Abstract:The increasing volume of patient visits in primary healthcare facilities (such as community health centers and clinics) demands efficient resource management and scheduling systems. The inability to predict patient arrival…
al patterns often leads to long queues and suboptimal allocation of medical personnel. To overcome these issues, this study proposes the development of an intelligent framework based on Stacking Ensemble Learning to accurately classify and predict patient revisit frequency patterns. The proposed architecture integrates three heterogeneous base classification models (base learners), namely Logistic Regression (LR), Support Vector Machine (SVM) with non-linear Radial Basis Function (RBF) kernel projection, and Random Forest (RF). The probability predictions from these three base models are then combined into a secondary Logistic Regression based meta-learner to optimize the final decision and reduce the predictive bias of single models. Experiments were conducted using a dataset sourced from actual primary healthcare medical records, totaling 499 data entries. Given the highly imbalanced distribution of demographic data, the preprocessing stages were comprehensively designed, encompassing numerical scale feature standardization and the application of the SMOTE (Synthetic Minority Oversampling Technique) class balancing algorithm. Model testing was performed using a 5-fold stratified cross-validation scheme to ensure the robustness of the model's generalization. Comprehensive evaluation results proved that the Stacking Ensemble architecture achieved peak performance with an Accuracy of 88.4%, Precision of 85%, Recall of 90%, and an F1-Score of 0.88. Furthermore, curve analysis revealed an Area Under the Receiver Operating Characteristic (AUC-ROC) score of 0.961 and a Precision-Recall AUC of 0.95, consistently outperforming the capabilities of each individual baseline model. These findings confirm that combining hybrid machine learning models can produce highly stable and reliable predictions while minimizing false negative errors. This architecture holds massive potential for practical implementation as the foundation of a decision support system in managing operational services within primary healthcare facilities.
Abstract:Student mental health disorders have become an increasingly important issue due to their significant impact on academic performance and psychological well-being. Machine learning-based approaches offer a promising alternative…
ative for supporting early detection; however, only a limited number of studies have conducted comprehensive evaluations of various ensemble learning algorithms by simultaneously considering predictive performance, computational efficiency, and statistical significance. This study aims to compare ten classification algorithms, namely Random Forest, Extra Trees, Gradient Boosting, Hist Gradient Boosting, AdaBoost, XGBoost, LightGBM, CatBoost, Logistic Regression, and Stacking Ensemble, using 5-fold Stratified Cross-Validation. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, Receiver Operating Characteristic–Area Under the Curve (ROC-AUC), training time, inference time, confusion matrix, feature importance, as well as One-Way Analysis of Variance (ANOVA) and post-hoc Tukey’s Honestly Significant Difference (Tukey HSD) tests. The experimental results show that CatBoost achieved the highest classification performance, with an Accuracy of 84.86%, an F1-Score of 87.19%, a Recall of 88.03%, and an ROC-AUC of 0.9220. XGBoost delivered comparable predictive performance with a substantially shorter training time (3.92 seconds), while LightGBM was the most computationally efficient algorithm, requiring an average training time of only 0.85 seconds. In contrast, the Stacking Ensemble required the longest training time (215.41 seconds) without providing a statistically meaningful performance improvement over the boosting-based models. The ANOVA results revealed significant differences in ROC-AUC among the evaluated models (p < 0.001), whereas the Tukey HSD test indicated that most boosting algorithms did not differ significantly from one another. These findings provide practical guidance for selecting classification algorithms by balancing predictive performance and computational efficiency to support early mental health screening systems for university students.
Abstract:Transformasi sistem kesehatan membutuhkan pendekatan yang tidak hanya berorientasi pada pelayanan kuratif, tetapi juga pada pencegahan, ketahanan gizi, dan pemanfaatan data secara presisi. Kajian ini bertujuan menganalisis…
is integrasi ketahanan gizi berbasis Precision Public Health dalam memperkuat resiliensi sistem kesehatan. Dengan pendekatan mixed methods sequential explanatory, kajian mengombinasikan analisis kuantitatif dan kualitatif untuk menilai hubungan antara integrasi data gizi, stratifikasi risiko, tata kelola kesehatan, dan kapasitas adaptif sistem. Hasil kajian menunjukkan bahwa ketahanan gizi perlu diposisikan sebagai determinan strategis dalam transformasi sistem kesehatan, bukan sekadar luaran kebijakan pangan. Integrasi data gizi dengan data sosial, lingkungan, epidemiologi, dan spasial memungkinkan intervensi yang lebih tepat sasaran, pengembangan sistem peringatan dini, serta peningkatan kapasitas sistem menghadapi krisis global. Temuan ini memperluas kerangka Precision Public Health dan Health System Resilience dengan menempatkan ketahanan gizi sebagai penghubung antara sistem pangan, data, pencegahan penyakit, dan tata kelola kesehatan. Implikasinya adalah perlunya interoperabilitas data lintas sektor dan kebijakan kesehatan yang lebih prediktif, adaptif, berkeadilan, dan berkelanjutan.
The transformation of the health system requires an approach that focuses not only on curative care, but also on prevention, nutritional resilience, and the precise use of data. This study aims to analyse the integration of nutritional resilience based on Precision Public Health in strengthening the resilience of the health system. Using a sequential explanatory mixed-methods approach, the study combines quantitative and qualitative analyses to assess the relationship between the integration of nutritional data, risk stratification, health governance, and the system’s adaptive capacity. The study’s findings indicate that nutritional resilience must be positioned as a strategic determinant in health system transformation, rather than merely an outcome of food policy. The integration of nutritional data with social, environmental, epidemiological, and spatial data enables more targeted interventions, the development of early warning systems, and an enhancement of the system’s capacity to cope with global crises. These findings expand the frameworks of Precision Public Health and Health System Resilience by positioning nutritional resilience as a link between food systems, data, disease prevention, and health governance. The implications are the need for cross-sectoral data interoperability and health policies that are more predictive, adaptive, equitable, and sustainable.
Abstract:Digital transformation has brought significant changes in the corporate risk management landscape. The application of Big Data and predictive analytics provides new opportunities for companies to identify, monitor and control…
ntrol risks more effectively and efficiently. This research aims to examine the implementation strategy of Big Data and predictive analytics in corporate risk management through the literature review method. The results show that the integration of these technologies can improve early detection of risks, accelerate data-based decision-making, and automate the risk mitigation process. However, successful implementation is strongly influenced by the readiness of technological infrastructure, human resource competence, data governance, and management commitment. With the right strategy, companies can build an adaptive, responsive and highly competitive risk management system in the digital era.
Abstract:Every company must adapt to the digital era, and a crucial strategy for achieving this is preparing for change. Employees who are ready for change tend to operate more effectively. This research aims to determine whether…
self-efficacy predicts readiness for change among digital enterprise employees. The study used a quantitative approach with a survey design. The sample comprised 106 digital enterprise employees. Data were collected using a 25-item Readiness to Change Questionnaire and a 10-item General Self-Efficacy Scale (GSES) instrument. The validity test utilized content and construct validity, ensuring the instruments accurately measure the intended constructs. The reliability test, using Cronbach's Alpha, ranged from 0.592 to 0.955, indicating varying levels of internal consistency, while the reliability of the measurement model (inner model) ranged from 0.773 to 0.961, suggesting high reliability. The results of the hypothesis testing show that (1) self-efficacy is a significant predictor of readiness for change, with a predictive relevance of 30%; (2) self-efficacy significantly influences the appropriateness dimension, with an effective contribution of 47.1%; (3) self-efficacy is a significant predictor of the management support dimension, with an effective contribution of 46%; (4) self-efficacy significantly predicts the change efficacy dimension, with an effective contribution of 47.9%; (5) however, self-efficacy is not a significant predictor of personal benefits. These findings underscore the importance of self-efficacy in enhancing employees' readiness for change within digital enterprises. Companies should focus on boosting employees' self-efficacy to improve their adaptability and effectiveness in the face of digital transformation. By recognizing and fostering the key dimensions influenced by self-efficacy, organizations can better support their workforce during periods of change. Overall, this research contributes to the understanding of psychological factors that facilitate successful change management in the digital era.
Abstract:This study sets out to determine the impact of resilience in the psychosocial support of internally displaced secondary school students in Cameroon. The researcher was motivated by the crises that is affecting two regions…
s in the country which has exposed children to significant risks of diverse forms such as sexual violence, seizure of daily activities, dropping out of school, destruction of infrastructure, association to armed groups, family separation all resulting to extended psychosocial distress and mental disorders especially among some children.The mixed method approach was adopted by the researcher. In the quantitative phase, a sample of 317 participants was drawn from some public schools in the selected regions. The qualitative data was collected from the field with the aid of an Interview guide. Data were analysed used analysing using both descriptive and inferential statistics and the interview guide was analysed using the process of thematic content analysis. The results reveal that the impact of resilience in the psychosocial support of internally displaced secondary students in Cameroon was significant for all the predictive components thus indicating their significance in providing psychosocial assistance among internally displaced secondary school students
Abstract:This study examines the influence of Maqashid Sharia dimensions on halal cosmetic usage intention among married Muslim women and investigates the moderating role of price affordability. A quantitative approach was employed…
ed using a survey method involving 157 married Muslim women residing in the Greater Jakarta area (Jabodetabek), Indonesia. Respondents were selected through purposive sampling, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The findings reveal that among the five Maqashid Sharia dimensions examined, only hifz al-mal (protection of wealth) and hifz al-nasl (protection of lineage) significantly and positively influence halal cosmetic usage intention. In contrast, hifz al-din (protection of religion), hifz al-nafs (protection of life), and hifz al-aql (protection of intellect) do not show significant effects. Price affordability demonstrates a significant direct positive effect on usage intention; however, it does not moderate the relationships between any of the Maqashid Sharia dimensions and usage intention. The model explains 65.5% of the variance in halal cosmetic usage intention (R² = 0.655) with a predictive relevance value of Q² = 0.469. These findings suggest that economic rationality and family orientation serve as the primary drivers of halal cosmetic consumption among married Muslim women, rather than individual religiosity or health awareness. This study contributes to the literature by applying the Maqashid Sharia framework to consumer behavior research and provides practical implications for halal cosmetic industry stakeholders.
Abstract:This study addresses the limitations of traditional Customer Relationship Management (CRM) systems by analyzing the adoption and impact of Artificial Intelligence (AI) integration (AI-Powered CRM). Informed by the Technology…
logy Acceptance Model (TAM) for employee perception and the Resource-Based View (RBV) for strategic capability, the primary objective is to evaluate how AI-driven automation enhances customer service processes and, subsequently, impacts marketing efficiency. The research employs an exploratory qualitative case study design, utilizing in-depth interviews, document analysis, and system observation on a single organization to gather rich, contextual data. The results demonstrate that AI integration significantly accelerated service, with chatbots handling 65–70% of routine queries and drastically reducing response times. Operationally, these improvements fostered high employee acceptance (TAM). Strategically, the AI-Powered CRM generated refined predictive analytics, resulting in a 12–18% improvement in campaign conversion rates and efficient resource allocation, confirming that AI creates a valuable and difficult-to-imitate strategic capability (RBV). The study concludes that AI-Powered CRM is a critical enabler for both operational efficiency and long-term strategic competitiveness in digital markets.
Abstract:In recent years, the housing market has faced significant challenges, including fluctuating prices and declining sales. To solve this issue, there was an increasing need for more sophisticated methods to predict housing…
prices accurately. This study aimed to provide real estate marketers with a tool to enhance their pricing tactics and mitigate the decline in home sales by predicting house prices using machine learning techniques. Several parameters were considered in this study, such as location, number of bedrooms, number of bathrooms, land area, building area, and number of carports. Linear regression and neural network methods were used to develop predictive models. The findings showed that the neural network method was more accurate than linear regression, which made it a better tool for real estate pricing strategies, with land area and number of carports being the most influential aspects in house price prediction.
Abstract:This study aims to analyze the effect of Procedural Justice on Organizational Citizenship Behaviour (OCB) through Perceived Organizational Support (POS) among hotel employees in Makassar City. The research is motivated by…
y the declining extra-role behaviors of employees, which affect service quality, allegedly driven by perceived procedural injustice and low organizational support. Using a quantitative approach and Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected through questionnaires from employees of three- to five-star hotels. The results reveal that Procedural Justice has a positive and significant effect on both POS and OCB, directly and indirectly. Moreover, POS functions as both a mediator and a moderator, strengthening the relationship between procedural justice and OCB. The R² and Q² values indicate a high explanatory and predictive relevance, while the Goodness of Fit Index confirms the empirical adequacy of the model. These findings support the Social Exchange Theory (Blau, 1964) and Organizational Support Theory (Eisenberger et al., 1986), asserting that fairness and organizational support foster reciprocal relationships that motivate employees to engage in voluntary, extra-role behaviors. This study contributes theoretically by extending the application of social exchange theory within the hospitality context and provides practical implications for hotel management to enhance procedural justice and organizational support as strategies to improve employees’ organizational citizenship behaviour.