Marketing, eWOM, and visit intention. This suggests that, in the context of RSUD Cengkareng as a public hospital, patients’ visit decisions are more directly influenced by digital information exposure and the credibility of online recommendations rather than by brand recognition alone.

This study aims to determine: 1) The effect of the product on customer loyalty, 2) Price influence on customer loyalty, 3) The influence of place / location on customer loyalty, 4) The influence of promotion on customer loyalty, 5) The influence of people / employees on customer loyalty, 6) Effect of physical facilities on customer loyalty, 7) Effect of process on customer loyalty Bank BRI Unit Ampek Nagari Cabang Bukittinggi. Type of research conducted is Expost-Facto, the number of study population as much as 834 people, Proportional sampling technique Simple Random Sampling. The sample of this research is customer Bank BRI Unit Ampek Nagari Cabang Bukittinggi numbered 114 people. Data were collected through questionnaires using Likert scale models that have been tested for validity and reliability. Data analysis technique used is Path Analysis (Path Analysis). The research results are: 1) There is significant influence between product to customer loyalty, 2) There is a significant influence between price and customer loyalty, 3) There is a significant influence between place / location on customer loyalty, 4) There is significant influence between promotion to customer loyalty, 5) There is significant influence between person / employee to customer loyalty Training, 6) There is significant influence between physical facility to customer loyalty, 7) There is a significant influence between the process on customer loyalty Bank BRI Unit Ampek Nagari Cabang Bukittinggi. Viewed from direct or indirect influence of product, price, place / location, promotion, person / employee, physical facility and process to customer's loyalty Bank BRI Unit Ampek Nagari Cabang Bukittinggi is 84.54%, it is obtained that the promotion has the greatest influence of 21.01%. This shows that the understanding of promotion directly has a big role to the emergence of customer loyalty Bank BRI Unit Ampek Nagari Cabang Bukittinggi.

Digital transformation in public health services requires cadres to possess adequate digital literacy and technology adaptation skills. This study aims to analyze the influence of digital literacy and technology adaptation on the effectiveness of cadre performance, with digital competence as an intervening variable in Pekalongan Regency. The study population was health cadres in Pekalongan Regency, with a sample of 90 respondents selected using a technique that was part of the total population of 285 respondents. Sampling used simple random sampling. Data collection was carried out using a questionnaire that had been tested for validity and reliability. Data analysis used [analytical methods, for example: Structural Equation Modeling (SEM) or Path Analysis] to examine direct and indirect relationships between variables. The research results are expected to provide an overview of: (1) the influence of digital literacy on the effectiveness of cadre performance, (2) the influence of technology adaptation on the effectiveness of cadre performance, (3) the influence of digital literacy on digital competence, (4) the influence of technology adaptation on digital competence, (5) the influence of digital competence on the effectiveness of cadre performance, and (6) the role of digital competence as an intervening variable in the relationship between digital literacy and technology adaptation on the effectiveness of cadre performance. The findings of this research are expected to serve as a basis for developing training programs and increasing the capacity of health cadres in the digital era, particularly in Pekalongan Regency.

This study aims to analyze the effect of transformational leadership and compensation on job satisfaction with employee performance as an intervening variable at PT Cipta Kridatama Site KIM Jambi. This research employed a quantitative approach using a survey method. The population of this study consisted of all employee of PT Cipta Kridatama Site KIM Jambi totaling 862 employees. The sample was determined using the slovin formula with a 10 percent error tolerance, resulting in 90 respondents. The sampling technique used was proportionate stratified random sampling. Data were collected through structural questionnaires and analyzed using Structural Equation Modeling based on Partial Least Squares (PLS-SEM) With The Assistance Of Smartpls 3 Software.  The result of  the study indicate that transformational leadership and compensation have a positive and significant effect on employee performance. Transformational leadership and compensation also have a positive and significant effect on job satisfaction. Furthermore, employee performance has a positive adn significant effect an job satisfaction.  The mediation test result show that employee performance is able to mediate the effect of transformational leadership and compensation on job satisfaction.These findings suggest that improving job satisfaction cannot be achieved solely through leadership and compensation policies, but also requires efforts to enhance employee performance. Therefore, organizations are encouraged to implement effective transformational leadership practices and fair compensation systems to improve employee performance and job satisfaction, particularly in high demand industries such as mining services.

This study investigates the factors influencing export performance in five ASEAN countries Indonesia, Malaysia, the Philippines, Singapore, and Thailand during the 2014-2023 period. The topic is highly relevant given the vital role of exports in sustaining monetary stability and promoting long-term economic growth. The novelty of this research lies in its integrated approach, which simultaneously examines key export-related macroeconomic variables, namely foreign direct investment and inflation, while incorporating foreign exchange reserves as a moderating variable an approach that remains limited in existing ASEAN-focused studies. This analysis uses secondary data obtained from the World Bank and processed using panel data regression methods, including the Common Effect Model, Fixed Effect Model, and Random Effect Model, strengthened by a Moderated Regression Analysis (MRA) approach. The results show that foreign direct investment and inflation significantly influence foreign exchange reserves. Furthermore, foreign exchange reserves have been shown to play a strategic role in strengthening the economic resilience of ASEAN countries and can be used as a reference in formulating monetary and international trade policies.

This study investigates the factors influencing export performance in five ASEAN countries Indonesia, Malaysia, the Philippines, Singapore, and Thailand during the 2014-2023 period. The topic is highly relevant given the vital role of exports in sustaining monetary stability and promoting long-term economic growth. The novelty of this research lies in its integrated approach, which simultaneously examines key export-related macroeconomic variables, namely foreign direct investment and inflation, while incorporating foreign exchange reserves as a moderating variable an approach that remains limited in existing ASEAN-focused studies. This analysis uses secondary data obtained from the World Bank and processed using panel data regression methods, including the Common Effect Model, Fixed Effect Model, and Random Effect Model, strengthened by a Moderated Regression Analysis (MRA) approach. The results show that foreign direct investment and inflation significantly influence foreign exchange reserves. Furthermore, foreign exchange reserves have been shown to play a strategic role in strengthening the economic resilience of ASEAN countries and can be used as a reference in formulating monetary and international trade policies.

The assessment of a company's value is crucial for investors to identify its prospects and performance. Financial ratios such as the Current Ratio (CR) and Return on Assets (ROA) are used to analyze factors affecting the company's value. This study aims to analyze the impact of CR and ROA on company value in manufacturing companies within the Miscellaneous Industries sub-sector for the period 2015–2024. The study uses a quantitative approach with data from annual financial reports of companies listed on the Indonesia Stock Exchange. Data analysis is conducted using panel data regression with the Random Effect Model (REM) as the best model. The dependent variable is company value, measured by Price to Book Value (PBV), while the independent variables consist of CR and ROA. The results show that CR does not have a significant effect on company value, while ROA significantly affects company value. Simultaneously, CR and ROA are proven to significantly affect company value, indicating that the combination of liquidity and profitability plays an important role in explaining PBV variations. This finding suggests that investors pay more attention to profitability than liquidity in the Miscellaneous Industries sector.

This study aims to analyze the externalities of the existence and activities of black grass jelly household crafts on labor absorption and the environment in Gampong Jawa. The study uses primary data obtained from distributing questionnaires. Data collection used a simple random sampling technique with 65 respondents. The model used in this study uses a quantitative descriptive analysis method with results measurement using a Likert scale. The results of the study indicate that the activities of black grass jelly household crafts in Gampong Jawa, Langsa City provide positive externalities on labor absorption with a labor absorption aspect index value of 77.54%. The environmental aspect index value was obtained with a total value of 76.51% which means that it is agreed that the activities of black grass jelly household crafts in Gampong Jawa, Langsa City provide a relatively smaller negative effect/externality, which is only 23.49%.  

This study aims to: (1) Determine the effect of the discovery learning model with the aptitude treatment interaction strategy on the mathematical concept comprehension and self-efficacy of 10th-grade students at SMA Negeri 3 Tarakan; (2) Determine the effect of the discovery learning model with the aptitude treatment interaction strategy on the mathematical concept comprehension of 10th-grade students at SMA Negeri 3 Tarakan; (3) Determine the effect of the discovery learning model with the aptitude treatment interaction strategy on the self-efficacy of 10th-grade students at SMA Negeri 3 Tarakan. The research population included all tenth-grade students of SMA Negeri 3 Tarakan in the 2024/2025 academic year. The research sample consisted of two classes selected randomly: one experimental class receiving discovery learning with an aptitude treatment interaction strategy and one control class receiving conventional learning. The research instruments consisted of a test measuring mathematical concept understanding on trigonometry material and a self-efficacy questionnaire. The data obtained were tested for prerequisites through normality and homogeneity tests before being analyzed using inferential statistical tests in the form of an independent samples t-test with the assistance of SPSS software version 26.0. The research results show that the implementation of the discovery learning model with the aptitude-treatment interaction strategy has a significant impact on students' mathematical concept understanding and self-efficacy simultaneously, with a significance value of 0.006 < 0.05. Partially, this learning model has a significant effect on students' mathematical concept understanding, with a significance value of 0.018 < 0.05. However, the effect of the discovery learning model with the aptitude-treatment interaction strategy on students' self-efficacy is not statistically significant, as indicated by a significance value of 0.089 > 0.05, even though there is a tendency for increased self-efficacy among students participating in the experimental class learning. Nevertheless, the influence of the discovery learning model with the aptitude treatment interaction strategy on students' self-efficacy is not statistically significant in partial terms, although there is a tendency for an increase in self-efficacy among students participating in the experimental class. These findings suggest that the discovery learning model with the aptitude treatment interaction strategy is effective in improving students' understanding of mathematical concepts in trigonometry material and has the potential to support the development of self-efficacy in mathematics learning.