The platform of digital media has particularly reshaped the political structure of Indonesia. The public is using social media applications for political participation and an expression of their political opinions. This survey was conducted in Indonesia to examine the impact of digital media among youngsters and their political engagement through digital platforms. The study was influenced by the phenomenon of massive student participation, which occurred in August 2019, in Indonesia as a reaction to a new corruption (KPK) law. The study opted for the public sphere theory of Jürgen Habermas; ‘The Public Sphere’. This correlates to the study objective whereby youth participate in political matters to learn and to discuss their common beliefs in the public spheres. The study design is quantitative and more than four hundred respondents were selected during the protests through random sampling techniques. The conclusion of the study demonstrates that youth participate in multiple activities, such as protests for new laws, which indeed strengthen democracy and contribute to the political structure of Indonesia. Therefore, youth participation through social media platforms shows their political participation in Indonesia. The study also suggests a few recommendations for youth to show their participation through online spheres.

The study examined the mediating roles of adaptive leadership in the relationship between financial technology innovation and rural bank performance. A survey-based research approach was applied, and the hypotheses were tested using a sample of 305 respondents. A measurement model was employed to evaluate the validity and reliability of the scales used in the study. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the hypothesized relationships and test the mediation effects. The empirical findings support the hypothesized relationship between both financial technology innovation and the director’s individual performance on rural bank performance. Adaptive leadership was found not to mediate the association between financial technology innovation and rural bank performance. The research highlights the importance of financial technology innovation and individual director performance in enhancing rural bank performance. Furthermore, the findings support the notion that developing financial technology innovation is crucial for fostering adaptive leadership. Additionally, adaptive leadership contributes to strengthening director individual performance, which ultimately drives overall performance improvements in rural banks. The integration of these variables offers new empirical insights. It expands the understanding of rural bank performance, highlighting how internal capabilities can be optimized to improve organizational outcomes in this under-researched sector.

The coffee shop industry has experienced significant growth, evolving into a highly competitive marketplace demanding specialty coffee and personalized experiences. While data-driven strategies are crucial for optimizing operations, many owners still struggle to effectively leverage their sales data to understand dynamic customer behavior and enhance decision-making. Addressing this gap, this study explores the application of machine learning (ML) techniques, specifically the Random Forest Regressor model, to predict sales performance within the coffee shop business environment. By analyzing factors such as transaction timing, store location, product type, and day of the week, this research aims to uncover patterns that can enhance inventory management and customer engagement. The Random Forest model was evaluated through cross-validation, yielding a mean Mean Squared Error (MSE) of 80.97, which indicates moderate predictive accuracy and represents an improvement over traditional forecasting methods commonly employed in the industry. Feature importance analysis revealed that Premium Beans is the most influential predictor, followed by seasonal trends (month), time of day, and weekend sales patterns. These findings underscore the importance of incorporating temporal and contextual factors into forecasting models.

Penelitian ini bertujuan untuk menganalisis sentimen komentar mengenai gaya hidup childfree di platform media sosial X menggunakan metode Naïve Bayes. Gaya hidup childfree menjadi perbincangan yang semakin populer setelah Gita Savitri Devi mengungkapkan pilihannya untuk tidak memiliki anak. Kontroversi muncul di media sosial seputar hak seseorang untuk memilih gaya hidup childfree, dengan berbagai alasan seperti kekhawatiran terhadap masa depan anak, masalah lingkungan, ekonomi, dan faktor personal. Metode Naïve Bayes digunakan dalam penelitian ini karena kemampuannya dalam klasifikasi teks dan analisis sentimen. Hasil penelitian menunjukkan akurasi model sebesar 66,20%, meskipun tergolong rendah, penelitian ini memberikan wawasan baru tentang pandangan masyarakat terhadap gaya hidup childfree. Diharapkan hasil penelitian ini dapat meningkatkan pemahaman kita tentang fenomena childfree dan memberikan kontribusi terhadap pengembangan metode analisis sentimen di masa depan.

This research focuses on developing a simulation model for forming student study groups using an enhanced K-Means algorithm, addressing the challenge of optimizing group dynamics to improve learning outcomes. By analyzing the effectiveness of the formed study groups through RMSE (Root Mean Square Error) after dimensionality reduction with various regression models—including Linear Regression, Ridge Regression, Lasso Regression, Elastic Net, Random Forest Regressor, Gradient Boosting Regressor, and XGBoost Regressor—we aim to provide educators with a robust tool for assessing group configurations. The study identifies four distinct clusters, revealing that "Previous_Score" and "Attendance" are critical variables, achieving a highest Silhouette Score of 0.64 with five selected features. The ridge regression model also yielded a low RMSE of 0.045, explaining 72.39% of the variance in "Exam_Score." The findings suggest that targeted interventions tailored to each cluster—yellow, purple, blue, and green—can enhance academic outcomes by addressing specific student needs. This data-driven approach optimizes group dynamics and fosters a more inclusive learning environment, enhancing academic performance and cultivating essential social skills. The study underscores the potential of machine learning techniques in education and suggests avenues for future research into alternative clustering methods and their long-term impact on student engagement and success.

This study aims to investigate the relationships between authentic leadership, organizational commitment, trust, and knowledge sharing behavior in the context of the workplace. Data were collected from members of the astronomy community in Indonesia involved in the development of community-based astronomy tourism. Multiple linear regression analysis was used to test the relationships between these variables. The results show that authentic leadership has a significant positive relationship with organizational commitment and knowledge sharing behavior, both tacit and explicit. Organizational commitment also mediates the relationship between authentic leadership and knowledge sharing behavior. However, trust does not strengthen the relationship between organizational commitment and knowledge sharing behavior. Managerial implications include the importance of developing authentic leadership and fostering organizational cultures that support employee commitment. Academic implications include supporting theories of authentic leadership and enriching understanding of the mechanisms of mediation and moderation in the relationships among variables. This study provides a significant contribution to understanding the dynamics of workplace relationships and highlights the importance of psychological factors in the development of sustainable organizations.