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Non-communicable diseases (NCDs) are the leading cause of death globally, with type 2 diabetes being one of the most prevalent conditions. Nutritional management for clients with type 2 diabetes requires strict adherence to the correct schedule, type, and quantity of food. To improve medication adherence, it is essential to implement preventive strategies that promote patient engagement and self-management. One such strategy is the Health Belief Model (HBM), which encourages patients to adopt healthy behaviors based on their perceptions of risk, severity, benefits, and barriers. This study aims to analyze the relationship between the Health Belief Model and medication adherence among type 2 diabetic patients in the Galala Community Health Center (Puskesmas) work area. A quantitative cross-sectional study design was used, with a sample size of 108 participants selected through simple random sampling. The independent variables in this study were perceived susceptibility, perceived severity, perceived benefits, perceived barriers, and cues to action, while the dependent variable was medication adherence. The results revealed significant relationships between all the HBM constructs and medication adherence. Specifically, perceived susceptibility (p=0.000), perceived severity (p=0.000), perceived benefits (p=0.000), perceived barriers (p=0.000), and cues to action (p=0.000) all had strong associations with adherence to medication. The findings suggest that the Health Belief Model is a valuable framework for improving medication adherence among type 2 diabetes patients. Additionally, the use of health information technology is an effective strategy to increase patient awareness of the risks of non-adherence and the importance of regular medication intake. A combination of education, social support, technology, and an individualized approach can create a supportive environment that encourages patients to manage their treatment effectively.

This study aims to examine the influence of Perceived Ease and Perceived Usefulness of the Unified e-Bupot System and e-Form on Taxpayer Compliance. The population in this study consisted of corporate taxpayers who utilized the services of tax consultant Edwin Suwandhy. A random sampling technique was applied, resulting in 66 respondents being selected. This research follows a quantitative approach, using questionnaires as the data collection method. The data obtained from the samples were analyzed using a multiple linear regression model. The study’s findings reveal that the perceived ease of using the Unified e-Bupot system positively influences taxpayer compliance. The greater the understanding and familiarity corporate taxpayers have with the e-Bupot Unification system, the easier they find it to comply with income tax reporting regulations. On the other hand, the perceived usefulness of the e-Bupot Unification system does not have a significant impact on taxpayer compliance. This result suggests that the perceived benefits of the system are not sufficiently compelling to improve corporate taxpayer compliance. Similarly, the perception of ease and usefulness of e-Form also showed no significant effect on taxpayer compliance. This is likely because corporate taxpayers feel that the e-Form system is not easy to use, and the perceived advantages of the system are still considered insufficient to drive compliance. These findings emphasize the importance of improving both the ease of use and the perceived benefits of the e-Bupot and e-Form systems to enhance taxpayer compliance. Future policy improvements should focus on increasing user experience and addressing any gaps in the perceived utility of these tax reporting systems, especially for corporate taxpayers.

The COVID-19 pandemic has exposed fundamental vulnerabilities in global supply chain systems, such as over-reliance on single suppliers and a lack of operational visibility. This has highlighted the urgent need for a new approach to risk management—one that leverages smart technologies. Artificial Intelligence (AI) has emerged as a promising solution, thanks to its capabilities in predictive analytics and adaptive, data-driven decision-making in real time. This study aims to develop an AI-based predictive system framework to enhance the resilience of global supply chains in the face of post-pandemic disruptions. Using the Design Science Research (DSR) methodology, the research designs and evaluates a system that integrates algorithms such as LSTM, Random Forest, Natural Language Processing (NLP), and Reinforcement Learning. It also applies a federated learning approach to ensure data privacy among supply chain partners. The study analyzes over 12,000 data entries from diverse sources, including IoT devices, weather data, demand trends, and social media. The system's effectiveness is evaluated through a combination of quantitative methods (PLS-SEM analysis on 103 respondents) and qualitative methods (interviews with 12 industry executives). The findings show that AI-driven predictive analytics significantly improve supply chain resilience (β = 0.67; p < 0.001), with demand forecasting accuracy increasing by up to 40% and delivery times reduced by 30%. Conceptually, the study contributes by designing a resilient model that integrates real-time visibility, adaptability, and cross-organizational collaborative learning. Unlike traditional approaches focused solely on automation, this framework offers a more holistic solution, addressing key gaps in the literature. The implication is clear: AI is becoming a strategic asset in building sustainable, resilient supply chains amid ongoing global uncertainty.

This study aims to determine the influence of tourist arrivals, the number of tourist attractions, and the room occupancy rate on the regional original revenue (PAD) of regencies/municipalities in Bali Province from 2018 to 2023. The analysis is conducted using panel data regression with the Common Effects Model, Fixed Effects Model, and Random Effects Model approaches. Model selection is based on the Chow test, Hausman test, and Lagrange Multiplier test. The results show that tourist arrivals, the number of tourist attractions, and the room occupancy rate simultaneously affect the regional original revenue of regencies/municipalities in Bali Province. Partially, the variables of tourist arrivals, number of tourist attractions, and room occupancy rate have a positive and significant effect on the regional original revenue of regencies/municipalities in Bali Province.  

The threat of Distributed Denial of Service (DDoS) is increasing develop along with increasing use of the Internet of Things (IoT) and Software-Defined Networking (SDN) architecture . Although SDN provides convenience in management network , properties its centralized control make it prone to to flooding attacks that can paralyze controller performance . Detection method conventional , such as approach statistics and machine learning, still own limitations in matter accuracy , high false positive rate , and dependence on extracted features manually . To overcome problem said , research This propose a hybrid deep learning based DDoS detection and mitigation model that combines Convolutional Neural Network (CNN) to extraction feature spatial from RGB and Gated Recurrent Unit (GRU) images for understand temporal correlation between traffic data network . System tested through network test-bed Mininet based with Ryu/Floodlight controller, using simulation DDoS attacks (Hping3, LOIC) and normal traffic (video streaming, HTTP server). Traffic data cross recorded in PCAP format, processed become RGB image measuring 200×200 pixels, and labeled based on type traffic . Evaluation results with metric accuracy , precision, recall, F1-score, and MCC show that the CNN–GRU model has performance more superior compared to baseline approaches such as CNN-only, GRU-only, as well as classical ML methods such as SVM and Random Forest. In addition , the system capable apply mitigation adaptive through automatic flow rule creation on edge switches. Findings This confirm that effective deep learning- based spatial -temporal hybrid approach in increase detection early and response DDoS attacks on SDN networks adaptive and real-time.  

This study aims to identify, measure, and analyze the influence of learning independence on student learning outcomes with learning motivation as a mediator in public high schools in East Jakarta. This study involves 10th-grade students from 3 (three) public high schools in East Jakarta based on different categories, with a total of 273 respondents selected through the Simple Random Sampling technique. The approach used in this study is quantitative, and data analysis was conducted using the Structural Equation Modeling-Partial Least Square (SEM-PLS) method through SmartPLS version 4.0 software. The results of the hypothesis testing show that, 1) learning independence has a positive and significant effect on learning outcomes, 2) learning independence has a positive and significant effect on learning motivation, 3) learning motivation has a negative and significant effect on learning outcomes, and 4) learning independence has a negative and significant effect on learning outcomes through the indirect influence of learning motivation. This study shows the involvement of other more dominant factors as mediating variables in the relationship between learning independence and student learning outcomes.

This study aims to analyze the influence of population size, Human Development Index (HDI), and Open Unemployment Rate (OUR) on poverty levels in regencies/cities of Special Region of Yogyakarta during 2018-2023 period. The research employed quantitative design with panel data regression analysis approach across five regencies/cities in DIY. Secondary data were obtained from official publications of Statistics Indonesia Yogyakarta Province and analyzed using Fixed Effect Model and Random Effect Model with Stata 17 software. The research findings indicate that HDI has negative and significant effect on poverty level, suggesting that improvement in human resource quality effectively reduces poverty. OUR proved to have positive and significant effect on poverty, confirming that increased unemployment raises poverty levels. Conversely, population size shows no significant influence on poverty in DIY. These findings provide strategic implications for local government to prioritize investment in education and health sectors as well as job creation programs in poverty alleviation efforts. High inter-regional heterogeneity indicates the necessity for policy approaches tailored to specific characteristics of each regency/city in DIY.

This study analyzes the effect of population and Human Development Index (HDI) on poverty in Jambi Province in the period 2018–2024. Using panel data from 11 districts/cities and regression methods with the best approach Random Effect Model (REM), the results show that poverty in Jambi tends to fluctuate. Tanjung Jabung Timur has the highest poverty rate, while Sungai Penuh City has the lowest. This study provides empirical understanding to support the formulation of more targeted poverty alleviation policies at the regional level.

Coronary heart disease (CHD) remains a leading cause of mortality worldwide. Early detection is essential to reduce complications and improve patient outcomes. This study aims to develop a classification model using machine learning algorithms to predict CHD risk based on clinical symptoms. The dataset used is the Cleveland Heart Disease dataset from the UCI Machine Learning Repository, consisting of 303 patient records with 14 clinical features. The preprocessing stage involved handling missing values, normalizing features, and transforming categorical variables. Four classification algorithms were applied: K-Nearest Neighbors (K-NN), Decision Tree, Random Forest, and Support Vector Machine (SVM). Each model was trained using stratified 10-fold cross-validation to ensure generalizability. Evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics showed that the Random Forest algorithm achieved the highest performance with 87.2% accuracy. Feature importance analysis indicated that chest pain type, resting blood pressure, cholesterol, and ST depression were the most influential indicators. These results demonstrate that machine learning, particularly Random Forest, can effectively support early diagnosis of CHD in clinical settings and has the potential to be integrated into clinical decision support systems (CDSS).

Thyroid cancer is the most common endocrine malignancy, with a steadily increasing incidence rate. Although the overall survival rate is relatively high, the risk of recurrence after definitive treatment such as Radioactive Iodine (RAI) therapy remains a significant clinical challenge. Predicting recurrence risk is crucial for optimizing monitoring strategies and interventions. With advances in technology, machine learning (ML) approaches are increasingly utilized to support medical predictions, including the recurrence of thyroid cancer. This study aims to evaluate the performance of four classification algorithms—Logistic Regression, XGBClassifier, Random Forest Classifier, and Voting Classifier—in predicting thyroid cancer recurrence using the Thyroid Cancer Recurrence After RAI Therapy dataset, which consists of 383 patient records and 13 key clinical attributes. The evaluation was conducted using accuracy, precision, recall, F1-score, and area under the curve (AUC) metrics. The results show that the XGBClassifier is the best-performing model with an accuracy of 97.4% and an AUC of 0.95, demonstrating superior performance in handling the minority class. This research is expected to contribute to the development of more effective machine learning–based clinical decision support systems for predicting thyroid cancer recurrence after therapy.

This study aims to examine the effect of each variable—Institutional Ownership, Capital Intensity, and Sales Growth—on Tax Avoidance in the Consumer Goods Industry listed on the Indonesia Stock Exchange. The data source for this research is the Indonesia Stock Exchange, covering the period from 2017 to 2023. Hypothesis testing was conducted using Panel Data Regression analysis with the Random Effect model. The results of this study indicate that there is no significant effect of Sales Growth on Tax Avoidance, whereas Institutional Ownership and Capital Intensity have a significant effect on Tax Avoidance.

This research is motivated by the problems in class VIII of Al-Irsyad TI Bulaan Kamba Islamic Boarding School that the learning process is still centered on the teacher, lack of student response to mathematics learning, lack of student courage to ask the teacher, students still think mathematics is difficult and scary, lack of interest in learning mathematics, and students are less enthusiastic when participating in mathematics learning. One alternative learning that can be done to overcome these problems is to apply the Problem Based Learning learning model. This study aims to determine the effect of the Problem Based Learning learning model on the interest in learning mathematics of class VIII students of Al Irsyad TI Bulaan Kamba Islamic Boarding School. The hypothesis in this study is "there is a significant effect of the Problem Based Learning learning model on the interest in learning mathematics of class VIII students of Al Irsyad TI Bulaan Kamba Islamic Boarding School". This type of research is a pre-experiment with a one group pretest-posttest research design, the population in this study were all students of class VIII of Al Irsyad TI Bulaan Kamba Islamic Boarding School, the sampling used was Random Sampling, first a normality test, homogeneity test, and average similarity test were carried out on the population data. The sample in this study was class VIII A as the sample class. The instrument in this study was a questionnaire on students' interest in learning mathematics. The data analysis technique used in this study was the paired sample t test. The results obtained from the paired sample t test were t count = 2.81 and t table = 2.05 and sig = 0.0045 at a real level of α = 0.05. From these results it can be concluded that H0 is rejected and H1 is accepted. So it can be concluded that there is a significant influence of the Problem Based Learning learning model on the interest in learning mathematics of class VIII students of Al Irsyad TI Bulaan Kamba Islamic Boarding School.    

Pending claims in Indonesia’s National Health Insurance (JKN) system pose a significant challenge, affecting hospital cash flow and administrative efficiency. A high rate of pending claims is often caused by incomplete documentation, misfiled medical records, and delays in verification processes. Optimizing manual administrative procedures may provide a solution to this issue. This study aims to evaluate the effectiveness of a standardized manual administration model in reducing pending claims in a hospital setting. A quasi-experimental pretest-posttest design without a control group was employed. Data were collected from a referral hospital in Indonesia from July to September 2024. A total of 138 inpatient claims were selected using simple random sampling. The intervention involved implementing a standardized manual administration system in August and September. Statistical analysis was conducted using Fisher’s Exact Test with a significance level of p < 0.05. The implementation of the standardized manual administration model significantly reduced pending claims from 70.3% (97/138) in July (pre-intervention) to 36.2% (50/138) in August and further to 11.6% (16/138) in September (post-intervention) (p = 0.000 and p = 0.003, respectively). Additionally, improvements were observed in medical record completeness, supporting examination documentation, and administrative accuracy. A standardized manual administration system effectively decreases pending claims in JKN by improving documentation and claim verification processes. Further research is needed to explore the long-term sustainability of this model and the potential benefits of digitalization.  

The interaction between genetic and environmental factors plays a crucial role in determining phenotypic traits in organisms. This study aims to analyze these interactions using computational approaches, including statistical models and machine learning algorithms. The data used include genetic factors (genotypes) and simulated environmental factors. Results indicate that machine learning models such as Random Forest can detect interaction patterns with high accuracy, as demonstrated by significant R² values. Additionally, heatmap visualizations provide deeper insights into the non-linear effects of genetic-environment interactions. This study highlights the potential of computational methods in exploring complex interactions, with broad applications in health, agriculture, and biotechnology.

The development of artificial intelligence (AI) in learning media presents challenges related to the uneven readiness of human resources and infrastructure, thus affecting the effectiveness of its implementation. This study aims to examine the effect of AI implementation in learning media development on learning outcomes of students of Faculty of Economics, University of Balikpapan. The method used is quantitative with survey design and Structural Equation Modeling (SEM) analysis using AMOS. The sample consists of 113 students selected by simple random sampling from the population of 376 active students. The results of the analysis showed that the readiness of lecturers and the quality of AI-based learning media had a significant effect on improving student learning outcomes. However, the success of AI implementation is also strongly influenced by infrastructure support and educator training. The findings provide important implications for learning media developers and policy makers to strengthen lecturers' capacity and improve technology infrastructure to support inclusive and sustainable digital transformation of education. In addition, ethical aspects and data privacy should be the main concerns in the development of AI-based learning media.

This meta-analysis systematically and comprehensively examines the structural and functional roles of the extended family in moderating the psychological impact of war trauma in the Middle East by compiling data from 47 independent studies involving 12,483 participants published between 2000 and 2023. An analytical approach using a random-effects model revealed that the presence and involvement of the extended family demonstrated a statistically significant moderating effect on the reduction of PTSD symptoms, anxiety, and depression, with an association strength of r = .42 (p < .001). Further meta-regression results indicated that support from the extended family contributed to a 37.8% reduction in PTSD symptoms (β = -.378, SE = .042, p < .001), a 29.4% decrease in anxiety levels (β = -.294, SE = .038, p < .001), and a 31.2% reduction in depressive symptoms (β = -.312, SE = .040, p < .001). Analysis of moderator variables showed that the protective effect of the extended family structure was more pronounced among children (r = .48) compared to the adult population (r = .38) and more salient among females (r = .45) than males (r = .39), indicating demographic sensitivity to the type of collective support received. Moreover, the high heterogeneity across studies (I² = 76.3%) indicated significant contextual and methodological diversity, though it did not obscure the core findings. These results contradict the theoretical emphasis advanced by Nakeyar and Frewen (2016) and Atallah (2017), who prioritized the role of the nuclear family in post-war healing contexts. In contrast, this study found that the extended family configuration has provided a more comprehensive and multidimensional form of psychological protection rooted in the distinct collectivistic values of Middle Eastern societies. Ultimately, these findings expand the conceptual horizon for understanding resilience mechanisms to trauma within non-Western cultural landscapes and open new possibilities for developing extended family-based interventions in the context of post-conflict psychosocial reconstruction.

This research was made to examine the determinants of financial performance of banking companies in Indonesia. There are four independent variables (board of diversity, net interest margin, operational efficiency, and liquidity risk) and a moderating variable (firm size) have been analyzed in this research. Testing the interaction effect of firm size in explaining the influence of these four independent variables on banking financial performance is still very limited. This quantitative research was analyzed by using secondary data from audited annual reports of the company. The purposive sampling technique was used to choose the research’s samples during the observation periods (2018-2022) and obtained 200 observations (40 samples over 5 years of research). Panel data regression with the EViews program was used to test the eight hypotheses which was developed in this research. The results of the Chow test and Hausman test confirm the use of the Random Effect Model in the analysis. The findings from testing the interaction model show that firm size does not moderate the influence of board of diversity and net interest margin on financial performance, while for operational efficiency and liquidity risk variables, the firm size shows a pure moderating role for the both.

The low level of student learning activity is often found in school learning activities, which has an impact on poor learning outcomes. This study aims to examine the significant comparison of learning activities and physics learning outcomes of vocational high school students using the cooperative learning models STAD (Student Teams Achievement Division) and TGT (Teams Games Tournament), as well as to describe student responses after the implementation of both models on the topic of energy and its changes. This research is a true experimental study with a posttest-only control group design. The population in this study consisted of 186 students, and the sample was determined using a cluster random sampling technique. Data collection methods included observation, tests, questionnaires, interviews, and documentation. The classical assumption test used was the normality test, followed by statistical analyses using the Independent Sample T-test and the Mann-Whitney U-test. The results of the analysis of student learning activity data using the Independent Sample T-test showed a Sig. (2-tailed) value of 0.699 > 0.05, while the analysis of learning outcome data using the Mann-Whitney U-test showed a Sig. (2-tailed) value of 0.806 > 0.05. These results indicate that there is no significant difference between the two learning models in terms of their effect on student learning activities and physics learning outcomes. In addition, the analysis of student responses showed that the implementation of the STAD and TGT models received a very good response. Thus, it can be concluded that while there is no significant difference between the two models, both are effective in generating student interest and positive engagement in learning physics.

This study aims to determine the effect of the Discovery Learning model assisted by E-LKPD on students' physics learning outcomes. The research was conducted at SMA Negeri 1 Percut Sei Tuan. This type of research is a quasi-experimental design with a pretest-posttest control group design. The sampling technique used in this study was cluster random sampling, consisting of an experimental class and a control class, each with 36 students. The solution to address the research problem was to apply the Discovery Learning model assisted by E-LKPD in the experimental class and conventional learning in the control class. The subjects of this study were students in classes X-3 and X-4. The instrument used was a learning test in the form of 15 multiple-choice questions. Before the different treatments were applied, a pretest was conducted, resulting in an average pretest score of 48.1 for the experimental class and 45.6 for the control class. Normality and homogeneity tests on the pretest data indicated that both classes' data were normally distributed and homogeneous. The two-tailed t-test resulted in a value of tcount < ttabel = = 0.97 < 1.994, which means H0 was accepted, indicating that the initial critical thinking ability of the experimental and control classes was the same. After the different treatments, a posttest was conducted for both classes, which resulted in an average posttest score of 76.5 for the experimental class and 63.5 for the control class. Normality and homogeneity tests on the posttest data showed that both classes' data were normally distributed and homogeneous. The one-tailed t-test resulted in tcount < ttabel = = 4.860 > 1.666, so the alternative hypothesis (Ha) was accepted. It can be concluded that the use of Discovery Learning assisted by E-LKPD has a significant effect on students' learning outcomes on the topic of work and energy in class X at SMA Negeri 1 Percut Sei Tuan.

This study explores the application of the normal distribution in analyzing the height data of Mathematics Education students at FMIPA Universitas Negeri Medan in 2024. Employing a quantitative descriptive-analytic methodology, the research involved collecting primary data from 10 randomly selected students through a questionnaire-based survey. Descriptive statistical analysis revealed a mean height of 161.4 cm with a standard deviation of 8.79 cm. The median height was found to be 164 cm, while the mode was 150 cm, indicating a slightly skewed distribution. To assess the suitability of the normal distribution model, the Shapiro-Wilk test was applied, resulting in a W value of 0.921 and a p-value of 0.361, which exceeds the 0.05 significance level. This confirms that the sample data follow a normal distribution pattern. The findings were further supported through visual representation using histograms and analysis based on the empirical rule, which showed that approximately 68% of the students' heights fall within one standard deviation of the mean (152.81–169.99 cm). Additionally, probability calculations demonstrated that the likelihood of a student being 160 cm tall or shorter is approximately 43.64%. These results validate the effectiveness of the normal distribution as a tool for analyzing biological or physical characteristics, even in small sample sizes. However, the study acknowledges its limitation in terms of sample size and suggests that future research involve larger and more diverse populations to enhance generalizability. The study highlights the relevance of normal distribution in statistical modeling, particularly for educational and health-related data interpretation and decision-making processes.