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This study seeks to investigate the direct impact of digital transformational leadership on employee engagement and to analyze the mediating function of work-life balance within higher education institutions in Medan. A quantitative methodology was utilized, employing a survey technique with a structured questionnaire disseminated to professors at higher education institutions in North Sumatra Province. The study's population and sample consisted of 120 permanent academics in North Sumatra Province. We utilized a questionnaire to collect data and Structural Equation Modeling (SEM) to analyze it. The findings indicated that digital transformative leadership exerted a favorable and significant impact on lecturer engagement. Nonetheless, work-life balance was not demonstrated to be a significant mediator or moderator in this relationship. This study demonstrates that within the framework of digital transformation, professor engagement is more significantly affected by leadership practices than by perceptions of work-life balance. Digital leadership that works can give teachers more power and make them more interested, but problems that come with the digital invasion need to be handled carefully. This study suggests that companies, particularly higher education institutions, should prioritize the enhancement of digital leadership capacity to sustain and elevate employee engagement in the digital age.

Management of hazardous and toxic waste (B3) in hospitals is an important aspect of maintaining environmental health and preventing pollution risks. RSUD Jenderal Ahmad Yani Metro has implemented hazardous waste management using the reduce, reuse, recycle (3R) concept; however, its implementation has never been comprehensively evaluated. This study aimed to evaluate the management of hazardous waste using the 3R concept at RSUD Jenderal Ahmad Yani Metro based on the Regulation of the Minister of Environment and Forestry No. P.56/Menlhk-Setjen/2015 and to identify the supporting factors. This research used a qualitative design with a case study approach. The research informants consisted of 23 participants selected using purposive sampling, including the hospital director, ward heads, IPCN, sanitation officers, 3R waste management officers, and sanitation operators. Data were collected through observation, in-depth interviews, and document review using interview guidelines and checklists. Data analysis was conducted using the Miles and Huberman model, including data reduction, data display, and conclusion drawing. The results showed that the transportation, storage, and processing stages of hazardous waste management were in accordance with the Regulation of the Minister of Environment and Forestry No. P.56/Menlhk-Setjen/2015. However, the sorting and containerization processes were not fully compliant because recyclable waste was still mixed with other hazardous waste. In addition, several supporting factors were identified, including the lack of training and certification among human resources, inadequate infrastructure, and the absence of technical guidelines and standard operating procedures for the 3R waste bank. Therefore, improving human resource capacity, infrastructure, and internal hospital regulations is necessary to optimize 3R-based hazardous waste management.

Peningkatan kasus gangguan kardiovaskular menuntut adanya sistem pemantauan detak jantung yang tidak hanya akurat, tetapi juga mampu memberikan respons cepat terhadap kondisi abnormal. Penelitian ini bertujuan mengembangkan sistem pemantauan detak jantung berbasis Internet of Things (IoT) yang terintegrasi dengan algoritma Extreme Gradient Boosting (XGBoost) untuk mendeteksi pola detak jantung abnormal secara real-time serta memicu respons otomatis melalui Miniature Medical Car. Metode penelitian yang digunakan adalah pendekatan kuantitatif dengan metode simulasi, di mana data denyut jantung dikumpulkan menggunakan Pulse Armband berbasis ESP32-C3 Mini dan sensor MAX30102, kemudian dikirim ke server untuk diproses melalui pipeline data mining. Dataset penelitian terdiri dari 800 sampel yang dibagi menjadi kelas normal dan abnormal, kemudian diolah menjadi 14 fitur statistik dan variabilitas detak jantung untuk proses pelatihan model. Hasil penelitian menunjukkan bahwa model XGBoost mencapai akurasi 94%, precision 93,3%, recall 93,3%, F1-score 0,933, serta ROC-AUC 0,96, yang mengindikasikan kinerja tinggi dalam membedakan pola detak jantung normal dan abnormal. Integrasi model dengan miniature car memungkinkan sistem memberikan respons fisik secara otomatis saat mendeteksi kondisi berisiko. Kontribusi penelitian ini mencakup pengembangan sistem IoT–ML real-time yang efisien, penggunaan fitur statistik ringkas untuk data fisiologis, serta implementasi respons berbasis robotik. Implikasi penelitian membuka peluang penerapan pada sistem monitoring medis jarak jauh, lingkungan publik, maupun perangkat asistif berbiaya rendah.

The Fréchet distribution is one of the commonly used Extreme Value Distributions (EVDs) in statistical modeling and heavy-tailed data analysis, where it plays an important role in describing product lifetimes as well as climatic and financial phenomena. The estimation of its two parameters, namely the shape parameter and the scale parameter, is traditionally based on the Maximum Likelihood Estimation (MLE) method. However, maximizing the likelihood function for this distribution involves numerical difficulties, which necessitates the use of numerical optimization methods. In this study, we propose the use of the Aquila Optimizer (AO), a recent metaheuristic algorithm inspired by the hunting behavior of eagles, as an efficient numerical tool for maximizing the likelihood function of the Fréchet distribution. The objective function was formulated as the negative log-likelihood function (-LogL), and the Aquila Optimizer was employed to obtain the optimal estimates of the distribution parameters. Several simulation experiments with different sample sizes were conducted to compare the performance of the proposed method with a conventional approach represented by the Nelder–Mead method, using the Mean Squared Error (MSE) criterion. The simulation results demonstrated that the Aquila Optimizer outperformed the Nelder–Mead algorithm in many cases, although the superiority was slight. The results also showed that both algorithms were consistent, as their MSE values decreased with increasing sample size. In addition, a practical application was carried out using real data, and the results of the survival function estimation indicated a good fit.

The retention of customers in the retail banking sector is a critical economic imperative; however, predictive modeling is frequently hindered by severe class imbalance and the “Black Box” nature of complex algorithms. This study proposes a Heterogeneous Stacking Ensemble framework integrating XGBoost, CatBoost, and Random Forest base learners with a Logistic Regression meta-learner to forecast customer attrition. To overcome the pervasive “Majority Class Bias,” we introduce a “Dual-Imbalance Defense” that synergizes the Synthetic Minority Over-sampling Technique (SMOTE) with algorithmic cost-sensitive penalization. Furthermore, moving beyond standard accuracy metrics, the framework mathematically derives a dynamic classification threshold to guarantee a strict 0.90 recall rate, actively optimizing the capture of at-risk capital. Model opacity is addressed through the integration of a SHapley Additive exPlanations (SHAP) TreeExplainer. This cooperative game theory approach provides localized, patient-level “Reason Codes” for regulatory compliance and reveals global systemic vulnerabilities, including non-linear drivers such as the “Product Paradox.” Achieving a 0.90 recall rate and an AUC of 0.8654, this framework provides a statistically robust and operationally transparent tool for targeted customer retention.

Micro, Small, and Medium Enterprises (MSMEs) constitute the backbone of Indonesia's national economy, contributing approximately 61% of GDP and absorbing 97% of the total workforce; however, the majority of MSME actors, particularly in eastern Indonesia, continue to face structural barriers in digital technology adoption and capital access that constrain optimal business performance. This study aims to analyze the partial and simultaneous effects of marketing digitalization and business capital on MSME performance in Ambon City. A quantitative approach with associative-causal design was employed, involving 30 respondents selected through purposive sampling from active MSME operators in Ambon City. Data were collected via a five-point Likert scale questionnaire and analyzed using multiple linear regression with IBM SPSS version 26, preceded by validity, reliability, and classical assumption tests. Results demonstrate that marketing digitalization exerts a positive and significant partial effect on MSME performance (t = 8.060; sig. = 0.000; β = 1.061), establishing it as the most dominant predictor in the model. Conversely, business capital shows no significant partial effect (t = 0.746; sig. = 0.462), attributable to the homogeneity of capital access among MSME actors in archipelagic regions. Simultaneously, both variables significantly influence MSME performance (F = 287.070; sig. = 0.000), with an exceptionally high R Square of 0.955, indicating that 95.5% of performance variance is collectively explained by the two predictors. These findings underscore the critical role of digital marketing capabilities over financial resources alone in archipelagic contexts. It is recommended that the Ambon City Government integrate digital marketing literacy training programs synergistically with inclusive financing schemes to comprehensively strengthen MSME competitiveness across the Maluku archipelago.

This study aims to analyze the employee turnover rate experienced by CV Premium Indonesia employees through the influence of workload and job stress. As a company engaged in the retail and distribution of mobile phone accessories from leading brands, the desire to leave the company often arises due to high workload and feelings of work stress because of the many demands within the company. The methodology used in this study is quantitative, employing SEM model data analysis with the help of SmartPLS software. The sampling technique used is saturated sampling, with a total sample of 127 respondents. The results of this study indicate that high levels of workload can increase employee turnover rates. Meanwhile, high levels of job stress experienced by employees can also increase employee turnover rates. These findings suggest that CV Premium Indonesia should address the issues of workload and job stress in order to retain employees. Effective strategies such as work-life balance, stress management programs, and workload adjustments could help reduce employee turnover and improve overall organizational performance.

The rapid development of the digital ecosystem has encouraged Micro, Small, and Medium Enterprises (MSMEs) in Ambon City to adopt online platform-based marketing strategies in response to changing consumer behavior that is increasingly digitally connected. This research focuses on MSME consumers in Ambon City who actively interact with local business products through digital platforms. The main problems identified include low digital marketing adoption by local MSME actors, limited empirical studies on Eastern Indonesia context, and the absence of an integrative analytical model that simultaneously tests three dimensions of digital marketing. This research aims to analyze the influence of digital marketing strategies encompassing social media marketing, paid digital advertising, and content marketing on consumer purchasing decisions of MSMEs in Ambon City. This study employs a quantitative approach with a survey design involving 100 respondents selected through purposive sampling, and data were analyzed using multiple regression analysis after passing classical assumption tests covering normality, multicollinearity, and heteroscedasticity. Results show that social media marketing has a significant effect with a regression coefficient of 0.387, paid digital advertising with a coefficient of 0.312, and content marketing with a coefficient of 0.274, all significant at the 0.05 level. Simultaneously, the three variables explain 67.1% of variation in consumer purchasing decisions with an F-count of 65.847. Social media marketing is proven as the most dominant dimension shaping consumer purchasing decisions of MSMEs in Ambon City. This research concludes that an integrated and contextual digital marketing strategy is a crucial instrument in driving MSME growth in Eastern Indonesia and recommends strengthening digital capacity of local business actors as a priority policy for regional MSME empowerment.Keywords: digital marketing; purchasing decision; MSMEs; social media; Ambon City

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

The issue of uninhabitable houses still requires an accurate identification mechanism because the manual data collection process has the potential to be time-consuming, costly, and subject to subjectivity in determining aid priorities. This study aims to develop a classification model to identify habitable and uninhabitable houses based on family socioeconomic data using the Random Forest algorithm. The research method includes data preprocessing, data division using stratified split in three scenarios, baseline model development, and optimization through hyperparameter tuning using GridSearchCV with 3-fold cross-validation and balanced class_weight parameters. The data used includes variables such as education type, employment status, occupation type, number of family members, and family insurance type. The test results show that the 70:30 data division scenario after tuning provides the best performance with a recall value of 0.5797 for uninhabitable houses and an F1-score of 0.4746. Feature importance analysis shows that education type and employment status are the most influential variables in the classification. The results of this study show that the model built is capable of increasing sensitivity in detecting uninhabitable houses to support more objective field survey prioritization.

This study aims to analyze the persuasive communication carried out by Bank Sampah Mutiara Timor in educating waste management among the people of Kupang City and to identify the obstacles faced in the communication process.This research uses a qualitative approach with a case study method and is based on a constructivist paradigm. Data collection techniques include in-depth interviews, participatory observation, and documentation. The informants consist of the managers of Bank Sampah Mutiara Timor and community members who participate as waste bank customers. Data analysis is conducted using the Miles and Huberman model, which includes data reduction, data presentation, and conclusion drawing and verification. The theoretical framework applied in this study is Joseph A. DeVito’s persuasive communication theory.The results show that the persuasive communication of Bank Sampah Mutiara Timor is implemented through several strategies, including direct socialization, waste management education, storytelling approaches, and hands-on practices in waste sorting and management. The communication process is carried out in three stages: preparation, implementation,and evaluation. This persuasive communication has been able to increase public awareness and understanding of the importance of proper waste management, although community participation has not yet been fully optimal. The obstacles encountered include cognitive or perceptual barriers, technical limitations, and a lack of public awareness and consistency in sorting waste.This study concludes that persuasive communication plays an important role in community-based waste management education. Therefore, Bank Sampah Mutiara Timor is encouraged to continuously develop innovative and sustainable communication strategies to further enhance community participation in waste management efforts in Kupang City.

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.

This study aims to design and develop a web-based waste collection scheduling system using PHP and MySQL at the Environmental Agency of West Sumba Regency. The main problem faced is that the scheduling process is still carried out manually, resulting in inefficiency, susceptibility to errors, and difficulties in monitoring and reporting. The system development method used is the Waterfall model, which includes requirement analysis, system design, implementation, testing, and maintenance stages. The developed system provides features for managing data on personnel, regions, vehicles, and structured waste collection scheduling. In addition, the system is equipped with notification features, schedule monitoring, and performance reporting that can be accessed by management. The results of this study indicate that the system improves effectiveness and efficiency in scheduling processes and facilitates supervision of waste collection activities. Therefore, the implementation of this system is expected to optimize and organize waste management in West Sumba Regency.

This community service program aims to improve adolescents’ financial literacy through an educational initiative entitled “Smart Teens: Managing Money Without Drama”, conducted for the youth of Vihara Dhamma Bhakti Tangerang. The program is motivated by the relatively low level of financial literacy among adolescents, which may lead to consumptive behavior and a lack of personal financial management skills from an early age. The methods employed include interactive lectures, financial management simulations, and group discussions covering basic financial planning, saving habits, and expense control. The results indicate an improvement in participants’ understanding of fundamental financial concepts, such as managing allowances, the importance of saving, and the ability to distinguish between needs and wants. Furthermore, participants demonstrated more responsible attitudes in making financial decisions. This program is expected to serve as an effective community-based financial literacy education model in fostering healthy financial behavior among adolescents.

Digital transformation in healthcare services is a strategic approach to improve access, efficiency, and service quality, particularly within Indonesia’s National Health Insurance (JKN) system. BPJS Kesehatan has introduced an online queue feature through the Mobile JKN application to minimize manual queuing and reduce waiting times in outpatient services. However, despite the widespread ownership of the application, its actual utilization for online queuing remains relatively low, including in a regional public hospital (RSUD) in West Bandung. This condition reflects a gap between the availability of digital health technology and patients’ actual usage behavior, highlighting the need to examine factors influencing adoption.This study aims to analyze the effects of perceived ease of use, social influence, and facilitating conditions on the actual use of the Mobile JKN online queue, with behavioral intention as an intervening variable among outpatients. A quantitative cross-sectional design was applied, involving 255 JKN outpatient participants selected through purposive sampling. Data were collected using structured questionnaires and analyzed using Structural Equation Modeling (SEM) with AMOS version 24, based on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Data analysis included descriptive statistics, validity and reliability testing, normality assessment, goodness-of-fit evaluation, Three Box Method, and hypothesis testing.

This study aims to investigate the determinants of job performance by integrating the variables of capability, social interaction, and Organizational Citizenship Behavior (OCB) as a moderating variable. The phenomenon of employee turnover and discrepancies in staff discretionary behavior within the hospital setting serves as the primary rationale for this study. Using a quantitative approach, data were collected from 240 respondents at Magelang City Islamic Hospital through purposive sampling. Data analysis was conducted using Structural Equation Modeling (SEM) based on AMOS software to test the causal relationships among variables in the structural model. The research findings provide empirical confirmation that individual capabilities and the quality of social inter-actions have a positive and significant influence on stimulating the formation of OCB. Furthermore, statistical test results demonstrate that capabilities, social interactions, and OCB simultaneously make a significant contribution to improving employee work performance. The presence of OCB is identified as a crucial factor capable of strengthening organizational performance outcomes. The practical implications of this study emphasize the need for hospital management to formulate human resource development strategies focused on strengthening technical skills and social cohesion to mit-igate the impact of turnover and accelerate the sustainable performance of healthcare services.

The management of essential drug inventory is a strategic component of hospital pharmaceutical services because it directly influences service continuity, cost efficiency, and the overall quality of healthcare delivery. Poor inventory control can result in excessive stock accumulation, increased risk of drug expiration, inefficient budget utilization, and potential drug shortages that may compromise patient care. This study aims to evaluate the effectiveness of essential drug inventory control at Pekerja General Hospital by applying the ABC-VEN, Economic Order Quantity (EOQ), and Reorder Point (ROP) methods. It also examines differences in inventory management efficiency between 2024 and 2025 based on inventory value, cost of goods sold (COGS), and Inventory Turnover Ratio (ITOR). A mixed-methods approach with a sequential explanatory design was used. Quantitative analysis involved a paired sample t-test comparing inventory data from 2024–2025, while qualitative data were collected through in-depth interviews and analyzed thematically using NVivo. The findings reveal a significant improvement in inventory management in 2025 (p < 0.05), reflected in reduced inventory value and COGS, along with an increased ITOR. However, the implementation of ABC-VEN, EOQ, and ROP methods has not been fully integrated, and challenges such as limited human resources and procurement bureaucracy persist.In conclusion, although inventory control became more efficient in 2025, further integration of inventory methods and strengthening of human resource capacity are necessary to ensure sustainable improvements.

This study examines the predictive influence of religious habituation and teacher role modeling on students’ emotional intelligence in an Islamic elementary school context. A quantitative approach with an ex post facto design was employed, involving 78 students selected through total sampling. Data were collected using Likert-scale questionnaires and analyzed using multiple linear regression. The findings indicate that religious habituation has a positive and significant effect on students’ emotional intelligence, while teacher role modeling demonstrates a relatively stronger contribution. Simultaneously, both variables significantly predict emotional intelligence, explaining 74.9% of its variance. These results suggest that students’ socio-emotional development is shaped by the integration of structured religious practices and pedagogical modeling within the school environment. The study contributes to the ecological-religious perspective on character education by emphasizing the importance of consistent spiritual habituation and teachers’ personal competence in fostering emotional regulation and social skills. Practically, Islamic elementary schools are encouraged to strengthen religious culture programs and support teachers’ emotional and moral development to enhance students’ emotional intelligence.

Adolescent depression is an important mental health concern associated with psychological conditions and excessive digital media use. The extreme imbalance of depression labels in behavioral datasets can reduce the ability of classification models to recognize minority cases. This study aims to develop a TabNet-based deep learning model for classifying adolescent depression labels using social media addiction, stress, anxiety, and related behavioral features. The study used a secondary dataset consisting of 1,200 adolescent samples. Data preprocessing included categorical feature encoding, stratified training and testing data splitting, feature standardization, and the application of the Synthetic Minority Oversampling Technique (SMOTE) to the training data. The TabNet Classifier was trained using Cross-Entropy Loss and the Adam optimizer with a step-decay learning rate and early stopping mechanism. The experimental results showed an accuracy of 99.15%, precision of 98.32%, recall of 100%, F1-score of 99.15%, and ROC AUC of 1.0000, with optimal performance achieved at epoch 53. These findings indicate that TabNet can effectively learn psychological and digital behavioral patterns for adolescent depression label classification. The proposed approach provides a potential computational framework for data-driven mental health risk classification, although further validation using diverse empirical datasets is required.