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This research aims to conduct a comparative analysis of machine learning algorithms for classifying the risk levels of maternal health. With a focus on the significance of identifying and classifying health risks for pregnant women, this study applies supervised learning methods employing Naïve Bayes, Decision Tree, and K-Nearest Neighbors algorithms. Utilizing the "Maternal Health Risk" dataset from UCI Machine Learning, the research is conducted on Google Colaboratory using Python. The results indicate that the Decision Tree algorithm achieves the highest accuracy rate at 90%, surpassing K-Nearest Neighbors (86%) and Naïve Bayes (65%). Consequently, Decision Tree emerges as the preferred choice for predicting maternal health risks, offering the potential for enhanced care and monitoring.

Programming algorithm learning analysis is a study that examines the approaches, methods, and results of the algorithm teaching and learning process in the world of programming. This research looks at whether algorithms are only used in the computer world. The results of analysis or research show that learning algorithms are not only used in the computer world, but these algorithms can also be used in everyday life and even everything we do in the real world. Most of them are said to be related to algorithms. Therefore, this analysis highlights the importance of diverse approaches in teaching programming algorithms. Then the aim of writing this article is to analyze or find out how the programming algorithm learning process works, where with a deeper understanding of the programming algorithm learning process, it can be hoped that the quality of programming education and students' ability to understand and apply algorithm concepts in the programming world and the real world will improve. This research uses a descriptive method, where the author analyzes, summarizes and describes different situations and conditions from different sources so that they are collected into an article. Collecting data or information sources is carried out using library research techniques from journals, books and internet media which are used as reference material relating to the title discussed, namely learning analysis of programming algorithms with more accurate data and clear sources

Abstract.Blended Learning is a combination of face-to-face learning and online learning. In elementary schools, many teachers choose to use blended learning to improve students' understanding, especially in computer subjects in mathematics learning at FITK UIN Sumatra Worodugu. So the aim of this research on the one hand is to encourage the use of integrated learning models for mathematics students to assess the development of students' understanding of programming concepts. This research uses qualitative research and the type of research analyzed is descriptive. Researchers tested students' perceptions of online education. Knowledge-based learning and connected learning environments during the Covid-19 pandemic. The data from this research is intended to be a source of learning algorithms for teachers and students in the mathematics curriculum. The research results show that the success of continuing education depends on two things: If there are less than 20 students, then the course is held as an independent unit. The second way, if the class has more than 20 people, the first week will be taught directly, the second week there will be introductions, including online training via WAG. The use of blended learning methods can increase students' understanding in algorithmic programming courses about the evolution of blended learning and its application. For example, the Internet due to disease. Can improve children's learning and increase their motivation, especially in the field of technology.  

This research will cover the application of programming algorithms in the context of mathematics learning and transmitting its impact on the quality of mathematics teaching. Therefore, this research aims to understand and explore the potential role of programming algorithms in supporting effective and relevant mathematics teaching. Literary research methods or literary research. Literature research includes reviewing or studying books or reading sources related to algorithm programming in a systematic and organized manner. Writing this article also uses a literature review method or research desk on the results of research that has been carried out previously and is related to the research discussed, especially algorithm programming. To collect some data This is done by looking for reputable journals, reading books. Scientific journals and various sources from website searches, Google Scholar, digital libraries. The results obtained from research articles and several books explain that in learning algorithms, programming has a role related to science or other science subjects such as mathematics and other subjects. These results come from reflections on literature that focuses on understanding and the role of programming algorithms with mathematics. This role includes the ability to make it easier for students with a mathematics background to collect, calculate, or even read data using programs created algorithmically. When we start studying programming algorithms we must first learn the basic concepts of algorithms and programming. In writing algorithms it is never linked to certain rules but only the intent and purpose of each step of the algorithm must be clear and precise. Mathematics learning algorithms are a concept that involves the use of structured and logical steps to teach and understand mathematical concepts to students. The following is a more detailed explanation of the importance of algorithms in mathematics learning: 1. Structured Steps: Algorithms consist of a series of structured and explicit steps to complete a task. math assignment or problem.This is a document that guides students through the process of solving a math problem. 2. Understanding Mathematical Concepts: Algorithms are used to describe and illustrate how to understand certain mathematical concepts. For example, how to calculate the area of ​​a triangle or how to solve linear equations.

This research discusses a comparative analysis of school exam scores during and after the Covid-19 pandemic that were achieved by students, which was carried out at SD Bahagia. The aim of this research is to compare student learning outcomes during the Covid-19 pandemic and after the Covid-19 pandemic, to find out the process results achieved by students with online learning / learning from home and face-to-face / new normal learning. This research uses a design method to make it easier for researchers to design student data analysis with structure and steps. The results and discussion of the student report card assessments during and after the Covid-19 pandemic were very good and the average score was not below 62, so the results obtained by the students in the statement were declared passed because the students were able to follow the online learning process and understand the material provided by the teacher. , even during the Covid-19 pandemic which limits long distances. Meanwhile, after the Covid-19 pandemic, teacher and student communication was achieved which resulted in students understanding the material easily without any obstacles.

This study aims to analyze the communication of marine mammals, especially whales and dolphins, through a bioacoustic approach combined with computational science as an effort to support conservation in the Tropical Ocean region. The focus of the location is on the Banda Sea, the Seram Sea, and the tropical Pacific region which are important migration routes for marine mammals. Data were obtained from underwater sound recordings using hydrophones, accompanied by visual observations to validate the behavior and existence of species. The analysis is carried out through several stages, including signal pre-processing with noise filtering and sound segmentation, spectral analysis using Fast Fourier Transform (FFT), as well as the creation of a spectrogram to visualize vocalization patterns. Machine learning algorithms such as Support Vector Machine (SVM) are used to classify interspecies voices, while deep learning approaches are applied to identify more complex communication patterns, including dialect variations. The results showed that whales produced low-frequency vocalizations (20–200 Hz) for long-distance communication, while dolphins used high-frequency clicks and whistles (5–20 kHz) for echolocation and social interaction. The integration of bioacoustics and artificial intelligence improves the accuracy of sound classification by more than 90%. These findings confirm the effectiveness of computational-based non-invasive methods in monitoring the presence and behavior of marine mammals and provide a scientific basis for sustainable conservation.

This study aims to determine the role of information technology in public sector accounting in preventing fraud that occurs in public sector organizations. The research conducted was a literature review research based on articles appropriate to the research topic for further analysis. The result of this research is that there are fraud prevention methods which include technical strategies and preventive strategies. The technical strategy includes efforts to prevent fraud through the use of privacy officer services, IoT access, managing data access, establishing timely reporting, and controlling data in real time. The preventive strategy is in the form of developing a fraud detection system that is run using machine algorithms, data mining, and meta learning methods.

DDOS (Distribute Denial of Service) is a type of structured attack. This attack has been around since 1990. DDoS attacks are capable of paralyzing servers by flooding network traffic and causing it to go down. To overcome this problem, the way to detect DDoS attacks has several methods and algorithms, one of which is the Artificial Neural Network algorithm and uses the Machine learning method due to the fast computing process, high accuracy, and this research uses the SKKNI research method Number 299 of 2020. The analysis was carried out uses training data from the latest dataset, namely CICIDS2017, which is a development of a previously existing dataset. DDoS attack detection testing using the confusion matrix method obtained bot precision of 0.99, recall of 0.99, and f1score of 0.99, 3