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Hypertension is one of the leading non-communicable diseases contributing significantly to cardiovascular morbidity and mortality worldwide. Despite the availability of extensive electronic medical record data in healthcare institutions, these data are often utilized only for administrative reporting rather than predictive analysis. Consequently, opportunities to identify age groups with a higher probability of developing hypertension remain underutilized. This study aims to implement the Naïve Bayes classification algorithm to analyze age distribution and classify the risk of hypertension among patients using healthcare data. The research adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, including business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Patient medical record data consisting of demographic and clinical attributes, including age, systolic blood pressure, diastolic blood pressure, body weight, gender, and hypertension status, were processed using the Naïve Bayes algorithm. Model performance was evaluated using a confusion matrix by measuring accuracy, precision, recall, specificity, and balanced accuracy. The implementation demonstrates that the Naïve Bayes algorithm is capable of classifying hypertension risk efficiently while providing probabilistic information regarding age groups with a higher tendency to experience hypertension. The resulting classification model offers an effective decision-support tool for healthcare providers in conducting targeted screening, preventive interventions, and evidence-based health planning. The findings also indicate that data mining techniques can transform routinely collected medical records into valuable clinical knowledge for early hypertension prevention and healthcare decision-making.

Digital transformation in the military procurement sector has brought significant changes to accountability patterns at the Naval Academy (AAL). Using the AP2EP management cycle (Analysis, Planning, Execution, Evaluation, and Control) as an analytical tool, this paper dissects the extent to which the E-Procurement system can mitigate budget deviation risks and enhance financial transparency. As a military educational institution striving to become a World Class Naval Academy, AAL faces unique challenges in balancing state financial regulations with specific educational logistics needs. Through a descriptive qualitative approach, this research demonstrates that procurement digitalization provides an automated audit trail that minimizes human intervention. Despite technical and cultural obstacles, strategic steps such as developing real-time dashboards have proven effective in optimizing state financial governance to support cadet education quality and maintain an Unqualified Opinion (WTP).

Batik motif classification has attracted growing attention in visual computing due to its role in cultural heritage preservation, textile informatics, museum documentation, and automated cataloging. Although many studies report high classification accuracy, robustness under real-world acquisition conditions remains insufficiently understood. Batik images are frequently affected by illumination variation, blur, folds, watermark overlays, wearable deformation, scale inconsistency, and background clutter, creating challenges that extend beyond conventional image-noise assumptions. Existing studies largely focus on improving classification performance, while the interactions among acquisition variability, feature representation, evaluation practice, and deployment constraints remain fragmented. This systematic literature review addresses this gap by synthesizing batik classification research through a robustness-aware perspective. Using query expansion, backward and forward citation chaining, relevance screening, and thematic coding, 116 candidate records were identified, resulting in 50 highly relevant studies for detailed analysis. The review reveals that robustness is shaped less by denoising alone than by the combined effects of acquisition conditions, representation design, evaluation realism, and deployment context. Handcrafted descriptors remain competitive for small datasets and structured motifs due to their data efficiency and interpretability, whereas deep learning models achieve the highest reported accuracy when supported by sufficient data diversity and realistic augmentation. Hybrid representations emerge as the most consistently balanced approach, combining local texture stability with higher-level abstraction across heterogeneous acquisition settings. The review further identifies recurring robustness failure patterns, including background dependency, illumination instability, motif-scale inconsistency, wearable deformation, and source-shift vulnerability. Based on these findings, a robustness-oriented research agenda is proposed, emphasizing cross-acquisition evaluation, representation-stability analysis, batik-specific robustness benchmarks, acquisition-aware augmentation, and deployable lightweight or hybrid architectures. The study contributes a domain-specific synthesis that reframes batik motif classification from an accuracy-centric task toward a robustness-aware visual recognition problem.

Allergic rhinitis (AR) is an inflammatory process of the nasal mucosa initiated by a hypersensitivity reaction and caused by exposure to allergens mediated by immunoglobulin E (IgE), with several characteristic symptoms including: nasal congestion, a runny nose or watery nasal discharge (rhinorrhea), nasal itching, sneezing, and  postnasal drip (PND). According to the World Health Organization’s Allergic Rhinitis and its Impact on Asthma (WHO-ARIA) guidelines, based on the duration of symptoms, allergic rhinitis is classified into two categories: intermittent allergic rhinitis (symptoms lasting less than 4 days per week or for less than 4 weeks) and persistent allergic rhinitis (symptoms lasting more than 4 days per week and for more than 4 weeks). The prevalence of allergic rhinitis based on a doctor’s diagnosis is approximately 15%; however, it is estimated to reach 30% when considering patients with nasal symptoms. Appropriate management of allergic rhinitis, in addition to alleviating symptoms, is also expected to improve the quality of life of patients whose lives have been disrupted by the condition, as the higher the severity and frequency of allergic rhinitis symptoms, the greater the impact on reduced quality of life.

Breast tumors are classified into two types, namely benign and malignant tumors, the latter commonly referred to as breast cancer. Breast cancer is one of the major health problems affecting women worldwide, including in Indonesia. According to WHO data in 2022, there were 2.3 million breast cancer cases with 685,000 deaths globally, while in Indonesia, 396,914 cases and 234,511 deaths were reported. The high incidence rate is exacerbated by low public awareness in recognizing early symptoms and performing early detection, resulting in 60–70% of cases being diagnosed at an advanced stage, supported by findings that 65.6% of female students still have a low level of knowledge. Female students were selected as research subjects because they are in a vulnerable reproductive age group and have an important role in increasing awareness of early detection, yet they still have limited knowledge. Based on this, this study aims to design a web-based early detection system for breast tumor risk using the Forward Chaining method, which functions as a tool to identify early symptoms, assess risk levels, and provide information on prevention and initial management. This study employed the method with the Expert System Development Life Cycle (ESDLC) model, consisting of the stages of assessment, knowledge acquisition, design, testing, and documentation, along with the Forward Chaining inference method.

This study analyzes visual signs in the Charlie Chaplin animations Safari at The Park and The King in The Ring using Charles Sanders Peirce’s semiotic framework, focusing on icons, indexes, and symbols. Film and animation communicate meaning through visual elements such as gestures, facial expressions, movements, and character interactions, making them rich for semiotic analysis. Using a descriptive qualitative method, the research identified and categorized visual signs in both animations. Results show that icons, which resemble real-world objects, dominate by establishing story settings natural safari environments in one animation and competitive boxing arenas in the other. Indexes reveal cause-and-effect relationships, demonstrating how gestures, expressions, and actions convey danger, fatigue, or emotional shifts. Symbols convey conventional or cultural meanings, such as Charlie Chaplin’s bowler hat and cane representing his comedic identity, a championship belt symbolizing victory, or a rose indicating affection. While both animations use the same types of signs, the intensity and focus vary with the narrative context: Safari at The Park emphasizes situational and natural elements, whereas The King in The Ring highlights competition and emotional reactions. This study confirms that Peirce’s triadic model effectively explains how meaning is constructed in animation through dynamic visual communication.

This study aims to analyze the effect of direct learning on engine components toward students' vehicle diagnostic abilities in vocational education. Employing a quantitative approach with an ex post facto design, the sample consisted of 80 students selected using simple random sampling from a population of 100 students. Data were collected using validated and reliable questionnaires. The data analysis techniques included descriptive statistics, classical assumption tests, and simple linear regression analysis. The results show that direct learning has a positive and significant effect on students’ vehicle diagnostic abilities, with a significance value of 0.000 < 0.05 and a coefficient of determination (R²) of 0.264. This indicates that direct learning contributes 26.4% to the improvement of students' diagnostic abilities. The findings emphasize the importance of practical, hands-on learning approaches in vocational education. These approaches not only enhance students' understanding of automotive theory but also improve their diagnostic skills. The study highlights that systematic learning and direct interaction with engine components are key factors in developing students' competencies, making them more effective in real-world vehicle diagnostics.

Psychological conflicts often arise intensely, as illustrated in Usmar Ismail’s play Ayahku Pulang. The character Gunarto experiences inner turmoil and trauma from family disputes, which trigger various defense mechanisms. This study aims to describe the forms of defense mechanisms exhibited by Gunarto and identify the factors behind their emergence.  Using a literacy  psychology approach, specifically Sigmund Freud’s psychoanalytic theory, the research elaborates on the character’s psychological dynamics. The qualitative method employed the read-and-record technique, with data analyzed through the Miles and Huberman model involving reduction, classification, and inference. Findings reveal eight defense mechanisms used by Gunarto: denial, repression, avoidance, compartmentalization, rationalization, displacement, sublimation, and identification with the aggressor. Their emergence is influenced by psychological factors such as past trauma, economic hardship, and disharmonious family relationships. In conclusion, Gunarto’s defense mechanisms reflect symptoms of individuals facing trauma and family disharmony. This study demonstrates how a literary-psychological approach enriches understanding of literature as a reflection of the human psyche and reveals more about a character’s inner world.

Differential equations involve derivatives of unknown functions and are widely used in mathematical modeling of various real-world problems. They can be classified based on linearity, homogeneity, coefficients, number of independent variables, degree, and order, thus requiring appropriate solution methods. One commonly used approach is the reduction of order method, which simplifies equations by reducing their order step by step. However, this method generally requires the solution of the corresponding homogeneous equation as an initial step. This study aims to solve nonhomogeneous linear ordinary differential equations of order with constant coefficients using the reduction of order method without determining the homogeneous solution. This research is a theoretical study based on relevant references concerning solution methods and types of differential equations. The procedure consists of two main stages: determining the fundamental symmetric polynomial variables based on the coefficients and constructing a sequence of solutions through first-order linear differential equations obtained from the reduction process. The results show that this method systematically produces the general solution of linear differential equations of order , making it an effective and efficient alternative approach

Febrile seizures are seizure episodes that occur in association with an elevation in body temperature (rectal temperature >38°C) caused by an extracranial process. Febrile seizures are classified into two types: simple febrile seizures and complex febrile seizures. The World Health Organization (WHO) estimated that in 2019 there were 18.3 million cases of febrile seizures worldwide, with approximately 154,000 resulting in death. This case report discusses a patient, An YZ, a 1 year and 5-month-old female, who was brought to the emergency department of Cut Meutia Hospital with a chief complaint of seizures. The seizures began two days prior to hospital admission, characterized by generalized tonic stiffening and clonic movements involving the entire body. Each episode lasted approximately 5 minutes. The seizures occurred twice, initially at 7:00 PM and subsequently at 9:00 PM. The patient was diagnosed with complex febrile seizures associated with morbilli and very mild microcytic hypochromic anemia due to iron deficiency anemia. Pharmacological management included cefotaxime, ranitidine, ondansetron, paracetamol, ambroxol, cetirizine, diazepam (Stesolid), and vitamin D. After three days of hospitalization, the patient showed clinical improvement and was discharged.

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 increasing sophistication of malware has rendered traditional signature-based detection methods insufficient, necessitating behavior-driven and adaptive analytical frameworks. This study presents a sequential deep learning framework that models system-level API call sequences as structured linguistic representations for behavioral malware detection. Unlike conventional comparative studies, this work systematically evaluates recurrent and attention-based architectures under controlled experimental conditions, with a particular focus on generalization performance and overfitting mitigation. Two neural architectures, a Long Short-Term Memory (LSTM) network and a Transformer-based attention model, are trained on publicly available API call sequence data for binary classification of malicious and benign executables. Beyond standard accuracy metrics, the study further examines model stability, convergence behavior, and the impact of long-range dependency modeling on detection robustness. Experimental results demonstrate that the Transformer architecture achieves superior performance, attaining 95.54% classification accuracy and consistent improvements in precision, recall, and F1-score, indicating a stronger ability to capture complex behavioral dependencies. These findings highlight the effectiveness of attention mechanisms in behavioral malware modeling and provide empirical evidence that NLP-inspired architectures offer a robust and scalable approach for real-world cybersecurity applications.

This study analyzes the concept of al-Kasb in the view of Muhammad ibn al-Hasan al-Shaybani and assesses the relationship between this concept and contemporary work ethics. With the changes in the modern economic system, which emphasize efficiency, objective rationality, and the achievement of optimal results, various ethical conflicts in the professional and business world have become more apparent. The methodology applied in this study is a literature review with a descriptive-analytic approach, through the analysis of classical and contemporary texts to explore the theological, ethical, and social aspects contained in the concept of al-Kasb, and then conceptually compare it with the characteristics of modern work ethics. The findings of this study show that al-Kasb is not only understood as an economic activity aimed at accumulating wealth, but also as a normative guide that integrates the goals of worship, ethical responsibility, and social obligations in the production process. Work activities are viewed as an individual responsibility that supports the practice of worship and as a collective responsibility to maintain social-economic welfare and harmony. Therefore, al-Kasb provides an integrative ethical foundation that has the potential to enrich and strengthen current work ethics by emphasizing values of integrity, justice, and social responsibility in the economic aspects of life.

Indonesia harbors extraordinary linguistic diversity with over 700 regional languages representing approximately 10% of the world's languages within 1.3% of global land area. However, this diversity faces existential threat from language shift toward Indonesian, urbanization, education policies favoring the national language, and globalization. UNESCO classifies 146 Indonesian languages as endangered, with several dozen facing imminent extinction as last speakers age without intergenerational transmission. This study documents the current vitality status of Indonesian regional languages, analyzes factors driving language endangerment and shift, evaluates existing conservation efforts, and proposes evidence-based strategies for language revitalization and maintenance. A multi-phase approach was employed: vitality assessment of 150 regional languages using UNESCO's Language Vitality and Endangerment framework with surveys involving 2,400 speakers; ethnographic case studies in 12 speech communities; policy analysis; evaluation of 25 revitalization programs; and predictive modeling of language shift trajectories. Of 150 surveyed languages, only 23 (15.3%) classified as safe with robust intergenerational transmission; 48 (32.0%) were vulnerable; 42 (28.0%) definitely endangered; 28 (18.7%) severely endangered; and 9 (6.0%) critically endangered. Key endangerment drivers included Indonesian-only education (92.3% of schools), urban migration (67.8% of youth), negative language attitudes (54.2% of parents), and lack of written traditions (73.4% of languages lacking orthographies). Modeling projected that without intervention, 40% of currently vulnerable languages will become definitely endangered within 20 years. Successful revitalization demands community-owned interventions, mother-tongue-based multilingual education, new digital language domains, and attitude change campaigns. Indonesia's linguistic diversity represents invaluable cultural and scientific heritage requiring urgent, coordinated conservation action.

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.

This study aims to analyze the influence of digitalization on the lifestyles of students at Universitas Nasional Pasim. The research employs a quantitative approach through field survey methods, targeting a population of 123 students from the Faculty of Economics, Class of 2025. A minimum sample size of 56 respondents was determined using the Slovin formula. The sampling technique utilized was purposive sampling, while data analysis involved validity and reliability tests, classical assumption tests, and multiple regression analysis to determine the relationships between variables. Empirical results indicate that digitalization has a positive and significant influence on student lifestyles, with the F-calculated value exceeding the F-table at the specified level of significance. The R-Square value further explains the contribution of the digitalization variable to changes in the respondents' lifestyles. The findings reveal positive impacts such as increased academic efficiency, instant access to information, and the convenience of transactions through practical digital payment systems. Conversely, negative impacts identified include a rise in consumptive behavior driven by cashless and paylater features, where student budgets are increasingly used for non-academic purposes. Furthermore, high intensity of social media usage triggers the Fear of Missing Out (FOMO) phenomenon and a decline in real-world social interaction. In conclusion, digitalization is a pivotal factor in shaping modern student lifestyles. It is essential to enhance financial literacy and strengthen self-control to minimize the negative impacts of technology in the future.

Higher education management at the master's level currently faces urgent challenges, namely learning fatigue and low engagement among professional students, especially in Prior Learning Recognition (RPL) classes. Currently, lecturers still tend to apply conventional learning methods based on static presentations that fail to accommodate andragogical characteristics due to a lack of dynamic interaction. Therefore, this study aims to evaluate the effectiveness of the “Humanistic Digital Andragogy” approach through the integration of gamification (Kahoot) and visual thinking (Whimsical) in the Strategic Human Resource Management course. The researchers used a descriptive qualitative design with thematic analysis and collected data through feedback from students in the Master of Management Program (Semarang and Sorong classes). The results revealed that technology served as a double catalyst: Whimsical visualization effectively reduced the cognitive load of complex strategy material, while competition in Kahoot triggered positive adrenaline (eustress) that increased attention. These findings confirm that the success of technology is highly dependent on the role of lecturers as humanistic facilitators (high-touch). This synergy has been proven to change students' perceptions of HRM from merely administrative to strategic partners, as well as creating learning satisfaction that is relevant to the world of work.

The idea of functions in mathematics and how they are used to build different mathematical models are methodically examined in this publication. Functions are basic mathematical constructs that show relationships between two or more variables in explicit equations, tables, or graphs. The fundamental building blocks of mathematics are functions, which enable the representation of variable interdependencies in a variety of formats, including formal mathematical expressions, data tables, and graphs. The classification of function types, such as linear, quadratic, and exponential, and their corresponding uses in the domains of physics, economics, and epidemiology are the main topics of this study, which takes a descriptive and exploratory approach.This article illustrates how knowledge of functions greatly aids processes through a review of the literature and an examination of secondary sources from current textbooks and academic publications. of judgment, forecasting, and analysis. In both academic and professional contexts, mathematical modeling based on functions has demonstrated efficacy in accurately and efficiently representing real-world occurrences. Thus, the significance of incorporating functional thinking into STEM education and multidisciplinary practice is emphasized in this essay.

The increasing complexity and scale of modern network traffic driven by IoT and cloud-based infrastructures have made accurate intrusion detection a critical challenge. Conventional network intrusion detection systems (NIDS) and many deep learning–based approaches struggle to reliably detect minority and stealthy attacks due to severe class imbalance and limited discrimination of subtle traffic patterns. To address these limitations, this study proposes a hybrid CNN–RBF–Attention framework for network intrusion detection. The proposed model integrates three complementary components: (i) a convolutional neural network for hierarchical feature extraction from network flow data, (ii) a radial basis function (RBF) network for localized nonlinear classification using prototype-based decision regions, and (iii) an attention mechanism that adaptively weights RBF activations to emphasize discriminative traffic patterns. SMOTE is applied exclusively to the training data to mitigate class imbalance. The framework is evaluated on the widely used CICIDS2017 and CICIDS2018 benchmark datasets in both binary and multiclass settings, using recall, precision, F1-score, confusion matrices, and ROC analysis. Experimental results demonstrate that the proposed hybrid model consistently outperforms standalone CNN and RBF baselines, particularly in terms of recall and F1-score. On the CICIDS2018 dataset, the model achieves 99.81% accuracy and 99.81% F1-score in binary classification, and 99.54% accuracy and 99.54% F1-score in multiclass classification. On CICIDS2017, it achieves 98.12% accuracy and 98.12% F1-score in binary classification, and 98.92% accuracy and 98.92% F1-score in multiclass classification. Confusion matrix and ROC analyses further show strong class separability and reliable performance in low–false-positive-rate regions, which is critical for real-world IDS deployment. These results confirm that combining deep hierarchical feature learning, localized prototype-based classification, and attention-guided refinement yields a robust, operationally reliable intrusion detection framework for highly imbalanced network environments.

Hypertension is a major modifiable risk factor contributing to the development of Acute Coronary Syndrome (ACS), which includes STEMI, NSTEMI, and unstable angina. The increasing prevalence of hypertension worldwide raises concern regarding its impact on cardiovascular outcomes. This study aimed to describe the profile of ACS patients with hypertension receiving angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) therapy in the Intensive Coronary Care Unit (ICCU) of RSUD Dr. Soetomo Surabaya. Using a descriptive cross-sectional method, data from 91 patients treated between July 2021 and October 2024 were analyzed. Variables included demographic characteristics, clinical classification of ACS, hypertension degree, comorbidities, types and doses of ACEI/ARB administered. The results showed that most patients were male (73%) and aged over 65 years (40%). Chi-square analysis revealed no significant relationship between hypertension degree, ACS classification, or most comorbidities with drug selection or dosage (p>0.05), except for a significant association between coronary heart disease comorbidity and ARB selection. These findings suggest that in hypertensive ACS patients, the choice between ACEI and ARB therapy is predominantly based on individual comorbidity profiles rather than blood pressure severity or ACS type. The study highlights the importance of personalized treatment approaches considering patient comorbidities to optimize cardiovascular outcomes.