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This study aims to analyze hematocrit values ​​using a micro method with capillary blood samples in pregnant women with anemia at the Sidodadi Kisaran Barat Community Health Center. This study is a descriptive study with a cross-sectional approach involving 15 pregnant women as respondents. Data collection was carried out through hematocrit laboratory examinations and data collection of respondent characteristics. The results showed that 60% of respondents had hematocrit levels below normal values, while 40% of respondents had hematocrit levels within normal limits. Low hematocrit levels in pregnant women are influenced by several factors, including age, education level, employment status, and level of knowledge about anemia during pregnancy. This condition has the potential to increase the risk of pregnancy complications if not optimally managed. Therefore, it is recommended that health workers continue to increase educational efforts regarding anemia prevention, the importance of regular pregnancy check-ups, and encourage pregnant women to maintain a balanced nutritional consumption pattern and adequate iron intake to support maternal and fetal health.

The rapid development of digital streaming platforms such as Netflix has generated a large volume of content data with diverse characteristics, thereby requiring effective analytical methods to understand emerging patterns and trends. This study aims to classify Netflix content into two main categories, namely movies and television shows, and to analyze genre trends and content characteristics using a data mining approach with the Naive Bayes algorithm. The dataset used in this study is the Netflix Shows dataset, consisting of 8,809 content entries, with the primary features analyzed including genre, rating, and country of production. The research process begins with data exploration and preprocessing stages, including data cleaning, handling missing values, and transforming categorical features to enable effective model construction. Subsequently, the dataset is divided into training and testing sets to objectively and systematically build and evaluate the Naive Bayes classification model. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to assess the model’s ability to accurately distinguish between Netflix content types. The experimental results demonstrate that the Naive Bayes algorithm is able to classify Netflix content into Movie and TV Show categories with accuracy, precision, recall, and F1-score values of 100%, respectively. The confusion matrix indicates that no misclassification occurred, suggesting that genre, rating, and country of production features provide a very clear separation between content classes. These findings indicate that the Naive Bayes algorithm can achieve exceptionally high classification performance with optimal evaluation results. The results further reveal distinct differences in characteristics between movies and television shows based on genre and production attributes. Therefore, this study is expected to contribute to the development of content recommendation systems and strategic content management within the streaming industry.

Queueing systems play an important role in evaluating service performance, especially in small-scale businesses such as barbershops, where fluctuating customer arrival patterns and limited service capacity often lead to long waiting times. This study aims to analyze the performance of barbershop services using the M/M/1 queueing model and an analytical approach based on experimentally tested arrival (λ) and service (μ) rates. The model was selected because it represents a single-server system with Poisson arrivals and exponentially distributed service times, closely matching real barbershop operational characteristics. Using assumed realistic parameters, the analysis shows that when λ = 12 customers per hour and μ = 6 customers per hour, the system becomes unstable with a utilization rate (ρ) exceeding 1, indicating continuous queue growth. Further simulations with increased service rates demonstrate significant improvements: at μ = 15, the system achieves ρ = 0.8 with an average waiting time of 16 minutes, while at μ = 13, the system remains stable but experiences a long waiting time of approximately 55 minutes. These findings emphasize that barbershop performance is highly sensitive to service speed and that even small increases in μ can produce substantial improvements in queue stability and customer waiting times. The study concludes that barbershops must ensure adequate service capacity—either through optimizing service duration, improving worker efficiency, or adding servers—to maintain service quality and enhance customer satisfaction.

Soil contamination by hydrocarbons, pesticides, heavy metals, and complex pollutants is rapidly increasing and degrading essential ecosystem functions. Physical or chemical treatments offer faster results, yet they are often costly, energy-intensive, and risk disrupting soil biological integrity without fully eliminating pollution sources. Microorganism-based bioremediation provides a more sustainable alternative by utilizing microbial metabolism to degrade or immobilize pollutants into less toxic and less mobile forms. This article presents a structured literature review on the roles and applications of microorganisms for bioremediation of contaminated soils, covering comparisons between single isolates and microbial consortia, dominant biological mechanisms, and ecological challenges in field application. A Systematic Literature Review approach was applied, using narrative synthesis and thematic clustering of national and international journals published between 2020 and 2025. The review indicates that single microbial isolates are commonly selected for specific pollutant targets, whereas microbial consortia are preferred for mixed or persistent contaminants due to metabolic synergy that enhances microbial adaptability and stepwise pollutant breakdown in highly polluted soils. Adaptive mechanisms such as EPS production and biofilm formation contribute to microbial resilience under stress and help retain contaminants within the soil matrix. Key challenges identified include inoculum stability under extreme conditions and limited microbial access to pollutants trapped in micro-soil pores. The findings highlight that microbial selection strategies must be tailored to pollutant characteristics and soil environmental conditions, while also emphasizing the potential of biofilm-based systems and organic carriers to support broader field implementation of microbial bioremediation.

This study aims to describe in depth the teacher's strategy in shaping children's behavior according to religious and moral values ​​in Group B at TK Negeri 01 Al-Icmi, PaguyamanPantai District, Boalemo Regency. The study used a qualitative research method. The subjects of this study were twelve children in Group B and one teacher of Group B at TK Negeri 01 Al-Icmi, PaguyamanPantai District, Boalemo Regency. Data were collected through interviews and documentation. Data analysis was carried out by data editing, data presentation, and conclusions. The results of the study indicate that the implementation of the strategy has been carried out optimally. Teachers act as good role models through positive behaviors demonstrated every day, such as getting children into the habit of washing their hands before eating, praying together, and maintaining politeness and mutual respect. These habits are not only imitated by students, but also become part of the school routine that supports the development of children's religious and moral values. In addition, the teacher's behavior formation strategy uses several methods, including storytelling, singing, habituation, and role models that are adapted to the characteristics of early childhood development. This method is applied both in daily routine activities and special programs aimed at strengthening children's understanding of religious and moral values.

This study investigates the relationship between corporate governance characteristics, financial structure, and Enterprise Risk Management (ERM) disclosure in Indonesian non-financial firms. Focusing on manufacturing companies listed on the Indonesia Stock Exchange in 2023, the analysis examines whether board size, the proportion of independent commissioners, and leverage influence the extent of ERM disclosure. Using a quantitative approach, multiple linear regression is applied to secondary data obtained from firms’ annual reports. The findings indicate that board size and the proportion of independent commissioners do not have a significant effect on ERM disclosure, while leverage exhibits a positive and significant relationship. This result suggests that firms with higher debt levels are more inclined to enhance risk disclosure as a mechanism to address information asymmetry and demonstrate accountability to investors and creditors. The study contributes to the ERM and corporate governance literature by providing evidence from an emerging market setting and highlighting the practical importance of financial structure in shaping risk transparency, offering relevant insights for corporate decision-makers and regulators to strengthen sustainable risk management practices.

This study investigates the impact of board characteristics on the financial performance of non-financial companies listed in the JII70 index in Indonesia. Motivated by the ongoing debate on the effectiveness of corporate governance mechanisms in enhancing firm outcomes, particularly within Sharia-compliant markets, this study focuses on three key board attributes: board size, board independence, and female representation on the board. Using a quantitative causal approach and panel data from 25 companies over the period 2020–2023, the study employs a fixed effect model to evaluate the relationship between board structure and financial performance measured by Return on Assets (ROA). The results show that board size has a positive and significant effect on firm performance, indicating that larger boards may enhance oversight capacity and provide broader resources beneficial to strategic decision-making. Conversely, board independence and board female representation do not exhibit significant effects on financial performance, suggesting that their roles may be more symbolic or constrained by institutional and contextual factors in the sampled companies. These findings highlight the importance of understanding corporate governance not merely in structural terms, but in relation to functional effectiveness and contextual maturity. The study offers implications for regulators, companies, and governance reform initiatives, particularly regarding strengthening substantive roles of independent and female commissioners in improving firm performance within Sharia-compliant markets.

Political participation is an important aspect in a democratic state structure, as well as a characteristic feature of political modernization. Political participation influences the legitimacy of society regarding the running of a government. One way of implementing political participation is through General Elections (Pemilu). The aim of this research is to determine the influence of presidential candidate debate broadcasts on multi-platform broadcast media on the political participation of FISIP UMJ student class of 2020. This research has an independent variable, namely presidential candidate debate broadcasts with the dimensions of frequency, attention and duration, and has a dependent variable, namely providing voting rights in elections, lobbying with officials, becoming a member of a political party. This research method uses a survey method by distributing questionnaires online to respondents via Google Form which aims to collect data from a sample of 2020 FISIP UMJ students who actively watch presidential candidate debates and know about political participation, with a total of 66 respondents selected. The data collection tool uses a questionnaire using a Likert Scale. The results of this research show that the presidential debate broadcast has an influence on political participation, which has a value of 0.736 or 73.6%, which means that the presidential debate broadcast influences political participation by 73.6% and the rest is influenced by other factors.

Abortion remains a complex reproductive health issue due to the interplay of multiple interrelated determinants. This study aimed to map the factors influencing the incidence of abortion based on recent scientific evidence. A literature review design was applied by analyzing ten selected articles published within the last five years and retrieved from major scientific databases. The selection process was conducted systematically through title, abstract, and full-text screening based on predefined inclusion criteria. The extracted data included study characteristics, type of abortion, examined determinants, and key conclusions. The synthesized findings indicate that abortion incidence is shaped by a combination of biological, social, and healthcare system related factors. Clinical determinants such as maternal age, endocrine disorders, uterine anatomical abnormalities, obstetric history, anemia, and hypertension play a substantial role in spontaneous and recurrent miscarriage. In contrast, structural factors including income level, contraceptive access, and legal regulations predominantly influence induced abortion. The discussion highlights that abortion should not be viewed as an isolated clinical event, but rather as the cumulative outcome of risks operating across multiple levels of influence. In conclusion, abortion represents a multifactorial phenomenon that requires comprehensive prevention strategies extending beyond medical interventions alone. These strategies should also address healthcare accessibility and broader social conditions. This review contributes to a deeper understanding of the complexity of abortion determinants and provides an evidence-based reference for developing more effective preventive approaches in the future.

A research about study of sandstone slope stability using the Bishop Simplified method in Uu Samarinda has been conducted. This study was conducted to analyze the rebound number values of sandstone slopes, evaluate their stability level, and calculate the safety factor using the Bishop method. The results showed that the rebound number values were 22.34 at point 1, 19.83 at point 2, and 18.07 at point 3. The Uniaxial Compressive Strength (UCS) values at the observation points were 1.90 MPa, 1.62 MPa, and 2.21 MPa, respectively. Geological Strength Index (GSI) evaluation indicated a rating of 80–85, demonstrating intact/massive rock structure, fresh and unweathered rock surfaces, and very good rock quality. Based on the Bishop method analysis, the slope factor of safety in 6.525  with a probability of failure is 0.000%, indicating that the sandstone slope in Ulu Samarinda is highly stable even under external pressures such as heavy rainfall or minor earthquakes. This study provides crucial information on the mechanical characteristics and stability of sandstone slopes in ulu Samarinda, which can serve as a reference for technical planning, geotechnical risk mitigation, and the sustainable development of safe areas.

During the coal stockpiling process, the quality of coal may increase or decrease due to direct exposure to open environmental conditions, which can lead to changes in its characteristics. The longer the coal is stored in an open area, the more it undergoes changes caused by rainfall, heat, and air exposure, resulting in an increase in moisture content and ash content, while the calorific value decreases. Therefore, this research was conducted to determine the optimal coal stockpiling duration at the ROM coal stockpile to ensure that the calorific value does not significantly decrease. Coal sampling was carried out every two days from the initial time of stockpiling. After a two-month stockpiling period, the final coal quality results showed a total moisture of 13.89% (ar), inherent moisture of 15.95% (ad), ash content of 4.59% (ad), volatile matter of 40.3% (ad), and fixed carbon of 39.16% (ad). Based on these results, it can be concluded that the recommended storage duration for MCV-HS type coal at the ROM coal stockpile is 154 days. The laboratory analysis results obtained during the research indicate that the longer the coal is stored, the higher the moisture content and ash content become, while the calorific value continues to decrease. This occurs due to water absorption and oxidation reactions that take place during the coal storage period in the ROM coal stockpile.

This study discusses the introduction of digital signature patterns using the Backpropagation method on Artificial Neural Network (JST) to identify a person's characteristics and potential. The increasing use of digital identities demands a verification system that is more secure, accurate, and adaptive to the variations of each individual's signature. The main problem faced in the signature recognition system is the low level of accuracy when the visual features of the signature have similarities between users, both in terms of shape, size, and stroke pressure. In addition, variations of signatures made by the same individual are also a challenge in the identification process. As a solution, this study implements Principal Component Analysis (PCA) to extract important features from the signature image before the training process using JST. PCA is used to reduce the data dimension so that the learning process becomes more efficient and optimal. A total of 80 signature images were used in this study, consisting of 60 training data and 20 test data. The results showed that the system was able to achieve an accuracy level of 92.5%. These findings prove that the combination of PCA and JST methods is effective in recognizing digital signature patterns and has the potential to be applied to digital security-based biometric identification systems.

Low back pain is a common complaint among pregnant women in the second and third trimesters due to biomechanical and hormonal changes. Non-pharmacological interventions like prenatal exercise are believed to be effective, though evidence from controlled trials is limited. This study aimed to evaluate the effectiveness of prenatal exercise in reducing low back pain intensity. A quasi-experimental pretest–posttest control group design was used with 30 pregnant women (15 in the intervention group, 15 in the control group), selected by purposive sampling. The inclusion criteria were gestational age of 20–36 weeks, no medical contraindications, and willingness to participate. The intervention group performed prenatal exercises twice a week for 8 weeks (45 minutes per session), while the control group received health education without physical exercise. Pain intensity was measured using the Visual Analogue Scale (VAS). Data analysis was conducted using paired t-test, independent t-test/Mann–Whitney, and ANCOVA to control for age, parity, and BMI. Both groups had homogeneous baseline characteristics (p > 0.05). The intervention group’s pain score decreased from 6.9 ± 1.1 to 3.6 ± 1.0 (Δ = -3.3; p < 0.001), while the control group’s pain score decreased from 6.8 ± 1.2 to 6.3 ± 1.1 (Δ = -0.5; p = 0.09). The difference in pain reduction between groups was significant (p < 0.001). ANCOVA confirmed that prenatal exercise was significantly effective after adjusting for confounders (β = -2.8; p < 0.001). Prenatal exercise is an effective, safe, and affordable intervention for reducing low back pain in pregnant women.

The development of value-added corn-based beverages representation an emerging opportunity within the agribusiness sector, particularly for student-led entrepreneurial initiatives seeking to utilize local agricultural resources. This study examines consumer perceptions of Zeagrain corn milk, a sweet-corn-derived drink positioned as a healthy, practical, and affordable alternative within the ready-to-drink market segment. The research aims to identify key attributes influencing consumer interest, evaluate perceived product performance, and assess the potential competitiveness of Zeagrain as a new agribusiness product. Data were collected using an online quantitative survey targeting respondents aged 17–35 years who represent potential urban consumers. The questionnaire measured demographic characteristics, consumption patterns of similar beverages, perceived importance and performance of product attributes, and purchase intention. The findings are expected to provide insights into consumer expectations regarding taste, packaging, nutritional value, and price. The results further serve as empirical input for improving product development strategies and enhancing the market readiness of Zeagrain. Ultimately, this study contributes to strengthening the commercialization prospects of corn-based beverages and supports innovation efforts among young agripreneurs seeking to optimize local commodities.  

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.

Partial Denture (GTSL) is an ideal option for patients with missing teeth, with various materials such as thermoplastic nylon (flexible denture), acrylic resin, and metal. Heat-cured acrylic resin and thermoplastic nylon are the two most commonly used materials for denture bases. Denture bases must have sufficient mechanical strength to withstand chewing forces. Acrylic resin, made from polymethyl methacrylate, has become the gold standard in prosthodontics due to its affordability, ease of fabrication, and satisfactory functional results. However, acrylic resin tends to be rigid, brittle, and may cause soft tissue irritation. As an alternative, thermoplastic nylon offers high physical strength, resistance to heat and chemicals, as well as plastic and flexible properties. However, thermoplastic nylon tends to absorb water, change color, and is difficult to repair. Thermoplastic nylon-based dentures provide comfort, aesthetics, and good marginal adaptation, with less soft tissue irritation compared to acrylic resin dentures. Although both materials have comparable characteristics in terms of retention, stability, and durability, flexible dentures are more attractive to patients who prioritize comfort and aesthetics.

The concept of supremum is fundamental in real analysis and plays a crucial role in the optimization of single-variable real functions. In practice, not all functions attain their supremum explicitly, which necessitates numerical approaches to evaluate their behavior computationally. This study aims to analyze the supremum of several one-dimensional real functions with different characteristics using a grid-search method implemented in Python. Four functions were examined: a parabolic function, a rational function with a sharp peak, a discontinuous piecewise function, and a function with a vertical asymptote. The analysis involved modeling the functions, discretizing the domain, performing numerical approximation of the supremum, verifying the results against analytical values, and using graphical visualization to observe the function behavior near the supremum. The findings indicate that the supremum of the parabolic, rational, and piecewise functions can be accurately identified, with results consistent with analytical expectations despite minor deviations caused by grid resolution limitations in the rational function. Meanwhile, the function with a vertical asymptote yields an unbounded supremum, which cannot be attained within the domain. These results demonstrate that Python provides stable and reliable numerical estimates of the supremum across various types of one-dimensional real functions, validating the effectiveness of computational methods in supporting conceptual understanding of supremum.

Fraudulent investment is a phenomenon that has become widespread among Indonesian society. The PKK mothers in Rt 5 Rw 2, Gayamsari sub-district, have not yet gained any knowledge or understanding about the dangers of fraudulent investments and their characteristics, nor have they received any knowledge or understanding related to financial literacy that could protect their families from the dangers of fraudulent investments. The PKK of Gayamsari sub-district urgently needs socialization on strategies to prevent being affected by fraudulent investments and to recognize the signs of such investments. In addition to fraudulent investments, they also feel the need to receive socialization about financial literacy and the characteristics of legal and safe investments, so that they can prevent becoming victims of fraudulent investments. Based on the problems that occur, solutions are needed to address them. The purpose of this community service activity is to provide understanding and knowledge about financial literacy so as not to fall into fraudulent investments. Partners are also given an understanding of safe and legal investments to avoid the dangers of fraudulent investments. The expected outcome of this service activity for the mothers of the PKK in Gayamsari sub-district is to understand the importance of financial literacy and the dangers of fraudulent investments along with their characteristics. It is hoped that the PKK mothers in Gayamsari sub-district will be wise in managing family finances by choosing safe investments for the future of their household and family, as well as being able to prevent becoming victims of fraudulent investment scams.

This study explores the major challenges faced by moral education in the modern era, particularly within Madrasah Ibtidaiyah, where rapid technological advancement, globalization, and shifting patterns of student interaction have significantly influenced the traditional process of moral development. The purpose of this research is to analyze teachers’ strategies in revitalizing moral education, identify the supporting and inhibiting factors in its implementation, and evaluate its impact on students’ discipline. Employing a qualitative approach with a case study design, data were collected through in-depth interviews with the school principal, Islamic ethics teachers, and students, as well as observations of daily moral habituation practices within the school environment. The findings indicate that effective revitalization of moral education occurs through an integration of teacher role modeling, consistent habituation of moral practices, and value internalization supported by interactive learning methods. These strategies contribute to improved student discipline, greater independence, and more stable behavioral patterns. However, the effectiveness of these efforts is still influenced by external factors such as excessive gadget use and limited continuity of moral guidance at home. The study provides an integrative and adaptive model of moral education that aligns with the characteristics of the digital generation and emphasizes the essential collaboration between teachers, schools, and families. The results highlight the importance of a responsive, contextually relevant approach to moral development that meets the evolving needs and challenges of 21st-century learners.

This study develops an architectural design framework integrating fun and playful spatial characteristics with futuristic, science-based approaches to enhance children’s engagement with science and technology learning. Traditional learning environments are often rigid and fail to stimulate curiosity, especially among children aged 7–11. Utilizing a qualitative conceptual analysis, this research synthesizes architectural theory, neuroscience learning principles, children’s spatial psychology, and science-based design methodologies. The results show that playful spatial elements—dynamic forms, color contrasts, sensory interactions, and imaginative environments—significantly improve motivation, exploration, and conceptual understanding. Futuristic design components further support scientific literacy by projecting technological possibilities and integrating interactive digital tools. The study produces a conceptual architectural model linking playfulness, scientific experience, and future-oriented visualization. It concludes that learning environments must be intentionally designed to merge emotional engagement, cognitive stimulation, and technological representation. The implications extend to the design of children’s museums, science centers, and early STEM learning facilities.