Iron deficiency is a common cause of anemia during pregnancy. The low consumption of Fe tablets is related to compliance with consumption. The purpose of this study was to determine the compliance of pregnant women taking iron tablets in the second trimester in Bukittinggi City. This was a qualitative study using a case study method. A purposive sampling technique was used, with the main informant being pregnant women in the second trimester in Baratan Village. Data were collected using interview techniques, observation, and documentation analysis and then validated using data triangulation, observer triangulation, theory triangulation, and method triangulation. The results of this study were analyzed based on data grouping. Testing data assumptions, alternative data explanations, and the results of the study were written. The compliance of pregnant women in taking Fe tablets shows the compliance of pregnant women in the second trimester in taking Fe tablets and how pregnant women know the benefits and impacts of not taking Fe tablets. Researchers know how long pregnant women take Fe tablets, and family support is a reinforcement for pregnant women if they forget when taking Fe tablets. Pregnant women who routinely have ANC will also receive Fe tablets from the midwife who checks for compliance with taking Fe is better when Fe tablets are also available. The opinion of researchers of primigravida pregnant women in the second trimester shows that the first, second, and third subjects are pregnant women in the second trimester who have experience taking Fe tablets. They reported the same complaints, namely nausea and vomiting after taking Fe tablets; even so, they remained obedient and routinely took Fe tablets every day, with and without being reminded by their families, they still routinely took Fe tablets. Some mothers understand the impact of not taking Fe tablets, namely, anemia and bleeding. During the interviews, all mothers said that they routinely checked their pregnancy every month with a midwife accompanied by their husbands.

Wings Air is a low-cost airline in Indonesia and is a subsidiary of the company PT. Lion Grub. The purpose of this research is to determine the influence of service quality and brand image variables on passenger satisfaction with Wings Air at Sultan Muhammad Kaharuddin Airport, Sumbawa, both partially and simultaneously. The method used is a quantitative method with non-probability sampling and purposive sampling techniques distributed through a questionnaire of 100 respondents. This research instrument uses a Likert scale with 22 statements according to the indicators of each variable. This research uses SPSS for descriptive analysis and testing classical assumptions and hypothesis testing, namely multiple linear regression analysis tests. The service quality variable gets a t-count > t-table result of 2,809 > 1.984 with a significance value of 0.006 < 0.05 and the brand image variable gets a t-count > t-table value of 2,313 > 1,984 with a significance value of 0.023 < 0.05. The results of this research indicate that the variables of service quality and brand image have a partial effect on passenger satisfaction. The variables of service quality and brand image simultaneously influence passenger satisfaction with an f-count > f-table value of 10,975 > 3.09 and a significance of 0.000 < 0.05 with an influence size of 16.8%.

This study aims to analyze the role of Islamic education management in fostering religious moderation in schools. Amid the challenges of globalization and technological advancement, Islamic education faces the need to reflect values of moderation, tolerance, and inclusivity. The research method employed is a literature study with a qualitative narrative approach, evaluating previous studies related to this topic. The findings indicate that effective Islamic education management can be a strategic instrument in instilling moderation values through curriculum, policies, and learning practices. However, the implementation of religious moderation still faces obstacles, such as the lack of systematic managerial strategies and institutional challenges. This study recommends the development of a more specific and structured Islamic education management model to support religious moderation in schools. Thus, it is expected to create an inclusive and harmonious educational environment capable of shaping students' moderate and tolerant character.

Technological advancements encourage the culinary business sector to adopt digital systems to improve operational efficiency. One such innovation is a desktop-based food ordering application. Mona Cafe in Medan developed this system to make it easier for customers to place orders without waiting long or writing them down manually. This application allows customers to select menus digitally, making the ordering process more practical and efficient. Additionally, it helps waiters record and manage orders more systematically, reducing errors and speeding up service. This technology also aligns with the digitalization trend, especially in attracting millennials and Gen Z. Besides enhancing customer convenience, this application helps Mona Cafe stay competitive in the growing culinary business. Developed using the waterfall method, the system makes cafe operations more structured and professional. With this innovation, Mona Cafe is expected to improve its service quality and customer satisfaction

This study aims to describe the self-esteem levels of students at Universitas Bhayangkara Jakarta Raya in facing their thesis. Self-esteem is an individual's evaluation of themselves, both positively and negatively, influencing how they tackle academic challenges, including thesis writing. This research employs a descriptive quantitative approach using a survey method, where data were collected through questionnaires distributed to students currently working on their theses. The findings indicate that most students have a moderate level of self-esteem, with a minority categorized as having high or low self-esteem. No significant differences in self-esteem levels were found based on gender. Factors such as social support from peers and academic advisors play a crucial role in enhancing students’ self-esteem during the thesis process. This research provides insights into the importance of self-esteem in academic success and its implications for developing academic support programs in higher education institutions.

This research have intenions to show and understand the legal principles of engagement in agreements when entering into agreements. know about the implementation of the principles of engagement, especially the principle of consensualism in realizing an engagement. The writing of this article applied through the normative legal research method where the author employs a legislative approach and a system of legal concept. The study results indicate that when making an agreement, several principles serve as the foundation of legal proceedings, guiding decisions and actions within the judicial system, as a legal basis this principle can provide an overview of the contract. Numerous guiding principles are available for reference and implementation in various contexts and situations. The principles of association include the principle of freedom of contract, the principle of good faith, the principle of pacta sunt servada, and the principle of consensualism. In implementing consensualism in a contract such as an online buying and selling agreement. Buying and selling online from the point of view of contract law in Indonesia uses Paragraph (1) of Article 18 of the Electronic Information and Transactions Law. There is also a lease agreement where the parties make an agreement. The legal basis for renting a house is explained in Government Regulation no. 44 of 1994, which guarantees legal protection for home owners or tenants. The regulation also explains that a rental agreement can be said to be valid if there is approval or permission from the owner of the dwelling. In accordance with the basic law of renting a house, there are 3 (three) clauses in the house rental agreement

This research aims to analyze GoPay user sentiments on the X social media platform (formerly known as Twitter) using the Naive Bayes Classifier algorithm. Sentiment analysis was conducted to understand user perceptions and satisfaction levels towards GoPay digital payment services based on their shared comments and reviews. Data was collected through a tweet crawling process containing the keyword "GoPay" within a specific period. The research stages included data preprocessing (case folding, tokenizing, filtering, and stemming), sentiment labeling (positive, negative), word weighting using TF-IDF, and classification using the Naive Bayes algorithm. The results showed that from a total of 1,431 analyzed tweets, 797 data contained positive sentiments, and 643 data contained negative sentiments. With a classification accuracy rate reaching 82.94%. The most frequently positively commented factors included ease of use and offered promotions, while the main complaints were related to technical issues and customer service. This research provides insights for GoPay developers to improve services according to user feedback.
 

Human action recognition involves recognizing and classifying actions performed by humans. It has many applications, including sports, healthcare, and surveillance. Challenges such as a limited number of classes of activities and variations within inter and intra-class groups lead to high misclassification rates in some of the intelligent systems developed. Existing studies focused mainly on using public datasets with little focus on real-life action datasets, with limited research on HAR for military obstacle-crossing activities.  This paper focuses on recognizing human actions in an obstacle-crossing competition video sequence where multiple participants are performing different obstacle-crossing activities. This study proposes a feature descriptor approach that combines a Histogram of Oriented Gradient and Region Descriptors (HOGReG) for human action recognition in a military obstacle crossing competition. The dataset was captured during military trainees’ obstacle-crossing exercises at a military training institution to achieve this objective. Images were segmented into background and foreground using a Grabcut-based segmentation algorithm, and thereafter, features were extracted and used for classification. The features were extracted using a Histogram of Oriented Gradient (HOG) and region descriptors from segmented images. The extracted features are presented to a neural network classifier for classification and evaluation. The experimental results recorded 63.8%, 82.6%, and 86.4% recognition accuracies using the region descriptors HOG and HOGReG, respectively. The region descriptor gave a training time of 5.6048 seconds, while HOG and HOGReG reported 32.233 and 31.975 seconds, respectively. The outcome shows how effectively the suggested model performed.

Monkeypox is a zoonotic disease caused by Orthopoxvirus, presenting clinical challenges due to its visual similarity to other dermatological conditions. Early and accurate detection is crucial to prevent further transmission, yet conventional diagnostic methods are often resource-intensive and time-consuming. This study proposes a deep learning-based classification model by integrating Xception and InceptionV3 using feature fusion to enhance performance in classifying Monkeypox skin lesions. Given the limited availability of annotated medical images, data augmentation was applied using Albumentation to improve model generalization. The proposed model was trained and evaluated on the Monkeypox Skin Lesion Dataset (MSLD), achieving 85.96% accuracy, 86.47% precision, 85.25% recall, 78.43% specificity, and an AUC score of 0.8931, outperforming existing methods. Notably, data augmentation significantly improved recall from 81.23% to 85.25%, demonstrating its effectiveness in enhancing sensitivity to positive cases. Ablation studies further validated that augmentation increased overall accuracy from 82.02% to 85.96%, emphasizing its role in improving model robustness. Comparative analysis with other models confirmed the superiority of our approach. This research enhances automated Monkeypox detection, offering a robust and efficient tool for low-resource clinical settings. The findings reinforce the potential of feature fusion and augmentation in improving deep learn-ing-based medical image classification, facilitating more reliable and accessible disease identification.

This research discusses the application of a Content Management System (CMS) as a solution to improve the management of the Afika Store e-commerce website. With CMS implementation, it is hoped that the process of updating content, product management and user interaction can be carried out more efficiently and structured. The research method used is system development with stages of needs analysis, design, implementation and evaluation. The test results show that the CMS implemented is able to improve website performance and make it easier for admins to manage content in real-time.