This study aims to analyze the impact of Green Human Resource Management (GHRM) on Employee Green Behavior (EGB), mediated by Green Work Engagement (GWE) and moderated by Individual Green Values. GHRM plays a crucial role in encouraging employees to engage in environmentally friendly practices, yet its impact on EGB is not always consistent. Using a quantitative-explanatory approach, data were collected through questionnaires with a Likert scale distributed to 66 employees involved in sustainability programs at the resort. The results of this study indicate that GHRM and GWE can influence the EGB, GWE can mediates the relationship between GHRM and EGB, IGV can moderates the relationship between GHRM and EGB. Future studies are encouraged to examine the role of employee ambidexterity as a moderating factor in the relationship between GHRM and GWE on EGB. Moreover, it is recommended that future research adopts a longitudinal design and a mixed-methods approach to provide more in-depth insights into employee environmental behavior.

This community service program was implemented to strengthen the digital literacy and competencies of health cadres in Simogirang Village, Prambon Sub-district, Sidoarjo Regency, particularly in the recording and management of Posyandu (integrated health service post) data. The initiative responded to ongoing issues stemming from manual documentation methods, which were prone to human error, inefficiency, and lack of integration with regional health information systems. To address these challenges, the program delivered an IT-based training module using a combination of direct instruction, demonstrations, and hands-on simulations. The training emphasized the use of accessible and cost-effective tools such as Google Forms and Google Sheets, designed in alignment with the principles of adult learning (andragogy) and Kurt Lewin’s change management model to facilitate behavioral adaptation and technology acceptance among the participants. A total of 25 health cadres participated in the program. Pre- and post-training evaluations demonstrated significant improvement in digital skills, with 80% of participants achieving a minimum of 85% accuracy in completing digital forms and managing records. In addition, a prototype digital system based on Google Workspace was successfully piloted in two Posyandu locations. This system enhanced the timeliness, accuracy, and integration of health data reporting to local health centers (Puskesmas), offering a scalable and sustainable solution for grassroots healthcare data management. Despite the positive outcomes, the program also identified key challenges, notably limited internet infrastructure and varying levels of prior digital exposure among cadres. These findings underscore the need for continuous mentoring, support from local government stakeholders, and potential investment in digital infrastructure. Overall, the initiative contributes to Indonesia’s broader goals of digital transformation in public health, particularly at the village level.

Science learning in public schools should be designed to accommodate both regular students and students with special education needs. This is also one of the implementations of SDG 4, Sustainable Development, specifically related to quality education and inclusive education. This study aims to measure the effectiveness of cooperative learning integrated with local wisdom values of Huyula in improving science concept mastery among regular students and students with special education needs in public schools. The study employs a pre-experimental design with a one-group, pretest-posttest approach. The study was conducted in five schools involving 323 regular students and 12 students with special education needs. The research results on the effectiveness of cooperative learning integrated with the local wisdom values of Huyula showed a significant increase in science concept mastery. This is indicated by the hypothesis test through the Wilcoxon signed-rank test with a value of z = -15.84, through the N-Gain test (high category, average 0.74), and a strong effect size through the rank-biserial correlation value (-1.00). This study concludes that the implementation of science learning integrated with Huyula values and media variety has an influence and is effective in improving the science concept mastery of regular students in the high category and students with special education needs in the medium category. This finding highlights the importance of integrating local wisdom values and media variety in supporting the improvement of science concept mastery through meaningful and inclusive teaching for all students. 

On the one hand, amid the digital transformation of the financial sector, opportunities have increased for Micro, Small, and Medium Enterprises (MSMEs); on the other hand, challenges remain in technology adaptation and sustainable financial management, especially in a post-crisis era. Thus, given the ongoing push toward strengthening economic resilience, an urgent need arises to examine how far digital financial intelligence may act as a key for the survival and growth of MSMEs. The focus of this study is to examine the impact of digital financial intelligence-the financial literacy, attitude toward financial technology, and capability to use digital financial applications-on MSME economic resilience. Data were collected for the survey using a quantitative approach, which consisted of an online questionnaire administered to 220 MSME actors in Indonesia, who were digital financial services users for a minimum of one year. Participants were selected based on purposive sampling, while data analysis was performed using Structural Equation Modeling (SEM) based on Partial Least Squares (SmartPLS) supported by SPSS, Tableau, and MonkeyLearn. The results of the analysis reveal that all dimensions of digital financial intelligence have a significant relationship with economic resilience, particularly in the tactile domains of crisis adaptation, recovery, and strategic decision-making. Overall, these results now show life documents the need for greater emphasis on literacy and digital readiness as the bedrock of MSME sustainability. This academic contribution is theoretically developing an integrated model of technology and finance while practically implementing the policy implications that would strengthen an inclusive and adaptive MSME digital ecosystem.

Accurate and structured performance assessment is an essential requirement for service-oriented organizations such as law firms. This study aims to develop a personnel performance measurement system at MFP Law Firm Jakarta using the Balanced Scorecard (BSC) approach. The BSC method was selected because it enables comprehensive performance evaluation from four perspectives: financial, customer, internal business processes, and learning and growth. The research was conducted by determining relevant performance indicators for each position within the law firm, namely Partner, Senior Lawyer, Junior Lawyer, Legal Staff, and Finance Staff. The assessment weights were set at 35% for the financial perspective, 25% for customer, 25% for internal business processes, and 15% for learning and growth. The results showed that the highest performance score was achieved by Personnel B (90.55), while the lowest was recorded by Pesonnel N (74.05). This system provides an objective overview of personnel performance achievements and can serve as a foundation for improving human resource management strategies within the law firm

Purpose: This research aims to address the methodological challenges posed by imbalanced data in classification tasks, where minority classes are severely underrepresented, often leading to biased model performance. It evaluates the effectiveness of hybrid sampling techniques specifically, the Synthetic Minority Oversampling Technique combined with Neighborhood Cleaning Rule (SMOTE-NCL) and with Edited Nearest Neighbors (SMOTE-ENN) in improving the predictive performance of ensemble classifiers, namely Double Random Forest (DRF) and Extremely Randomized Trees (ET), with a focus on enhancing minority class detection.
Methods: A total of eighteen simulated scenarios were developed by varying class imbalance ratios, sample sizes, and feature correlation levels. In addition, empirical data from the 2023 National Socioeconomic Survey (SUSENAS) in Riau Province were employed. The data were partitioned using stratified random sampling (80% training, 20% testing). Models were trained with and without hybrid sampling and optimized through grid search. Their performance was evaluated over 100 iterations using balanced accuracy, sensitivity, and G-mean. Feature importance was interpreted using Shapley Additive Explanations (SHAP).
Results: DRF combined with SMOTE-NCL consistently outperformed all other models, achieving 87.56% balanced accuracy, 82.17% sensitivity, and 86.75% G-mean in the most extreme simulation scenario. On the empirical dataset, the model achieved 76.37% balanced accuracy and 75.49% G-mean.
Novelty: This study introduces a novel integration of hybrid sampling techniques and ensemble learning within an interpretable machine learning framework, providing a robust solution for poverty classification in imbalanced datasets.

Breast cancer remains one of the leading causes of death among women worldwide, primarily due to late detection and diagnosis. Early and accurate prediction is essential to improve survival rates. Machine learning (ML) techniques have proven effective in supporting early diagnosis. This study aims to evaluate and compare the performance of three different approaches: traditional ML, ensemble ML, and deep learning (DL) for early prediction of breast cancer using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The methodology includes data collection, preprocessing, and the design of predictive models. Traditional ML algorithms used include Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Decision Tree (DT). Ensemble ML techniques comprise Random Forest (RF), XGBoost, and AdaBoost, while DL models include Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). The models were evaluated using precision, recall, F1-score, and accuracy. The results indicate that XGBoost achieved the highest accuracy (0.99), with strong recall (0.98) and F1-score (0.986), outperforming all other ensemble and traditional ML methods. CNN achieved 0.99 in all evaluation metrics, slightly outperforming RNN, which attained 0.98 accuracy and 0.985 F1-score. These findings confirm that ensemble ML techniques outperform traditional models, while CNN leads among DL models. Furthermore, the proposed models demonstrated superior prediction performance compared to existing studies, particularly in minimizing false negatives, which is critical for healthcare applications.

In Aceh, the provision of guidance is regulated in Articles 5 and 6 of Gubenur Regulation Number 139 of 2016, which states that the main task and function of the Civil Service Police Unit and Wilayatul Hisbah Aceh is the guidance function. Article 1 point 14 of Qanun Number 7 Year 2013 on Jinayat Procedure Law states that the function of Wilayatul Hisbah (WH) is to socialise, supervise, enforce, and foster the implementation of Islamic Sharia. Therefore, there is a problem of interpreting the authority of coaching carried out by Satpol PP and WH of Lhokseumawe City. This study aims to determine the legal basis for the coaching of suspected jarimah violators by Satpol PP and WH of Lhokseumawe City and the legal consequences of coaching suspected jarimah violators without going through the judicial process carried out by Satpol PP and WH of Lhokseumawe City. The research method used is empirical juridical. Data sources are obtained through interviews with respondents and informants. The results of this study indicate that the coaching carried out by Satpol PP and WH of Lhokseumawe city against suspected jarimah violators does not have a clear legal basis. The coaching carried out is only based on a statement signed by the alleged jarimah violator during the investigation and the legal consequences of coaching against jarimah offenders by Satpol PP and WH of Lhoseumawe City without going through the judicial process is carried out without a legal basis which results in serious legal consequences such as the coaching is not legally valid (can be cancelled), violates human rights, and can be sued civilly or criminally prosecuted and reduces public confidence in Sharia law.

Folklore is an oral cultural heritage that contains moral values and local wisdom that are important to be transmitted to the younger generation. This study aims to identify and describe the moral values in the folklore "Sultan Hadlirin and At-Taqwa Mosque Loram Kulon Kudus" contained in the book Cerita Rakyat Pesisir Timur by Mohammad Kanzunnudin. The method used is descriptive qualitative with data collection techniques in the form of observing and taking notes and data analysis using the Miles and Huberman model (data reduction, data presentation, and drawing conclusions). The results of the study indicate that this story contains a number of main moral values, namely responsibility, religiosity, social concern, and tolerance. This finding confirms that folklore is not only a medium of entertainment and cultural preservation, but also a means of character education that is relevant to answer the moral challenges of today's young generation. Therefore, the integration of folklore in moral education needs to be encouraged in order to strengthen cultural identity and form individuals with noble character.

This study aims to improve students' learning outcomes in the subject of Civic Education (PKn), particularly on the topic of Human Rights (HAM), which has often posed challenges at the elementary school level due to students’ low comprehension. The Contextual Teaching and Learning (CTL) model was implemented to connect learning materials with students’ real-life experiences. This research employed a Classroom Action Research (CAR) method using the Kemmis and McTaggart model, which includes four stages: planning, action, observation, and reflection, conducted over two cycles. Data were collected through observation, learning outcome tests, and documentation. The data were analyzed using descriptive quantitative analysis to evaluate the improvement in learning outcomes based on the number of students meeting the Minimum Mastery Criteria (KKM). The results showed a significant increase, with 12 out of 17 students (70.59%) achieving the KKM in the first cycle, and 16 students (94.12%) in the second cycle. These findings indicate that the CTL model effectively enhances students’ learning outcomes by helping them relate the concept of human rights to their daily lives. CTL can serve as an innovative alternative teaching strategy for Civic Education at the elementary level. The researcher recommends the continuous application of the CTL model and further development of varied contextual learning materials to achieve more optimal outcomes.