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This study aims to determine the extent to which professional competence and personality competence influence teacher performance at SMAN 1 Sangatta Selatan. This research is categorized as field research using a quantitative approach. Data were collected through questionnaires to obtain responses from participants, observations to directly monitor activities, and documentation techniques to gather supporting data. The population in this study consisted of 33 teachers at SMAN 1 Sangatta Selatan. The sample was determined using the Slovin formula, resulting in 33 respondents, all of whom were teachers from the same school. To analyze the data, multiple linear regression analysis was employed. The results showed that professional competence significantly influences teacher performance. The influence of professional competence (X1) on teacher performance (Y) was 7.2%, while the influence of personality competence (X2) on teacher performance (Y) reached 4.5%. The coefficient of determination (R²) value was 0.807, indicating that 80.7% of teacher performance at SMAN 1 Sangatta Selatan is jointly influenced by professional competence (X1) and personality competence (X2), while the remaining 19.3% is influenced by other variables not examined in this study. Based on the analysis, personality competence was found to have a more significant simultaneous effect on teacher.    

Rapid developments in the Artificial Intelligence (AI) industry have triggered an increased need for workers with specialized competencies, which has implications for significant variations in salary levels. This research aims to analyze the factors that influence salaries in the AI sector using the multiple linear regression method. The dataset used includes 15,000 AI job vacancies with variables including job and company characteristics. The data was engineered via the one-hot encoding method and divided into two parts: training data (80%) and test data (20%). The analysis results show that the regression model is able to explain 85% of the variation in salary, with an R² value of 0.85 and a Root Mean Square Error (RMSE) of USD 23,221. The three main factors identified as having a significant influence on salaries in the AI field are work experience, company location, and the industry in which the company operates. The experience factor reflects the skills and knowledge developed over many years, which can increase productivity (Rony et al., 2023). Company location also plays an important role, as the cost of living and demand for skilled labor varies by region (Badran, 2019). Additionally, the specific industry in which an employee works influences salary, given that more developed industries can often offer higher compensation (Huang, 2025). This research makes a significant empirical contribution to the understanding of compensation structures in the AI labor market.

The increasing number of Indonesian Migrant Workers (TKW) working abroad, particularly through programs organized by BP2MI, has become a significant concern in managing the labor export process. One of the challenges faced is the uncertainty of the number of TKW to be sent each year, which is influenced by various external and internal factors. Therefore, this study aims to apply artificial neural networks (ANN) with a backpropagation algorithm approach to predict the number of TKW that will be processed by BP2MI. This method was chosen due to its ability to recognize patterns and nonlinear relationships between variables that affect the decision-making process for TKW export. In this study, the data used includes factors such as the number of job seekers, government policies, and the condition of the international labor market. The artificial neural network with the backpropagation algorithm is used to train the model based on existing historical data, with the goal of generating accurate predictions regarding the number of TKW to be processed in the coming years. The results of the tests show that the developed model can provide fairly accurate predictions and can serve as a tool for BP2MI in planning and managing the export of TKW more effectively. With the application of this technology, it is expected that the decision-making process related to TKW export can become more efficient and well-predicted.

The retail industry generates an extremely large and continuously growing volume of transactional data along with the advancement of digital technology, thereby requiring sophisticated and systematic data analysis approaches to support effective and evidence-based business decision-making. This study aims to analyze retail sales data by utilizing the Retail Sales Dataset obtained from the Kaggle platform, which consists of 100,000 transaction records and broadly represents the characteristics of retail transactions. The main focus of this study is to classify product categories and predict customer segments, including the identification of high-spending customers (high spenders), based on demographic attributes such as age and gender, as well as various transaction-related features. The research methodology includes data preprocessing, label encoding, and feature engineering to generate additional variables, including Age_Group, Is_Holiday, and Spender_Group, which are expected to enhance the predictive capability of the models. Several machine learning algorithms, namely Decision Tree, Random Forest, and XGBoost, were implemented and evaluated to compare their respective performance. The experimental results indicate that multiclass product category classification achieves relatively low accuracy, ranging from 27% to 34%. These findings suggest the high complexity of retail data and highlight the need for further model optimization, class balancing techniques, and feature refinement to improve predictive performance in future studies.

Tax Management refers to a company’s efforts to manage its tax obligations efficiently and legally in order to optimize net income. This study aims to examine the effect of Fixed Asset Intensity and Leverage on Tax Management, with Profitability as a moderating variable, in mining companies listed on the Indonesia Stock Exchange (IDX) for the 2021–2024 period. This research is conducted because tax management practices are considered to potentially influence corporate profitability and financial performance. The study is grounded in Agency Theory and employs a quantitative approach. The sample was selected using purposive sampling, resulting in 28 companies observed over four years, with a total of 112 secondary data observations obtained from annual reports or financial statements. Data analysis was performed using EViews 13 with a Moderated Regression Analysis (MRA) approach. The findings indicate that: (1) Fixed Asset Intensity has no significant effect on Tax Management; (2) Leverage has a significant negative effect on Tax Management; (3) Profitability does not moderate the relationship between Fixed Asset Intensity and Tax Management; and (4) Profitability strengthens the effect of Leverage on Tax Management.

The availability of quality human resources provides an opportunity for leaders to select the best employees for strategy implementation. However, the effectiveness of strategy implementation depends not only on human resources but also on the organizational culture and leadership style applied. Based on the results of the first hypothesis test, it was found that the organizational culture variable has a positive and significant effect on employee performance at the Pesisir Selatan Regency DPMDPPKB. Therefore, in this study, the first hypothesis (H1) is accepted. Based on the results of the second hypothesis test, it was found that the leadership style variable has a positive and significant effect on employee performance at the Pesisir Selatan Regency DPMDPPKB. Therefore, in this study, the second hypothesis (H2) is accepted. Organizational culture partially has a positive and significant effect on employee performance. Therefore, it can be concluded that if organizational culture improves, employee performance will also improve. Leadership style has a positive and significant effect on employee performance. Therefore, it can be concluded that if leadership style improves, employee performance will also improve. Organizational Culture and Leadership Style simultaneously have a positive and significant effect on employee performance at the Pesisir Selatan Regency DPMDPPKB. Therefore, it can be concluded that if Organizational Culture and Leadership Style improve simultaneously, employee performance will also improve.

This study aims to: (1) describe the implementation of local wisdom–based learning in the teaching process of Grade VII B at UPT SMP Negeri 4 Kelara Jeneponto; (2) determine the improvement of students’ social awareness after the implementation of local wisdom–based learning; and (3) identify the enhancement of students’ learning culture through the application of this learning model. The research employed a Classroom Action Research (CAR) design using the Kemmis and McTaggart model, conducted in two cycles consisting of planning, action, observation, and reflection. The subjects were 25 seventh-grade students, focusing on two main variables: social awareness and learning culture. Data were collected through observation, questionnaires, and documentation, and analyzed using descriptive quantitative and qualitative techniques. The findings indicate that the integration of Jeneponto cultural values Tabe’ Karaeng, Sipakatau, Sipakalabbiri, Sipakainga, Siri’ na Pacce, Akbulo Sibatang, and empo sipitangari was implemented effectively and became more optimal in Cycle II after improvements were made through Problem-Based Learning (PBL) and the use of more contextual local cases. Students’ social awareness increased from an average of 63.13% in Cycle I (categorized as Fair) to 80.42% in Cycle II (categorized as High). Similarly, students’ learning culture improved from 69.69% (Fair) in Cycle I to 82.66% (High) in Cycle II, meeting the classical mastery criteria. The study concludes that integrating local wisdom into classroom instruction not only strengthens students’ understanding of social studies content but is also effective in shaping their social character and learning habits. This model is recommended for development in other subjects and educational levels.

This study aims to (1) analyze the effect of gamification on students’ learning motivation and (2) analyze the effect of gamification on students’ positive perceptions in economics learning among Grade X students at SMAN 1 Pangkep. This research employed a quantitative approach with an ex post facto design and involved 198 students selected through proportional random sampling. A Likert-scale questionnaire was used as the main research instrument to measure the variables of gamification, learning motivation, and positive perception. Data analysis was conducted using simple linear regression to identify relationship patterns and determine the magnitude of influence of the independent variable on both dependent variables. The results of the study indicate that gamification has a positive and significant effect on student learning motivation. In addition, gamification also has a positive and significant effect on students' positive perceptions of economics learning. These findings indicate that the application of gamification elements in learning can encourage student engagement, increase learning motivation, and create a more interesting, enjoyable, and meaningful learning experience..

This study examines the impact of capital expenditure, balancing funds, and local revenue on economic growth in the former Besuki Residency area, covering Banyuwangi, Jember, Situbondo, and Bondowoso Regencies during the 2014-2024 period. Using path analysis on panel data and individual district analysis, the study investigates the direct, indirect, and mediating effects of local revenue as an intervening variable. The results show that all three independent variables significantly affect economic growth in the panel data for all four regencies. Capital expenditure has a significant direct effect only in Bondowoso Regency and overall panel data, but is insignificant in Banyuwangi, Jember, and Situbondo. Balancing funds exhibit a significant direct effect across all regions, while local revenue has a consistent direct effect in all regions and panel data. The mediating role of local revenue is inconsistent, with the indirect effect of capital expenditure through local revenue being insignificant in all regions. On the other hand, the indirect effect of balancing funds through local revenue is significant in Banyuwangi, nearly significant in Jember and Situbondo, and insignificant in Bondowoso. These findings highlight the complexity of fiscal dynamics in decentralization, where local revenue management and governance factors are crucial. The study supports previous research suggesting that central fiscal transfers are less effective without strong local revenue support. Local governments are encouraged to enhance local revenue autonomy to maximize the multiplier effect of capital expenditures and balancing funds, fostering sustainable economic growth.

Instagram has become a strategic medium for government institutions in delivering public information, particularly weather and disaster-related information that requires speed, clarity, and ease of understanding. The BMKG of Gowa Regency utilizes digital communication innovations through the use of informative memes and a virtual character named Shinna as a visual communication strategy to enhance the effectiveness of information dissemination to the public. This study aims to analyze the influence of meme usage and the virtual character Shinna on the information satisfaction of Instagram users of BMKG Gowa Regency by employing the Stimulus–Response (S–R) theory perspective, which views visual messages as stimuli capable of eliciting cognitive and affective responses from audiences. This research adopts a quantitative approach using a survey method. Data were collected through an online questionnaire with a four-point Likert scale administered to followers of the BMKG Gowa Regency Instagram account who had viewed or interacted with meme content and the virtual character Shinna. Data analysis was conducted using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with the assistance of SmartPLS software. The results indicate that the use of memes and the virtual character Shinna has a positive and significant effect on users’ information satisfaction, both partially and simultaneously. Meme usage shows a more dominant influence compared to the virtual character Shinna, while the coefficient of determination (R²) value of 0.834 indicates that both variables explain 83.4% of the variance in users’ information satisfaction. These findings confirm that public communication strategies based on memes and virtual characters are effective in enhancing audience understanding, comfort, and satisfaction with disaster-related information.

Because it directly impacts revenue, customer loyalty, and long-term business sustainability, customer churn is a critical issue for the e-commerce industry. High churn rates indicate that a business is unable to retain existing customers, which means it is more expensive to acquire new customers. Therefore, a precise analytical approach is needed to identify customer behavior patterns that are likely to churn. Using machine learning methods, this study analyzes and predicts customer churn. For this study, the E-Commerce Customer Churn 2025 dataset, obtained from Kaggle, was used. This dataset consists of 10,000 customer data and contains fifteen variables covering transaction behavior, customer characteristics, and churn status. Data preprocessing, descriptive analysis, exploratory data analysis (EDA), and classification model development using Logistic Regression and Random Forest algorithms were part of the research project. Model evaluation was conducted using a Confusion Matrix and Receiver Operating Characteristic (ROC) Curve to evaluate the model's accuracy and ability to distinguish between churned and non-churned customers. The results showed that the Random Forest model performed better than Logistic Regression, with an ROC-AUC of 1.00. Furthermore, feature importance analysis revealed that the days_since_last_purchase variable was the most dominant factor in predicting customer churn. These findings are expected to help e-commerce companies design more effective, data-driven customer retention strategies.  

Stocks represent a capital market instrument with the potential to generate high returns. When making investment decisions, investors typically assess various internal aspects of a company, including its financial performance. The objective of this study is to examine the influence of profitability, liquidity, and leverage ratios on stock prices in the Indonesian banking sector, with a particular focus on state-owned banks, in both partial and simultaneous regression models. The methodology employed is quantitative analysis, with a secondary data set being utilized. The sample was determined using a purposive sampling technique, covering four state-owned banks (BRI, BNI, Mandiri, and BTN) for the 2010-2024 period. The findings of the analysis demonstrate that profitability and leverage exert a substantial negative influence on the stock prices of these banking institutions, while the liquidity ratio does not demonstrate a significant effect. Concurrently, all three variables exert an influence on stock prices, with an R-squared value of 58%.

This study aims to examine the influence of internship experience, work competencies, and career flexibility on the employability readiness of students from the Management Study Program at Universitas Maritim Raja Ali Haji in entering the workforce. A survey method with a quantitative approach was applied to 114 respondents selected randomly. Data was collected through questionnaires and analyzed using multiple linear regression with SPSS version 22. The results show that internship experience does not significantly affect students' readiness to enter the workforce. However, work skills and career flexibility variables have a positive and significant impact on employability readiness. Overall, these three factors contribute positively and significantly to students' readiness to enter the workforce. The findings highlight the importance of integrated development within the higher education curriculum, including improving work skills and career adaptation to better prepare students for professional challenges. Therefore, higher education institutions need to consider these factors when designing more relevant and adequate educational programs for students.

This study aims to determine the influence of work discipline (variable X1) and work culture (variable X2) on employee performance (variable Y). The research method used is a descriptive and verifiable method with a quantitative approach. Data collection techniques include observation, interviews, documentation, and literature studies to obtain comprehensive and relevant data for the purpose of the research. Data analysis techniques are carried out systematically, starting from the data compilation process, data tabulation, to the hypothesis testing stage. Data analysis uses multiple linear regression to determine the influence of each independent variable on the dependent variable. The results showed that the determination coefficient (R²) value was 69.8%, which means that work discipline and work culture simultaneously have a significant effect on employee performance. Meanwhile, 30.2% of employee performance was influenced by other factors outside of the study, such as motivation, work ability, leadership, personality, job satisfaction, work environment, and employee loyalty. Thus, it can be concluded that improving work discipline and a positive work culture plays an important role in improving.

This study aims to analyze the effect of education level on the quality of financial statements based on SAK EMKM, with accounting information systems as a moderating variable, focusing on coffee shop micro, small, and medium enterprises (MSMEs) in Kediri City, Indonesia. A quantitative approach was employed using primary data collected through structured questionnaires distributed to 32 business owners or financial managers selected by purposive sampling. Data were analyzed using descriptive statistics, classical assumption tests, and Moderated Regression Analysis (MRA) with SPSS software.The results indicate that education level has a positive and significant effect on the quality of financial statements, suggesting that higher educational attainment enhances accounting understanding and compliance with SAK EMKM. However, accounting information systems were found to weaken this relationship, indicating that system complexity, limited digital literacy, and insufficient technical training may reduce the effectiveness of education in improving financial reporting quality. These findings highlight that improving financial reporting quality among MSMEs requires not only higher educational capacity but also appropriate technological support and continuous technical assistance. This study contributes to the accounting literature by emphasizing the contextual role of accounting information systems in shaping the relationship between human capital and financial reporting quality in MSMEs.

The process of determining scholarship eligibility often faces challenges related to subjectivity and uncertainty in assessment criteria, which can result in inaccurate and unfair decisions. Scholarship selection generally involves multiple criteria, such as academic achievement, family economic conditions, and supporting factors that are difficult to evaluate using conventional decision-making methods. Therefore, an appropriate decision support approach is required to handle such uncertainty effectively. This study aims to implement the Fuzzy Mamdani method in a decision support system to determine scholarship eligibility more objectively and accurately. The research method consists of data collection, fuzzification of input variables, formulation of fuzzy rules, inference using the Mamdani approach, and defuzzification using the centroid method to obtain a crisp eligibility value. The results show that the Fuzzy Mamdani method is capable of producing flexible eligibility scores by considering the degree of membership of each criterion. The generated output reflects real conditions more comprehensively compared to traditional methods. The implementation of this method can assist decision-makers in improving transparency, consistency, and fairness in scholarship selection. This research is expected to contribute to the development of intelligent decision support systems in the field of educational assessment.

This study aims to determine the effect of service quality and promotion on members' saving decisions at BMT Fajar Bina Sejahtera Metro Branch. This research uses a quantitative approach with data collection through questionnaires given to 98 respondents, selected using purposive sampling technique. Data analysis was conducted using multiple linear regression with the help of SPSS software. The results show that both service quality and promotion have a positive and significant effect on saving decisions, both partially and simultaneously. However, service quality has a more dominant influence compared to promotion. The coefficient of determination (Adjusted R²) indicates that 32.4% of the variation in saving decisions can be explained by these two independent variables. Therefore, BMT Fajar Bina Sejahtera needs to continuously improve service quality and design more effective promotional strategies to attract and retain members to be more active in saving. This research provides important insights for BMT management in formulating more precise policies to enhance members' saving decisions.

Social media has become a double-edged sword; acting as a space for religious learning while simultaneously serving as a medium for spreading radicalism. As digital natives, students are vulnerable to extremist narratives if not equipped with adequate cognitive immunity. This study aims to examine the influence of Digital Religious Literacy and Critical Thinking skills on the resilience of Islamic Religious Education (PAI) students in countering radical narratives. Using a quantitative approach with a correlational method, this study involved 30 PAI students selected through random sampling. Data collection instruments used a Likert scale questionnaire that had been tested for validity and reliability. Data analysis was performed using multiple linear regression. The results showed that: (1) The level of digital religious literacy and counter-radicalism attitudes of students were in the "very high" category with mean scores of 22.40 and 23.00 respectively; (2) There is a significant positive relationship (Sig. < 0.05) between digital literacy and critical thinking towards the attitude of countering radicalism; and (3) Simultaneously, these two variables contributed 68.2% in forming students' mental resilience in the digital space. These findings imply the need for a reorientation of the PAI curriculum that integrates the strengthening of critical reasoning and digital literacy as a fortress of religious moderation.

This study investigated how employee engagement and work–life balance influenced turnover intention, with rewards examined as an intervening mechanism, among Generation Z employees in a state-owned enterprise context. A quantitative explanatory design was applied using a census approach involving all 36 Generation Z employees at PT PLN (Persero) Pematangsiantar. Data were collected through structured questionnaires measured on a five-point Likert scale and were analyzed using multiple regression and mediation-based path analysis in SPSS. The findings showed that employee engagement and work–life balance significantly predicted perceived rewards, and all three variables exerted significant effects on turnover intention. Moreover, rewards partially mediated the effects of employee engagement and work–life balance on turnover intention, indicating that positive work experiences reduced employees’ intention to leave when they were reinforced by fair and meaningful reward practices. However, the small sample size limited the generalizability of the findings beyond the organizational setting studied. This study contributes by demonstrating that retention of Generation Z employees is shaped not only by psychological attachment and work–life balance, but also by how organizations institutionalize recognition and value exchange through reward systems, particularly in public service and state-owned enterprises.

This article proposes Marketing 0.0 as a fundamental conceptual repositioning of marketing theory rooted in the Trust Economy framework. Unlike mainstream marketing theory, which positions trust as an outcome of communication, persuasion, or brand reputation, cross-disciplinary studies in economics, sociology, and neuroscience suggest that trust functions as a pre-decisional condition that precedes market exchange. This research employs a conceptual-theoretical methodology, synthesizing transaction cost theory, social capital, neuroeconomics, and contemporary trust literature. The article's primary contribution is formalizing the shift in trust's position from a dependent variable to an ontological gateway in marketing decision-making. Marketing 0.0 is positioned not as a stage in marketing evolution, but rather as a conceptual ground zero that enables continuous tactical adaptation without reliance on pseudo-novelty. The article concludes with theoretical and practical implications for the development of marketing science in an era of uncertainty and information saturation.