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:This study uses Environmental, Social, and Governance (ESG) as a moderating variable to examine how firm size and leverage affect business performance. This study reports ESG scores for the study period using panel data…
a from companies listed on the Indonesia Stock Exchange (IDX). Panel data regression using the Random Effect Model (REM) approach, which is based on the Hausman test results and employs robust standard error (PCSE) to address heteroscedasticity and multicollinearity issues, is the analysis method employed. The findings demonstrate that: (1) leverage significantly affects business performance, (2) firm size significantly affects business performance, (3) ESG positively moderates the relationship between leverage and business performance, as shown by a coefficient of 0.011 and a probability of 0.04, and (4) ESG negatively moderates the relationship between business size and business performance, as shown by a coefficient of -0.56 and a probability of 0.035. In addition, the model's F-statistic score of 24.49 (probability 0.0000) indicates that it is significant.
Abstract:Financial performance is one of the important aspects for a company because it is a guideline for describing the financial condition. Factors that can affect financial performance are Intellectual capital and company size.…
e. Good intellectual capital will create added value and improve the company's financial performance, likewise company size is also important in the financial reporting process, because large companies basically have greater financial strength in supporting performance. This study aims to determine the effect of intellectual capital on financial performance and the effect of company size on financial performance in banking companies listed on the IDX for the period 2020 - 2023. This study uses descriptive research with a quantitative approach. The data collection technique used in this study is secondary data. The population in this study was 47 Banking Companies. The sampling technique used was purposive sampling so that the sample in this study was 37 companies, the observation period was 4 years, so the amount of data used in this study was 148. Data analysis using classical assumption tests, multiple linear regression analysis, coefficient of determination and hypothesis testing using partial tests with t-tests. The results of the study show that there is a significant influence of intellectual capital on financial performance and there is no significant influence of company size on financial performance.
Abstract:Raising school-aged children is one of the challenging tasks for mothers, so if a mother is unable to cope with these challenges, she is vulnerable to experiencing parenting stress. This is also felt by housewives in SD…
X Cianjur City who accompanied their children aged 6-8 years old. In addition to having to accompany children to optimally study, because this age is a stage of transition from learning by playing to a more academic learning period, mothers also have regular domestic tasks that have to be done. These tasks cause mothers to be careless and prone to stress. The purpose of this study is to obtain empirical data on the causality of mindful parenting on parenting stress in distance learning. This study had 78 participants, and the measuring instrument used to measure mindful parenting was Mindfulness in Parenting Scale (MIPQ) with 0.937 reliability. As for measuring parenting stress, the researchers used the Parental Stress Scale (PSS) with 0.965 reliability. The researcher used a simple linear regression test statistical technique with a regression coefficient of -.628 and a determination coefficient of 68.2%. These results show that if the mother has low mindful parenting behavior, it will cause high parenting stress.
Abstract:Performance is an achievement that can be achieved by an organization within a certain period of time. Regional financial performance or regional capacity is one measure that can be used to see the ability of a region to…
run the regional economy. This study aims to determine and analyze the effect of regional original income and capital expenditure on regional financial performance, as well as to determine which variables have the most dominant influence on regional financial performance in districts/cities in Central Sulawesi province. The data processed is secondary data from the annual reports of districts/cities in Central Sulawesi province for 2019-2023 which were taken with total sampling. The method used in this study is the multiple linear regression analysis method using the SPSS 25 application. The results of this study indicate that: regional original income does not have a significant effect on regional financial performance and capital expenditure has a negative and significant effect on regional financial performance.
Abstract:This study analyzes the determinants of food insufficiency prevalence across 34 provinces in Indonesia using a quantile regression approach. The research focuses on three key variables: the Food Security Index (IKP), rice…
e productivity (PVP), and per capita food expenditure (PPM), using 2023 cross-sectional data from Statistics Indonesia (BPS). Results from ordinary least squares (OLS) and quantile regression at the 25th, 50th, and 75th percentiles reveal that the Food Security Index has a consistent and significant negative effect on food insufficiency across all quantiles, with the strongest influence observed at the higher quantile (τ = 0.75). In contrast, rice productivity is not statistically significant at any quantile, while per capita food expenditure is only marginally significant at the 25th quantile. These findings suggest that improvements in food security contribute significantly to reducing food inadequacy, especially in regions with high prevalence rates. However, the impact of rice productivity and food spending varies across different segments, implying that context-specific policies are needed. The study recommends targeted government interventions to enhance food access, affordability, and regional food security equity.
Abstract:The poverty gap index is an important indicator in planning more effective and targeted policies, but it has rarely been studied. This study analyzes the distribution pattern of poverty gap and the influence of non-food…
per capita expenditure, open unemployment rate, and Gini ratio on poverty gap in districts/municipalities in Sumatra Island in 2023. Using cross-section data from 154 districts/municipalities, the analysis was conducted through the calculation of the global Moran index and spatial autoregressive (SAR) regression. The results show that the gap of poverty in Sumatra Island has a clustered spatial pattern. Regions with moderate to high poverty gap are concentrated in the northern, southern, and western islands, especially in Aceh, Bengkulu, South Sumatra, Lampung, and Nias, Mentawai, and Meranti Islands. In contrast, the central part of Sumatra tends to have lower poverty gap. From the regression analysis, non-food per capita expenditure has a negative effect and Gini ratio has a positive effect on poverty gap. These findings emphasize the importance of considering spatial factors in the formulation of poverty alleviation policies in Sumatra.
Abstract:This study aims to investigate the influence of leadership and competence on the effectiveness of village fund management in Walea Kepulauan Subdistrict, Tojo Una-Una District. The specific objectives are: (1) to examine…
the effect of village head leadership on the effectiveness of village funds, (2) to explore the impact of village apparatus competence on the effectiveness of village funds, and (3) to analyze the simultaneous influence of both village head leadership and apparatus competence on the effectiveness of village funds. The research utilizes a survey approach with a quantitative method. A total of 117 respondents, including village government officials and members of the Village Consultative Body (BPD), were selected from Walea Kepulauan Subdistrict. Data analysis was conducted using multiple regression analysis with the assistance of SPSS software. The results show that: (1) village head leadership has a negative and significant effect on the effectiveness of village funds, contributing 11.9%, (2) village apparatus competence has a positive and significant effect on the effectiveness of village funds, contributing 45.6%, and (3) both village head leadership and apparatus competence together have a positive and significant effect on the effectiveness of village funds, contributing 51.2%. These findings suggest that enhancing leadership quality and the competence of village apparatuses is crucial for improving the management and effectiveness of village funds in Walea Kepulauan Subdistrict.
Abstract:This study investigates the influence of attention-seeking behavior and loneliness on problematic social media use among early adults who are active social media users in the Jabodetabek area. A total of 201 participants…
were selected through self-selected surveys. The instruments used include the Bergen Social Media Addiction Scale (BSMAS) for measuring problematic social media use, the Attention-Seeking Scale for assessing attention-seeking behavior, and the UCLA Loneliness Scale version 3 for evaluating loneliness. Multiple linear regression analysis revealed that attention-seeking behavior and loneliness have a significant impact on problematic social media use, with an R² of 0.661, indicating a 66.1% contribution. The data analysis showed that attention-seeking behavior (p-value < 0.001, t = 8.185) and loneliness (p-value < 0.001, t = 6.086) both significantly influence problematic social media use. In conclusion, both attention-seeking behavior and loneliness significantly contribute to problematic social media use among early adults in Jabodetabek.