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Adolescent girls often complain of dysmenorrhea, or discomfort during menstruation, which can range from mild to severe and interfere with daily activities, especially learning. To prevent this disease from lowering the quality of life of adolescents, it must be treated. Education about dysmenorrhea and its treatment is one of the actions that can be taken. The study used a pre-experimental design approach, a quantitative research design, and a single-group pre-test-post-test research plan without a control group design. Using a proportional stratified random sampling approach, 57 female students of class X formed a research sample. The use of films on dysmenorrhea for health education serves as an independent variable of the study. Adolescent girls' knowledge and attitudes about dysmenorrhea function as a study-dependent variable. The validity and reliability of these instruments are checked. Univariate and bivariate analysis were used as data analysis methods in this study. The Wilcoxon Signed Rank Test is used for hypothesis testing. It was determined that health education using dysmenorrhea film had an impact on the knowledge and attitudes of adolescent girls about the treatment of dysmenorrhea based on the findings of the Wilcoxon Signed Rank Test (Asymp.) with a value of 2-tailed 0.000 (p<0.05). The findings of the study showed that among grade 10 students at SMA Negeri 13 Selatan Halamahera, health education through dysmenorrhea films had an impact on the knowledge and attitudes of adolescent girls about the treatment of dysmenorrhea.

This study examines the determinants of green innovation and its impact on sustainable business performance among micro, small, and medium enterprises (MSMEs) in the Bandung Raya region of Indonesia. Specifically, the study analyzes the influence of environmental knowledge and market pressure on green innovation, as well as the effect of green innovation on sustainable performance. A quantitative approach was employed using data from 150 MSMEs, and structural relationships were tested through multiple regression analysis. All measurement instruments demonstrated high reliability (Cronbach’s Alpha 0.89–0.95) and validity (corrected item–total correlation > 0.80). The results reveal that environmental knowledge has the strongest positive and significant effect on green innovation (β = 0.728; p < 0.001), indicating that MSMEs with greater environmental awareness are more likely to adopt eco-friendly innovations. Market pressure also significantly influences green innovation (β = 0.257; p < 0.001), demonstrating the role of consumer expectations, competition, and green product trends in shaping sustainable business practices. Furthermore, green innovation has a very strong and significant impact on sustainable business performance (β = 0.847; p < 0.001), suggesting that eco-friendly practices enhance cost efficiency, customer satisfaction, firm reputation, and environmental outcomes. Overall, the study highlights the importance of combining internal awareness with external pressures to foster green innovation and strengthen sustainability among MSMEs. The findings provide theoretical contributions to green innovation and sustainability frameworks, while offering practical implications for MSMEs, policymakers, and business support institutions.

Presidential Instruction Number 1 of 2025 concerning State Expenditure Efficiency gave rise to a fiscal paradox at the beginning of the new administration, with a cut of Rp306.69 trillion facing a 17.9% APBN deficit for flagship programs such as Free Nutritious Meals, which triggered doubts about public trust, especially students at the University of Jambi affected by the BOPTN adjustment. This study analyzes the influence of student perceptions on the dimensions of effective-efficient (X1) and transparent-accountable (X2) policies on public trust (Y) as an indicator of government legitimacy. A quantitative survey approach was applied to 400 active students at the University of Jambi (proportional random sampling using the Slovin e=0.05 formula), with SPSS multiple linear regression analysis after classical assumption testing, validity (r> r-table), and reliability (Alpha Cronbach's 0.920 (x1); 0.949 (x2); 0.918 (y)). The results show a significant simultaneous effect (F=200.951; sig=0.000), partial X1 is dominant (t=7.116; β=0.162; sig=0.000) and X2 is significant (t=5.532; β=0.110; sig=0.000), with R²=0.503 explaining 50.3% of the variation in trust. The findings confirm the theory of Easton (1965) and Weber (1947) that efficiency performance evaluation shapes trust, so it is recommended that a real-time APBN dashboard, transparent communication to regional PTNs, and fiscal literacy strengthen the legitimacy of good governance.

“Hallyu” (the Korean Wave) refers to the popularity of everything originating from Korea. Aims to examine the description of students’ K-Pop celebrity worship behavior before and after receiving group counseling with the Cognitive Restructuring technique, analyze the differences in students’ behavior, and test the effect of the counseling on reducing celebrity worship tendencies. The findings are expected to provide theoretical contributions to the field of guidance and counseling, particularly regarding the application of Cognitive Restructuring to mitigate obsessive behaviors, as well as practical benefits for school counselors and institutions in helping students reduce celebrity worship tendencies so they can remain focused on academic and social development. The research employed an experimental method with a One-Group Pretest–Posttest design. The subjects consisted of eight tenth-grade students at SMK Negeri 1 Kuantan Mudik who were identified as having a high level of K-Pop celebrity worship. The instrument used was a 19-item K-Pop celebrity worship scale that had been tested for validity and reliability. Data were analyzed using the Wilcoxon test and N-Gain calculations to determine treatment effectiveness. The results showed that prior to receiving group counseling with Cognitive Restructuring techniques, most students were categorized as having high or moderate levels of celebrity worship. After the intervention, all students experienced a decrease in celebrity worship behavior, falling into the low category. This demonstrates that group counseling using Cognitive Restructuring is effective in helping students shift irrational thinking patterns toward more rational ones, enabling them to control excessive celebrity worship behavior.  

This research extends the previous work of Tsaqila, Winiarti, and Widaningrum (2024), who applied the Complex Proportional Assessment (COPRAS) method within a decision support system for supermarket branch location selection. Unlike the prior study, which focused on Ponorogo through a web-based framework, this study expands the implementation of COPRAS to the Medan Area, Medan Kota, Medan Polonia, dan Medan Maimun districts, adapting it to local urban, social, and economic characteristics. The main objective is to identify the most strategic site for a new supermarket by analyzing multiple criteria, including land cost, population density, accessibility, safety, cleanliness, and disaster risk. Data were collected from both field surveys and official government publications. The findings reveal that the COPRAS method provides reliable and objective assessments among the evaluated alternatives, with Medan Area emerging as the most suitable location for supermarket development. Overall, this study broadens the practical scope of the COPRAS method in a different regional context and reinforces its reliability and adaptability as a multi-criteria decision-making tool in the modern retail industry.

The growing demand for transparency, accountability, and measurable performance has transformed higher education institutions into complex public organizations required to deliver reliable and stakeholder-oriented services. Within this governance-driven environment, institutional governance plays a fundamental role in shaping service excellence and institutional legitimacy. Drawing on a public administration perspective, this study examines how governance dimensions influence academic service performance in higher education. This research employs a qualitative descriptive-analytical design. Data were collected through in-depth interviews, document analysis, and institutional observations involving university leaders, academic administrators, faculty members, and students. The analysis focuses on governance dimensions—transparency, accountability, participation, effectiveness, and responsibility—and their integration into institutional systems such as performance management, quality assurance, and digital infrastructure. The findings reveal that governance frameworks are formally established through regulations and digital systems; however, their operational integration remains uneven. Transparency improves service reliability when supported by consistent information management, while accountability mechanisms tend to emphasize procedural compliance rather than performance-based evaluation. Stakeholder participation is institutionalized but largely consultative. The study concludes that service excellence in higher education is a governance-driven outcome that requires systemic alignment between governance principles, institutional capacity, and performance management processes. Strengthened governance integration enhances service reliability and institutional legitimacy.

This study examines the application of double integrals in calculating the volume of cylindrical concrete piles as a basis for estimating material requirements in building foundation structures. The volume calculation was carried out using a double-integral approach in polar coordinates for three pile segments with lengths of 4 m, 3.9 m, and 4 m, each having a diameter of 60 cm. The results were then validated using the standard geometric formula to ensure consistency and mathematical reliability. The obtained concrete volume was subsequently used to estimate material needs based on a 1:1.5:3 mix proportion consisting of cement, sand, and gravel. The findings indicate that double integrals can be effectively applied to generate accurate estimations of both volume and material requirements, supporting logistical planning in construction. This approach also highlights the strong connection between mathematical concepts—particularly multivariable calculus—and practical applications in civil engineering. Furthermore, the study emphasizes that double integrals may serve as a relevant alternative when structural modeling requires deeper analytical exploration or validation beyond conventional geometry. Therefore, the implementation of double integrals not only reinforces theoretical understanding but also enhances precision in evaluating structural components within building foundation planning.

This study aims to analyze the effect of the implementation of the Intelligent Tutoring Sistem (ITS) based on Artificial Intelligence (AI) on improving the understanding of mathematical concepts, especially in fractional and basic geometry materials, in Class V students of SD Negeri 2 Badran, Temanggung Regency. The research method used was a quasiexperimental experiment with a Non-equivalent Control Group Design. The research sample consisted of 48 students who were divided into two groups, namely the experimental group (n=24) who received learning with the help of AI-based ITS, and the control group (n=24) who received conventional learning with lecture methods and practice questions. The research instrument is in the form of a test of understanding of mathematical concepts that has been validated by experts and tested for reliability. Data were analyzed using parametric statistical tests of the Independent Sample t-test and N-Gain Score to measure the improvement. The results showed that there was a significant difference in understanding of mathematical concepts between the experimental group and the control group. The average post-test score of the experimental group (82.45) was significantly higher than that of the control group (70.12) with a p< value of 0.05. N-Gain analysis showed that the improvement in conceptual understanding in the experimental group was in the "moderate" category (g=0.56), while the control group was in the "low" category (g=0.32). These findings indicate that AI-based ITS is effective in improving students' understanding of mathematical concepts. The advantages of the system lie in its ability to provide instant feedback, personalize materials according to learning pace, and present interactive materials, thus helping to better construct students' conceptual understanding. It is recommended that schools consider the integration of ITS technology as a supplementary tool in mathematics learning at the elementary level.

Mining operations rely heavily on the performance and reliability of heavy equipment used in the production process. One of the most important hauling units in open-pit mining is the dump truck, which functions to transport overburden and coal from the mining front to disposal areas. Due to high operational intensity, dump trucks require effective maintenance management to ensure equipment reliability and reduce unexpected downtime. However, maintenance activities are often carried out based only on routine service schedules without analytical planning based on historical data. This study aims to analyze the implementation of forecasting methods in maintenance management to improve the effectiveness of dump truck maintenance planning in mining operations. The research was conducted during field work practice at PT Putra Perkasa Abadi Jobsite BIB, Tanah Bumbu, South Kalimantan. The data used were historical maintenance records of dump truck units obtained from the maintenance department. The research method used a quantitative approach with time series forecasting analysis to identify maintenance patterns and estimate future maintenance needs. The results show that forecasting-based maintenance planning can help companies predict maintenance requirements more accurately and prepare maintenance resources more efficiently. Furthermore, the implementation of forecasting methods can reduce unexpected equipment failures and support operational efficiency in mining activities.

The 2018 financial statement manipulation case of PT Garuda Indonesia Tbk revealed a violation of the ethical principles of the public accountant profession and weak auditor independence in detecting irregularities in revenue recognition. This incident has created an urgency to empirically test the extent to which professional ethics influence auditor independence in preventing financial statement manipulation. This study aims to analyze the influence of public accountant professional ethics on auditor independence using quantitative research methods. Data were obtained through variable measurements using structured instruments which were then processed and analyzed using the SPSS application through validity tests, reliability tests, simple linear regression analysis, and t-tests to determine the significance of the influence between variables. The results of the study indicate that professional ethics has a positive and significant influence on auditor independence, where the stronger the application of ethical principles, the higher the level of auditor independence in carrying out their professional duties. The conclusion of this study confirms that the consistent application of professional ethics is a fundamental factor in maintaining auditor objectivity and preventing the recurrence of financial statement manipulation cases such as those that occurred at PT Garuda Indonesia Tbk.

This study aims to explore public perceptions of urban spatial planning policies in addressing the impacts of urbanization in Medan. Data were collected using a 15-item Likert-scale questionnaire and analyzed descriptively, using proxy factor analysis (PCA), and using cross-demographic difference tests. The initial sample size consisted of seven respondents. The results showed strong recognition of the impacts of urbanization, such as increasing land pressure, slums, and limited infrastructure. The majority of respondents also supported data-driven spatial planning to address these issues. However, there were doubts about the effectiveness of government land conversion controls, which were considered suboptimal. Nevertheless, these results demonstrate the importance of public participation in urban planning. The internal reliability of this study was relatively low, with a Cronbach's alpha value of -0.112, making the findings exploratory in nature. The researchers recommend that this study be conducted with a larger sample size and further testing to obtain more representative and valid results, as well as to delve deeper into public perceptions of urban spatial planning policies.

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

The COVID-19 pandemic significantly affected the financial performance of PT Kereta Api Indonesia (Persero), as reflected in the shift from profit in 2020 to a substantial pre-tax loss in 2021. This change had direct implications for the company’s tax components, particularly current tax and deferred tax, in accordance with PSAK 46 on Income Taxes. This study aims to analyze the changes in current tax and deferred tax between the two reporting periods and to examine the role of deferred tax benefits in reducing the company’s net loss. The research employs a quantitative descriptive approach with a comparative analysis method using secondary data from the company’s interim consolidated financial statements. The findings indicate that in 2021 the company recognized a deferred tax benefit that converted total income tax into a net tax benefit, thereby reducing the company’s net loss by approximately 15.8 percent. These results demonstrate that deferred tax does not merely arise from temporary differences but can function as an instrument of loss mitigation during periods of financial distress. The implications of this study highlight the importance of accurate application of PSAK 46, especially in times of economic downturn, and emphasize the need for realistic assessments of future taxable profits to ensure the reliability of deferred tax asset recognition.

This study aims to analyze the influence of product quality on consumer satisfaction in the Kipang Sisera Panyabungan Business. The research method used is a quantitative approach with data collection techniques through observation, interviews, and questionnaire distribution to 96 respondents who are customers of Kipang Sisera. The data obtained were analyzed using validity, reliability, and regression analysis tests with the help of SPSS 25. The results of the validity test showed that all items in the questionnaire were valid with a rtable value of > (0.1689), while the results of the reliability test using Alpha Cronbach showed a value of > 0.60, which means that the research instrument is trustworthy. The results of the partial regression test (t-test) showed that product quality had a positive and significant effect on consumer satisfaction with a significance value of 0.000 < 0.05. The determination coefficient (R²) test showed that product quality contributed 62.9% to consumer satisfaction, while the rest was influenced by other variables that were not studied in this study. These findings indicate that improving product quality, both in terms of taste, packaging, and quality consistency, is very important in maintaining consumer satisfaction and the sustainability of Kipang Sisera's business in the midst of market competition.

This study aims to examine the quality of public services at the Population and Civil Registration Office (Disdukcapil) of Pohuwato Regency using the SERVQUAL model, which comprises five dimensions: Tangibles, Reliability, Responsiveness, Assurance, and Empathy. This research was conducted in response to persistent public complaints regarding slow service processes, long queues, limited facilities and infrastructure, network disruptions, as well as insufficient information and uncertainty concerning service completion time. The study employed a descriptive qualitative approach through observation, interviews with Disdukcapil employees and community members, and documentation. The findings indicate that the quality of public services has not yet been optimal. The Tangibles dimension remains weak due to inadequate service facilities. Reliability has not been fully achieved as a result of delays in document issuance and system disruptions. Responsiveness among service officers is inconsistent in addressing public complaints. In terms of Assurance, officers demonstrate adequate professionalism; however, transparency in procedures and service timelines remains inconsistent. Meanwhile, in the Empathy dimension, attention to the community particularly vulnerable groups still needs improvement. Overall, the public services provided by Disdukcapil Pohuwato have not fully met community expectations and require improvements in facilities, staff competence, and the effectiveness of service systems.

In an increasingly competitive workplace, employee performance is one of the important factors determining a company’s success. PT Gadjahmada Nusantarajaya, a company engaged in the service and trade sector, faces the challenge of maintaining and improving its employees’ performance. Internal factors such as organizational culture, work communication, and work discipline are thought to have a significant influence on employee performance. Based on this, this study was conducted to determine the influence of organizational culture, work communication, and work discipline on employee performance at PT Gadjahmada Nusantarajaya. The research questions in this study are: (1) Does organizational culture influence employee performance? (2) Does work communication influence employee performance? (3) Does work discipline influence employee performance? and (4) Do organizational culture, work communication, and work discipline simultaneously influence employee performance at PT Gadjahmada Nusantarajaya? This research is quantitative. Data were obtained through primary data collected using a questionnaire, as well as secondary data from company documents. The study population was all 49 employees of PT Gadjahmada Nusantarajaya. The sampling technique used saturated sampling; thus, the entire population was used as the research sample. Data analysis used validity tests, reliability tests, classical assumption tests, multiple linear regression analysis, and hypothesis tests (t-tests and F-tests). The results showed that partially (t-tests) the variables of organizational culture, work communication, and work discipline had a significant effect on employee performance. Simultaneously (F-tests), these three variables also had a significant effect on employee performance at PT Gadjahmada Nusantarajaya.

This study aims to analyze and test the mediating role of job satisfaction in the relationship between career development and job stress on employee loyalty at BPR Agung Sejahtera. This study used a quantitative methodology to collect primary data from 90 employees of BPR Agung Sejahtera through questionnaires. Partial Least Squares (PLS) with Structural Equation Modeling (SEM) was used to test the relationship between latent variables. Data testing included validity, reliability, bootstrapping , Adjusted R-Square, Path Coefficient, and Specific Indirect Effects . The results showed that career development had a positive and significant effect on job satisfaction. Conversely, job stress was found to have a negative and significant effect on job satisfaction. Career development also had a positive and significant effect on employee loyalty. Job stress had a negative and significant effect on employee loyalty. Job satisfaction had a positive and significant effect on employee loyalty. However, the mediating role of job satisfaction in the relationship between career development and job stress on employee loyalty proved to be insignificant. This finding indicates that employee loyalty is more influenced by good career development and low levels of job stress than the mediating role of job satisfaction. Overall, these findings conclude that managing career development and work stress through job satisfaction is crucial for increasing employee loyalty. It is hoped that these research findings will help BPR Agung Sejahtera's management better manage its human resources to achieve the company's goals.

The use of protective equipment in the form of helmets is an important aspect of ensuring motorcycle rider safety. However, violations of helmet usage still frequently occur and are difficult to monitor continuously. This study proposes a real-time helmet detection system using the YOLOv8 object detection method. The YOLOv8n model was trained using a helmet and no-helmet image dataset that underwent data augmentation to improve the model’s robustness against variations in environmental conditions. The system was implemented using the Python programming language with the support of the Ultralytics and OpenCV libraries. The system input was obtained from a webcam with a resolution of 640×640 pixels, where each video frame was processed in real time to detect the Helmet and No Helmet classes. The system displays bounding boxes and class labels in real time and is equipped with a violation duration calculation mechanism. When a no-helmet condition is detected continuously, the system generates pop-up alerts and automatic notifications via the Telegram application. The experimental results show that the system is capable of detecting helmet usage and no-helmet violations in real time with stable performance. The integration of violation duration calculation helps reduce momentary detection errors and improves the reliability of identifying valid violations

In the contemporary digital marketplace, consumers increasingly face diverse product choices and brand communications. Understanding the mechanisms through which product quality and brand perception influence customer loyalty remains critical for competitive advantage. The mediating role of customer trust in this relationship has received limited empirical attention within Indonesian market contexts. This research analyzes the direct and indirect effects of product quality and brand perception on customer loyalty, with customer trust as a mediating variable, using Partial Least Squares Structural Equation Modeling (PLS-SEM) methodology on 103 respondents. A quantitative cross-sectional survey design was employed, collecting data via Likert-scale questionnaires (1-5) with 15 measurement items across four latent constructs: Product Quality (5 items), Brand Perception (4 items), Customer Trust (3 items), and Customer Loyalty (3 items). Data analysis utilized PLS-SEM via SmartPLS 3.0, including assessment of measurement model validity (outer model), structural relationships (inner model), and mediation effects through bootstrapping (5000 iterations). The outer model demonstrated adequate validity with 12 of 15 indicators loading above 0.7, and all constructs meeting composite reliability (CR > 0.7) and average variance extracted (AVE > 0.5) criteria. The inner model revealed that product quality significantly influenced customer trust (β = 0.624, p < 0.001), while brand perception showed no significant direct effect (β = 0.045, p = 0.767). Customer trust strongly predicted loyalty (β = 0.650, p < 0.001). Product quality demonstrated a significant indirect effect on loyalty through trust (β = 0.405, p < 0.001), indicating full mediation. The model explained 43.5% of trust variance and 42.2% of loyalty variance. Product quality emerged as the dominant antecedent of customer trust and loyalty, while brand perception did not significantly contribute. Trust served as the critical mechanism translating quality into loyalty. These findings suggest that companies should prioritize quality assurance and consistent delivery over brand marketing campaigns for sustainable loyalty development. The research contributes to mediation theory in consumer behavior and provides actionable strategic guidance for practitioners in emerging markets.