ABSTRACT Background Vulval Lichen Sclerosus (VLS) is a chronic inflammatory condition requiring lifelong management to prevent severe complications including malignancy. A shared care model between general practitioners (GPs) and public non‐GP specialists (dermatologist or gynaecologist) for patients with stable LS would alleviate the demand on public vulval services. This study aims to assess GPs' level of comfort in performing vulval examinations for patients with VLS which would underpin the feasibility of shared care. It also explores GPs' level of confidence in vulval health, preferences for future vulval training and perspectives on rapid access pathways which would enable a successful shared care approach. Method Online survey invitations were sent to GPs across South Australia via email, GP social media groups and health network newsletters between October and December 2024. Survey questions focused on GPs' prior training, confidence in assessing LS, preferences for additional training and referral pathways. Descriptive statistics were used to describe survey responses while Chi‐squared and Fisher's exact test statistical methods were used to identify associations between explanatory variables and outcome variables. Additional binary logistic regression analysis was used to model odds ratios, confidence intervals and corresponding p ‐values for associations between the dependent and independent variables. Results A total of 132 complete responses were received. 74% of GPs were comfortable undertaking vulval examinations for patients with VLS while 26% were not. Stage of training, scope of practice and previous additional training in women's health had statistically significant associations ( p < 0.05) with this outcome. Detecting malignancy and performing vulval biopsy were skills with lower reported levels of confidence. Most GPs desired future upskilling in vulval health, particularly in the form of specialist led face‐to‐face and webinar‐based education, while the most preferred rapid access pathway was an e‐referral (or email) with a phone call to the specialty registrar as needed. If additional training, resources and rapid access pathways were provided, most GPs (91%) were inclined to participate in shared care. Conclusion Most GPs in this study were comfortable in undertaking vulval examinations for patients with VLS and expressed an inclination to participate in shared care. Further efforts are required to provide GPs with additional training and rapid access pathways for specialist input to facilitate a safe and effective shared care model.

The article examines the problem of coordinating an enterprise’s business processes. It is substantiated that traditional modeling approaches do not ensure a comprehensive consideration of interdependencies between operational, investment, innovative, and financial processes. Methodological approaches have been developed for constructing an integrated model that ensures optimal interaction among the enterprise’s core, auxiliary, supporting, and managerial business processes. A block-based model structure is proposed, in which business processes are represented by corresponding production functions and cost functions. The use of recurrent dependencies allows for the alignment of resource flows and financial constraints. To determine optimal managerial decisions, the use of statistical testing methods and scenario modeling is proposed. The model can serve as a basis for developing decision support systems that enhance management efficiency under uncertainty.

The widespread use of social media has made assessing users’ tastes and preferences increasingly complex and important. At the same time, the rapid dissemination of misinformation on these platforms poses a critical challenge, driving significant efforts to develop effective detection methods. This study offers a comprehensive analysis leveraging advanced Machine Learning (ML) techniques to classify news articles as fake or true, contributing to discourse on media integrity and combating misinformation. The suggested method employed a diverse dataset encompassing a wide range of topics. The method evaluates the performance of five ML models: Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Decision Trees (DTs), and Support Vector Machines with Radial Basis Function (SVM-RBF) kernels. The presented methodology included thorough data preprocessing, detailed parameter tuning during model training, and robust statistical analyses to ensure fair and accurate performance comparisons. The results demonstrate that the combination of Term Frequency-Inverse Document Frequency (TF-IDF) with ANN and CNN achieved the highest accuracy of 99.13%, showcasing the effectiveness of these approaches in text-based news classification. The LSTM model followed closely with an accuracy of 98.59%, while the DT and SVM-RBF models achieved accuracies of 85.67% and 90.22%, respectively. These findings highlight the superior performance of deep learning (DL) models when combined with effective feature extraction techniques such as TF-IDF. The models offer practical utility and show promising potential for integration into editorial workflows to facilitate pre-publication news verification. Furthermore, statistical test methods such as Analysis of Variance (ANOVA) and Tukey’s Honestly Significant Difference (HSD) tests are also performed. The obtained results clarify significant performance differences among the evaluated models, highlighting their unique capabilities and comparative strengths in the context of fake news detection. Hence, the presented study reinforces the importance of artificial intelligence based tools in promoting media reliability and provides a foundation for future advancements in automated misinformation detection systems.

This study focuses on a problem of probabilistic modeling of steady-state thermal-hydraulic conditions of a tree-configured pipeline network, which occur under the influence of random external factors. An original algorithm designed to ensure acceptable accuracy and high computational efficiency is proposed to solve the probabilistic modeling problem. Alongside the models introduced in the study, the main tenets of the proposed approach are presented and finite formulas for calculating the main statistical characteristics (means, variances, and covariances) of all operating parameters (flow rates, pressures, and temperatures) are explained. Numerical calculations demonstrate the superior capabilities of the proposed algorithm compared to other alternative methods, including matrix stepwise and statistical testing methods, in terms of both accuracy and speed

Purpose This study aims to enhance estimation accuracy and address uncertainty in measurement data by deriving confidence intervals for the Six Sigma Quality Index based on statistical inference results. A fuzzy testing method is then proposed, utilizing confidence intervals as an evaluation framework for process quality. Design/methodology/approach First, confidence intervals for the Six Sigma Quality Index are derived based on the statistical inference results. Next, these confidence intervals are employed to construct a fuzzy estimation of the index. Finally, fuzzy numbers and their membership functions are developed to create a fuzzy hypothesis testing model using the derived confidence intervals. Findings The fuzzy testing method proposed in this study is grounded in the use of confidence intervals. It not only mitigates the risk of misjudgment caused by sampling errors but also offers a more comprehensive approach compared to traditional statistical testing methods. Originality/value The Six Sigma Quality Index functions not only as a bridge between businesses and customers but also as a tool for internal engineers to assess and analyze processes and propose improvements. However, since the index involves unknown parameters, sampled data are employed for estimation. To improve estimation accuracy and address uncertainty in measurement data, this paper derives the confidence intervals of the Six Sigma Quality Index based on statistical inference results. Building on these results, it proposes a fuzzy testing method that utilizes confidence intervals as a novel approach for evaluating process quality.

Wide-angle X-ray diffraction is a crucial technique for probing the nanoscale texture and strain gradient of nanofiber-based composite materials, particularly in determining the 3D orientation distribution of crystalline nanofiber networks. However, extracting 3D orientation information of nanofibers from diffraction patterns remains a significant challenge, especially when dealing with diffraction patterns resulting from multiple fiber sets. Here we introduce Restrfcn, an end-to-end framework which integrates a transformer encoder with a fully connected network through residual connection. We demonstrate its capability in extracting fiber orientation parameters even when the number of nanofiber sets is a variable. To eliminate ineffective neurons in the network, which can simplify the architecture and enhance the model's fitting performance, the Restrfcn model is optimized by using a statistical hypothesis testing method. The deployment of Restrfcn has significant potential for providing real-time data analysis in high-throughput and multi-dimensional synchrotron diffraction experiments.

Objective. Development of a methodology for assessing the reliability of electronic components of very large-scale integrated circuits using stochastic modeling by the Monte Carlo method for failure prediction and reliability parameter optimization. Method. The Monte Carlo statistical testing method was applied to model the degradation processes of VLSI electronic components. A mathematical model was developed that takes into account the influence of temperature, humidity, mechanical stresses and electrical loads on reliability parameters. 10 simulation iterations were performed. Result. Statistical distributions of failure-free operation time for various types of VLSI components were obtained. Temperature influences contribute most to reliability degradation (52.0%), humidity – 29.4%, mechanical stress – 22.8%, and electrical loads – 12.7%. The model demonstrates a prediction accuracy of 95.5% when compared with experimental data. Conclusion. The Monte Carlo method provides effective modeling of the reliability of VLSI electronic components taking into account multiple impact factors. The proposed methodology allows optimizing design parameters and operating modes to improve reliability by 12-15%.

Child malnutrition remains a very important public health issue in Indonesia. Malnutrition is a condition of deficiency in energy and essential nutrients that can lead to impaired physical growth, mental development, and an increased risk of mortality in children. The prevalence of malnutrition among toddlers in Indonesia is still quite high and shows disparities between regions, especially in provinces with high poverty rates. One province of concern is North Sumatra, which, according to data from the Ministry of Health, has had a significant incidence of malnutrition in the last five years. This condition was exacerbated by the emergence of the COVID-19 pandemic at the end of 2019, which has had a major impact on various sectors of life, including family health and economy. The pandemic caused significant disruptions to primary healthcare systems, including a decrease in posyandu activities, immunizations, and monitoring of children's nutritional status. The decline in household income during the pandemic made it difficult for families to meet their balanced nutritional food needs. A UNICEF study showed an increased risk of acute malnutrition in children during the pandemic, especially in previously vulnerable areas. To measure the impact of the COVID-19 pandemic on the incidence of child malnutrition, a statistical approach that can compare data before and after the pandemic is needed. This study aims to analyze the difference in the incidence of child malnutrition before and after the COVID-19 pandemic in North Sumatra Province using the Wilcoxon test method. Using the Wilcoxon Signed-rank Test statistical method, a comparative analysis was performed between the medians of the data from 2018 and 2023. The results of the study showed that there was a difference between the medians of the two data sets.

Anomaly detection—identifying deviations from Standard Model predictions—is a key challenge at the Large Hadron Collider due to the size and complexity of its datasets. This is typically addressed by transforming high-dimensional detector data into lower-dimensional, physically meaningful features. We tackle feature extraction for anomaly detection by learning powerful low-dimensional representations via contrastive neural embeddings. This approach preserves potential anomalies indicative of new physics and enables rare signal extraction using novel machine-learning-based statistical methods for signal-independent hypothesis testing. We compare supervised and self-supervised contrastive learning methods, for both Multilayer perceptron-and transformer-based neural embeddings, trained on the kinematic observables of physics objects in LHC collision events. The learned embeddings serve as input representations for signal-agnostic statistical detection methods in inclusive final states. We achieve significant improvement in discovery power for both rare new physics signals and rare Standard Model processes across diverse final states, demonstrating its applicability for efficiently searching for diverse signals simultaneously. We study the impact of architectural choices, contrastive loss formulations, supervision levels, and embedding dimensionality on anomaly detection performance. We show that the optimal representation for background classification does not always maximize sensitivity to new physics signals, revealing an inherent trade-off between background structure preservation and anomaly enhancement. We demonstrate that combining compression with domain knowledge for label encoding produces the most effective data representation for statistical discovery of anomalies.

ABSTRACT Mechanical ventilators are invasive therapies in the form of assistive devices that take over respiratory functions, invasive breaths. Besides being used as a breathing aid, the use of mechanical ventilation can certainly cause complications for clients, one of which is Ventilator Associated Pneumonia (VAP). VAP is a lung infection caused by the use of a mechanical ventilator for more than 2 days. The prolonged duration of mechanical ventilator use is suspected to be one of the main factors leading to the occurrence of VAP. To determine the relationship between the duration of mechanical ventilator use and the incidence of Ventilator Associated Pneumonia (VAP) in the ICU of RSUD Karawang.The research method used is cross-sectional. The population used consists of patients who are on mechanical ventilation in the Intensive Care Unit (ICU) and have met the inclusion and exclusion criteria from January to November 2024, totaling 84 respondents. The sample size of 70 people was determined using the Slovin formula. The sampling method used is the consecutive sampling method. The statistical test method uses univariate analysis and bivariate analysis using the Chi-Square test. Out of 70 samples studied, there were 37 patients who were not affected by vap and 33 patients who were affected by vap. The correlation test yielded an Asymptotic Significance (2-sided) value of 0.045, thus the Asymptotic Significance (2-sided) value < α (0.05). There is a relationship between the duration of mechanical ventilator use and the incidence of Ventilator Associated Pneumonia (VAP) in the ICU of RSUD Karawang. Keywords: Ventilator, Ventilator Associated Pneumonia (VAP), ICU ABSTRAK Ventilator mekanik merupakan terapi invasive berupa penggunaan alat bantu yang bertugas mengambil alih fungsi pernapasan, nafas invasif. Selain digunakan sebagai alat bantu nafas, penggunaan ventilasi mekanik tentu saja dapat menimbulkan komplikasi pada klien salah satunya Ventilator Associated Pneumonia (VAP). VAP merupakan penyakit infeksi pada paru-paru yang disebabkan oleh penggunaan ventilator mekanik lebih dari 2 hari. Durasi lama penggunaan ventilator mekanik disinyalir menjadi salah satu faktor utama terjadiya VAP. Untuk mengetahui hubungan lama penggunaan ventilator mekanik dengan kejadian Ventilator Associated Pneumonia (VAP) di ruang ICU RSUD Karawang.Metode penelitian yang digunakan adalah cross sectional. Populasi yang digunakan adalah pasien yang menggunakan ventilator mekanik di Intensive Care Unit (ICU) yang sudah memenuhi kriteria inklusi dan eklusi dari bulan Januari-November tahun 2024 sebanyak 84 responden. Jumlah sampel sebanyak 70 orang ditentukan menggunakan formula Slovin. Metode sampling yang digunakan adalah metode consecutive sampling. Metode uji statistik menggunakan analisis uji univariat dan analisis bivariat menggunakan uji Chi-Square.Dari 70 sampel yang diteliti terdapat pasien yang tidak terkena vap sebanyak 37 orang dan yang terkena vap sebanyak 33 orang. Uji korelasi didapatkan hasil nilai Asymptotic Significance (2-sided) sebesar 0,045 sehingga nilai Asymptotic Significance (2-sided) < α (0,05).Terdapathubungan antara lama penggunaan ventilator mekanik dengan kejadian Ventilator Associated Pneumonia (VAP) di Ruang ICU RSUD Karawang. Kata Kunci: Ventilator, Ventilator Associated Peneumonia (VAP), ICU

ABSTRACT The process of inferring the intention of conversational implicatures involves the interpretation of the speaker’s sentiment. However, the relationship between implicatures and sentiments has not been clear enough, and there is no research explaining the relationship between the response orientation of conversational implicature and sentiment score. Therefore, based on the dialogue snippets of conversational implicature, this paper uses automatic sentiment analysis, statistical testing and other methods to identify the statistical dependencies between implicatures and sentiments, and compare the sentiment scores of implicatures and literal meanings. The results show that the response orientation of implicature has a significant impact on the sentiment score, and its source only contains response utterance. In addition, within the response utterance, whether there is a significant difference between the sentiment scores of implicature and literal meaning is related to the selected algorithms of sentiment analysis. The sentiment lexicon-based method like Pattern cannot distinguish the sentimental difference between implicature and literal meaning, but the sentiment score obtained by the VADER-based method that considers grammatical and syntactical heuristics has significant differences in implicatures and literal meanings. Finally, the methodological implications of the experiments and results for the development of computational pragmatics are provided in this paper.

To optimize the cost structure of enterprises, improve decision-making in the production process, and enhance production and quality control decisions, this study comprehensively considers factors such as the procurement cost of finished products, inspection costs, replacement losses, disassembly, and assembly costs. By employing statistical inference, hypothesis testing, genetic algorithms, dynamic programming, and other methods, along with collected data and materials, a three-stage multi-process, multi-component production optimization decision-making model is established, covering component inspection, finished product inspection, and defective product disassembly. A sampling inspection model was implemented using Python programming, enabling enterprises to effectively control component quality. This model provides the optimal decision-making solution for the production process.

Entrepreneurship is considered an important mechanism for improving the Indonesian economy through job creation, especially for young entrepreneurs, however, interest in entrepreneurship among young people, especially students, is still quite low, while in the current digital era, social media has become an important platform that influences the mindset and behavior of the younger generation, including in the context of entrepreneurship. For this reason, this research aims to analyze the influence of social media use and entrepreneurial motivation on interest in entrepreneurship among Lampung University students. The method used in this research is a quantitative approach, where data is collected through questionnaires distributed to 150 students from various departments at the University of Lampung. The data that has been collected is then analyzed using IBM SPSS version 25 software and testing the hypothesis using the t statistical test method. The results of this research support the hypothesis that has been established, namely that the use of social media has a significant positive effect on business resilience, and entrepreneurial motivation has a significant positive effect on entrepreneurship interest.

The low utilization of campus portals as a means of independent learning by students, even though the portal has been provided as part of the digitalization efforts of higher education. Therefore, it is necessary to explore further how various variables in the Technology Acceptance Model (TAM), such as perceived ease of use, perceived usefulness, attitude toward use, and intention to use, influence the level of acceptance and utilization of campus portals for independent learning. The Independent Campus Portal uses the variables Perceived Ease of Use (PEU), Perceived Usefulness (PU), Attitude Toward Using (ATU), Behavioral Intention to Use (BIU), and Actual System Use (ASU) by applying the Technology Acceptance Model (TAM). Data collection for this study was carried out using a questionnaire. The sample used was 61 students majoring in Physics Education. The statistical testing methods used were statistical tests for normality, homogeneity, validity, reliability, and hypothesis testing using the t-test with the SPSS program. The results of this study indicate that of the six hypotheses proposed, all six hypotheses were accepted and had a significant influence. This research demonstrates the need for improved system optimization and technical support services to enable the portal to function more effectively and efficiently in supporting the Independent Campus program. The implications of this research demonstrate the importance of developing and optimizing the campus portal as a self-directed learning tool that is responsive to the needs and perceptions of users, particularly students.

The El Nino phenomenon has an influence on the production of oil palm plants at PT Perkebunan Nusantara I Regional 7, Bandar Lampung, Lampung. What is the impact on oil palm production due to the El Nino phenomenon? What is the impact of the El Nino phenomenon on the average fruit bunch weight of oil palm fruit bunches? To understand the impact on oil palm production and the average fruit weight of oil palm fruit caused by the El Nino phenomenon. The design of this research is quantitative, using the parametric statistical test method Independent Sample T Test analysis with a confidence level of 95%, the data used is data on oil palm plant productivity, average bushel weight, rainfall and total bunches from 2012 to 2022.The results of the analysis, after carrying out the Independent Sample T Test analysis, showed that El Nino had an effect on the productivity of oil palm plants with a significance value of 0.009. El Nino also had an effect on the average bushel weight, a significance result of 0.00 was obtained. EL Nino has an effect on total oil palm bunches, based on Independent Sample T Test analysis, a significance of 0.00 was obtained. Because the Sig value is 0.05, H0 is rejected, with a confidence level of 95% indicating that there is a correlation between oil palm production results and the El Nino phenomenon. El Nino also influences rainfall which fluctuates during a prolonged dry season.

Multilevel thresholding image segmentation plays a crucial role in various image processing applications. However, achieving optimal segmentation results often poses challenges due to the intricate nature of images. In this study, a novel metaheuristic search algorithm named Weighted Chimp Optimization Algorithm with Fitness–Distance Balance (WChOA-FDB) is developed. The algorithm integrates the concept of Fitness–Distance Balance (FDB) to ensure balanced exploration and exploitation of the solution space, thus enhancing convergence speed and solution quality. Moreover, WChOA-FDB incorporates weighted Chimp Optimization Algorithm techniques to further improve its performance in handling multilevel thresholding challenges. Experimental studies were conducted to test and verify the developed method. The algorithm’s performance was evaluated using 10 benchmark functions (IEEE_CEC_2020) of different types and complexity levels. The search performance of the algorithm was analyzed using the Friedman and Wilcoxon statistical test methods. According to the analysis results, the WChOA-FDB variants consistently outperform the base algorithm across all tested dimensions, with Friedman score improvements ranging from 17.3% (Case-6) to 25.2% (Case-4), indicating that the FDB methodology provides significant optimization enhancement regardless of problem complexity. Additionally, experimental evaluations conducted on color image segmentation tasks demonstrate the effectiveness of the proposed algorithm in achieving accurate and efficient segmentation results. The WChOA-FDB method demonstrates significant improvements in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM) metrics with average enhancements of 0.121348 dB, 0.012688, and 0.003676, respectively, across different threshold levels (m = 2 to 12), objective functions, and termination criteria.

The article is devoted to the problem of surface water pollution by pollutants contained in the atmospheric air and getting into water bodies as a result of rainfall and subsequent surface runoff. The authors propose a general scheme of the corresponding calculation for simplified conditions: one source of atmospheric air pollution of constant power, no vertical wind and no background pollution. The calculation scheme consists of three blocks: development of an algorithm for calculating the concentration field of pollutants in the air; determination of the concentration of pollutants in rainwater; accounting for meteorological conditions. It is proposed to calculate the concentration field of a substance in the air for fixed values of wind speed and direction by numerically integrating the turbulent diffusion equation for a stationary case. The corresponding calculation formula is given. To calculate rainwater pollution for fixed horizontal coordinates, the absorption intensity function is introduced into consideration, which generally assumes both physical and chemical absorption. Finally, the mass of the substance getting into the water body is calculated by integrating over the entire catchment area. It is proposed to consider adverse meteorological conditions using the method of statistical tests (the Monte Carlo method). The advantage of this method is that it belongs to non-parametric methods of mathematical statistics, which do not require information on the principle of probability distribution of the random variables under consideration and their functions. Reference data, in particular, the state standard of Ukraine "Building Climatology", can be used as initial data for determining the probability distribution function of wind speed and direction. The obtained distribution functions allow modeling the probability distribution of the pollutant mass entering a water body with rainwater. The developed calculation scheme allows determining the pollution of a water body under the most adverse meteorological conditions.

Background: „Laterality in beach volleyball is mainly manifested in the attacking activities of an individual (serving, spiking). Functional asymmetry affects the player’s movement expression in all movement situations, and laterality is also important for the specialization of individual players.Objective: The study aims to assess the lateral involvement of selected muscles (deltoideus, biceps femoris semitendinosus) and their efficiency in the phases before the spike jump and in the wind-up phase of the spike.Materials and Methods: The research group consisted of 12 female players with an average age 16.17 (SD= 1.5). The main method used for data collection was measurement by Noraxon surface electromyography. We entered the transformed data into Microsoft Excel 365 for Mac. To evaluate the data, we used descriptive (frequency, percentage) and inferential (Shapiro-Wilk test, T-test, Man Whitney U test and correlation) statistical methods. Results: The conclusion of the study is that there is no difference between the right and left sides in the evaluated muscle groups (deltoideus= 0.206; biceps femoris=0.569; semitendinosus=0.792). An interesting finding of the study is the strong correlation between the left deltoideus and the right biceps is interesting (0.812). The efficiency is higher in the wind-up phase of all the assessed muscles. The highest difference in efficiency is in the biceps femoris on the left leg, where the difference in efficiency of engagement in individual phases is over 25%. Conversely, the lowest difference is for the left semitendinosus (5.5%).Conclusions: The study confirms that the effectiveness of muscle activity is more efficient in the wind-up phase, at the same time we add that in this phase the muscles work more symmetrically.

Abstract The American Society of Testing and Materials (ASTM), D1990 details standard practices for establishing and maintaining design values for dimension lumber. Included in this standard are statistical methods for detecting changes in dimension lumber properties over time. A characteristic (or design) value, also known as the allowable property, is published for each lumber property, and this value must be updated whenever changes in the resource warrant. Currently ASTM D1990 calls for the use of the Wilcoxon Rank Sum test (also known as the Mann-Whitney test) to detect changes in these properties. In essence, this test is a two-sample test designed to detect changes in the underlying populations from whence the samples were drawn. In this work, FPL researchers recommend, with justification, that this practice be revised to the use of one-sample tests that focus on detecting disparities between the current resource properties and the corresponding currently accepted design values. We detail the tests that are recommended for properties whose design values are (based on) either the mean or some quantile. We further examine the impacts, in terms of statistical errors, of these alternative tests in comparison to the current paradigm through simulations using a collection of distributions modeled from actual within-grade softwood lumber modulus of elasticity and modulus of rupture data.

Dysmenorrhea is a condition of pain and cramps experienced by women before they experience menstruation or when menstruation occurs. Physical activity can be done in the form of sports such as jogging. This study aimed to determine the relationship between physical activity, jogging, and the degree of dysmenorrhea in 3rd year female students majoring in Nursing at the Madura State Polytechnic. This study used an analytical correlation method using the Analytic Correctional-Cross Sectional. The population was 25 people, and the sample in this study was 3rd-year female students majoring in Nursing at the Madura State Polytechnic who experienced dysmenorrhea and did physical jogging activities routinely and regularly. Data collection by providing a closed-ended questionnaire for physical jogging activities and a Numeric Rating Scale (NRS) questionnaire for the degree of dysmenorrhea, and analysis using the Spearman Rank test statistical analysis method with α = <0.05. The results obtained a significant value of 0.006, meaning <0.05 means that physical jogging activities are related to the degree of dysmenorrhea, where most female students who experience mild dysmenorrhea do physical jogging activities routinely. The limitations of this study are the relatively small number of samples taken and other factors that can affect the degree of dysmenorrhea, such as stress levels, nutritional status, lifestyle, and family history. Based on the results of the research that has been conducted, it can be concluded that the degree of dysmenorrhea can be reduced if someone routinely does physical activity, in this case by jogging.