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This study aims to find out and analyze the legality of foreign investment registration in the online single submission risk based approach (Oss-RBA) system in Indonesia. This research is a normative research using primary, secondary and tertiary legal materials collected by tracing or searching and documentation studies, the legal materials that have been collected are processed and analyzed in several stages, namely inventory, identification, classification and systematization. The results of the study show that (1) Law Number 25 of 2007 concerning investment has been in compliance with TRIMs. (2) Foreign investment business licenses in the Oss-RBA system are not considered to violate TRIM's because they are not regulated in the illustration of the violation of the TRIM's agreement.     

The State, as the supreme authority over land, water, and outer space, including the natural resources within, holds the highest level of power as an entity representing the entire population. This authority includes the regulation, utilization, use, and maintenance of these resources, as well as defining the legal relationships between individuals and these resources. The primary goal of this authority is to achieve the greatest welfare for the people and to promote justice, well-being, and freedom within a society based on the principles of an independent, sovereign, just, and prosperous Indonesian legal state. In the context of Indonesian agrarian law, land is considered a part of the earth's surface and is governed by Law No. 5 of 1960 on Basic Agrarian Principles (UUPA). Land rights, such as Ownership Rights and Building Use Rights (HGB), play a crucial role in land management and use, including as collateral for credit. Mortgage Rights, regulated by Law No. 4 of 1996, provide security over land and related objects, with the right holder entitled to sell the property through public auction if the debtor fails to meet their obligations. The importance of land registration and ownership rights is regulated by Government Regulation No. 24 of 1997, which ensures legal certainty for land and property owners. Creditors, including banks, play a critical role in the economy by providing credit facilities, where collateral on land and buildings is a key aspect to ensure the security of repayment and the legal validity of transactions. The classification of creditors as separatis, preferen, and konkuren facilitates the execution of mortgage rights in the event of contract breaches, thereby providing the necessary legal protection for lenders.

The detection of computer network attacks is becoming increasingly important as the complexity and frequency of cyber-attacks threatening information systems and network infrastructure continue to rise. These attacks may lead to severe consequences, including data breaches, service disruptions, and financial losses. To address these challenges, artificial intelligence techniques have become a major focus in the development of more effective, adaptive, and reliable intrusion detection systems. Among various classification algorithms, the C4.5 decision tree has demonstrated strong performance due to its simplicity, interpretability, and high classification accuracy. This study aims to apply the C4.5 algorithm for network attack detection using a comprehensive dataset that includes multiple categories of attacks and normal network activities. The proposed methodology consists of several stages, including data preprocessing, feature selection, decision tree model construction, and performance evaluation using standard metrics such as accuracy, precision, recall, and F1-score. Data preprocessing is performed to handle missing values, normalize data, and reduce noise, thereby improving the overall quality of the dataset and enhancing classification results. The experimental results indicate that the C4.5 decision tree algorithm effectively classifies network traffic into attack and normal categories with a satisfactory level of accuracy. The model successfully identifies attack-related patterns and highlights significant features that influence detection performance. Further analysis reveals that appropriate feature selection and parameter tuning significantly contribute to improving model reliability and robustness. This research provides a valuable contribution to the development of efficient, accurate, and practical network intrusion detection systems. The proposed approach is expected to strengthen information security frameworks and support proactive defense strategies against increasingly sophisticated cyber threats, thereby enhancing the protection of critical network infrastructures.

Image classification is widely used in everyday life such as in car steering, closed-circuit television (CCTV), traffic cameras, etc. The implementation of image classification can be done using several methods, including neural network and support vector machine models. The neural network method is able to find the right weights that allow the network to show the desired behaviour while the support vector machine method has many dimensions and can overcome linear and non-linear data. In this research, feature extraction was carried out using VGG16 to increase accuracy. This research aims to find out how to implement the neural network and SVM algorithms to classify images and determine the results of analyzing the performance of the two methods. The data used in this study is secondary data consisting of 10 types of large wild cats with a total of 2339 training image datasets and 50 testing image datasets. The research stages consist of data augmentation, model design, model training, and model evaluation. Classification with the neural network model produced an accuracy of 96% and the support vector machine model produced an accuracy of 96%, which means that in a consistent training environment, the two models have the same performance.

Admission of new students to an educational institution is an activity that is always carried out every new academic year, where prospective new students always increase from year to year (Muwardah and Pramunendar, 2015). Admission of students can be held from elementary to middle school, from middle school to high school / vocational school. The focus of this research is the registration of new students at SMK. As is known, SMK is a Vocational High School or abbreviated as (SMK) and where there are many majors provided which ultimately makes prospective new students confused about which major is right for them because will take a long time.. Based on C4.5 as a Classification Algorithm: C4.5 is a popular algorithm for building decision trees. It works by dividing a dataset into smaller subsets based on attribute values, thus forming an easy-to-understand tree structure. Classification results using decision trees provide a clear visualization of the decision-making process and the variables that contribute to student choices.

Achieving financial stability for the family is a goal that everyone has. The family is the most significant foundation for the growth of human life in Indonesia. The first and most important living environment where the personality that shapes a person's life is created is the family. The development of moral values, social justice, humanity, and religion is essentially a process decided by the family. The family is the first and main social structure that plays an important role in providing life values ​​to children who are growing and developing so that they can find purpose in their life journey. The classification of family welfare according to population data owned by the village government is the aim of this study. Based on the results of the study, fuzzy logic and the Tahani model can be used to process population data according to the level of family welfare, resulting in output in the form of family classifications such as prosperous, poor, and underprivileged families. To assess the effectiveness of incorporating fuzzy logic into the program being developed, the fuzzyTECH application is also used to test the program subdistrict.    

This study aims to determine the effect of commitment and organizational culture on employee engagement at DC Yogya Group Cikoneng Branch, Tasikmalaya. The method used is a case study, data obtained through questionnaires to the number of employees working at DC Yogya Group Cikoneng Branch. Sampling using saturated sample technique and the method of analysis used is multiple linear regression analysis method. From the results of the study indicate that commitment, organizational culture and employee engagement are included in the low classification. Simultaneously commitment and organizational culture have a significant effect on employee engagement at DC Yogya Group Cikoneng Branch. Partially, the commitment variable has a significant effect on employee engagement at the DC Yogya Group Cikoneng Branch, and the organizational culture variable has a significant effect on employee engagement at the DC Yogya Group Cikoneng Branch.  

The development of the times is accompanied by progress in various fields of life, such as science, technology, and economics. Progress is also felt in the scientific field of journalism. One of the terms in the field of journalism that is commonly known by the general public is print mass media, such as newspapers. Today's newspapers are not only printed on paper, but many newspapers have created their own blogs or pages so that news can be read online. The aim of this research is to analyze language errors based on the classification of linguistic categories, namely syntax, which includes the areas of spelling and sentence effectiveness. The data source is several news stories published on the detikNews website in the February 2024 edition. The data was then analyzed using the listening method and note-taking techniques. The results of the research showed that there were 52 errors in the use of spelling and punctuation, which were described as follows: a) there were 11 errors in writing foreign words; b) there were 8 errors in writing numbers; c) there were 13 errors in writing capital letters; d) there were 15 errors in the use of punctuation marks; e) there were 5 errors in writing pronouns. Apart from that, there were 44 errors in the use of ineffective sentences, which are described as follows: a) there were 14 errors in choosing diction; b) there are 10 sentences with no subject or predicate; c) there are 9 sentences with overlapping words; d) there are 11 long-winded sentences. It is hoped that the results of the analysis in this article will increase understanding and deeper knowledge regarding linguistics, namely syntax, including the areas of spelling and word choice and their use, knowledge about the types, as well as providing suitable teaching materials for class X high school students for them to carry out assessments and considerations. about adolescent social behavior critically.

For students, Dasar Elementary School offers a formal education pathway. Mengubah pemahaman siswa terhadap materi pembelajaran is quite important in the learning process. Perlu dicapainya, siswa atau didik adalah fungsi dari adanya seorang pendidik.This is due to the fact that in the process of teaching mathematics, understanding concepts is a foundation that is extremely important for making decisions on the solution of mathematical problems as well as daily problems. Due to a few factors that make it difficult for students to understand math lessons, they find it difficult to understand or even understand them at all. To achieve the maximum learning outcomes, education must be provided by qualified teachers. One of the most important educational resources for achieving the highest learning outcomes is a teacher. A teacher is one of the most important educational resources; learning will be more enjoyable if the teacher can implement a flexible learning model. In addition, teachers are expected to be constantly creative in their approach to teaching. Due to this, a system is required to determine the student's level of proficiency in comparison to the advanced learning environment in order to assess student learning. The collected data from the survey will be divided into two categories: training data and testing data. The model's training data results will be used to assess the accuracy of the data testing. The classification results indicate that the Naïve Bayes algorithm is a good choice for reducing student anxiety levels in foreign language learning, with a remarkably high accuracy rate of 95.24%.  

Understanding literacy and linguistics in Indonesia, especially through novel reading, plays an important role in honing students' cognitive skills, such as critical and creative thinking. Syntax as part of linguistic studies, helps students understand language structures that support effective communication and deeper literary appreciation. This study aims to understand the structure and function of active and passive sentences in Indonesian focusing on the types of active and passive sentences and the role of sentence elements in shaping meaning. This study uses methodological and theoretical approaches with qualitative and syntactic descriptive methods to analyze the pattern of sentence function in the novel "9 of Nadira" by Leila S. Chudori. The research process involves data collection, classification, analysis, and presentation of data with listen, record, and bill techniques. The results showed that the sentence function pattern in the novel "9 of Nadira" by Leila S. Chudori uses different sentence patterns in each active and passive voice. Transitive and bitransitive active sentences have additional objects and complements, while intransitive sentences do not, and only sentences with objects can be changed to passive by undergoing changes in their predicates. This research has the benefit of providing a deeper understanding of the structure and function of active and passive sentences in Indonesian.  

Village monies are sourced from the State Budget (APBN) and are allocated in accordance with Law Number 6 of 2016 for the purposes of development, coaching, social activities, and community empowerment. The existence of village finances is necessary to support all sources of revenue for the village, and as the government's revenue grows, so too must the village's public service infrastructure. As a result, the sentiment analysis of village officials will be done in this study. The analysis will include the classification of community sentiment using the Naive Bayes technique. To assess each method's accuracy, two will be contrasted. The village administration, as the highest social institution in the community, is crucial in establishing norms, distributing funds, and creating a help-related socialization process. Furthermore, some Pada Eweta village inhabitants may not receive help, which may cause social rivalry amongst locals. Sentiment categorization will be used to classify responses as positive or negative. Based on feedback from visitors, this study seeks to evaluate the validity of the two approaches put to the test and provide insight into the level of service provided by village officials. The accuracy of both methods will be verified by evaluating the outcomes with the RapidMiner tool.

This study aims to analyze the correlation between students' academic achievements and the effectiveness of campus promotion in attracting new prospective students. Academic performance is often used as an indicator of educational quality in higher education and is frequently highlighted in promotional strategies. However, the direct relationship between student academic achievement and campus promotion success has not been fully understood. This research utilizes the Apriori method in data mining to discover hidden patterns related to student academic performance and campus promotion. The data analyzed include undergraduate students from the 2017–2019 cohorts at STMIK Kaputama Binjai, with variables such as study programs, GPA, school background, and major during high school. The results indicate a significant correlation between academic achievement and the effectiveness of campus promotion, which can be used by institutions to develop more targeted promotional strategies. By leveraging data mining, universities can more effectively identify potential student segments and enhance the overall image of the institution.

Gout arthritis, commonly known as gout, is a degenerative disease caused by abnormal purine metabolism, characterized by elevated levels of uric acid in the blood. (hiperurisemia). According to data from the World Health Organization, the number of people suffering from gout reaches 230 million, or 34.2% of all non-communicable diseases. In Indonesia, it is increasing. Gout disease based on age characteristics is particularly high in individuals aged ≥ 75 years (54.8%). Central Java is estimated to be around 2.6 - 47.2%, varying across different populations. The percentage of the population suffering from gout arthritis at Puskesmas Kecipir is 13.67%. The number of people affected is 3,084 out of 22,560 in the productive age group. In the village of Kalibuntu, the percentage of the elderly population suffering from gout arthritis is 9 out of 37. The purpose of this scientific paper is to provide nursing care for Mrs. R's family with musculoskeletal system disorders: gout arthritis in Kalibuntu Village, Rt 04 Rw 01, Losari District, Brebes Regency. The assessment results of Mrs. R's family indicate that they are unaware of gout arthritis, its causes, classification, symptoms, as well as how to prevent and manage gout arthritis when pain occurs. The nursing problems that arise are knowledge deficit and chronic pain.

This research aims to apply the K-Means Clustering method in grouping consumer interests regarding the use of services at the Binjai Post Office. The Post Office is part of a state-owned enterprise in North Sumatra Province with the main task of providing postal and logistics services. Postal services remain one of the most important means of communication, especially for sending packages, letters, and documents. However, with various services and diverse consumer needs, post offices can provide more effective and relevant services. The K-Means Clustering method is a classification technique based on machine learning algorithms used to identify patterns present in consumer interest data. The data used in this research includes various related variables, namely the type of delivery, total cost, and delivery time. The results of the clustering process conducted using 3 clusters indicate that there is a grouping of consumer data based on preferences for using delivery services. In group 1, there are (21 data points) with a centroid at coordinates (C1) 2; 4.3810; 3.5238. In group 2, there are (124 data points) with a centroid at coordinates (C2) 3; 2.0565; 3.1452. In group 3, there are (387 data points) with a centroid at coordinates (C3) 3.6925; 1.1370; 1.7209. This research shows that the application of K-Means Clustering can enhance the understanding of consumer interests and assist in the development of more targeted strategies to optimally meet needs.

Handling of dental disease problems requires that it be handled quickly and correctly, but not all teams of dental experts can carry out treatment quickly due to the lack of a team of dental experts who are in the workplace or hospital 24 hours a day.  Apart from that, the public also has very little knowledge of information about dental disease, so that to treat dental disease, people have to consult a dentist. To classify images of dental disease, feature extraction is needed. Feature extraction is taking characteristics of an object that can describe the image. One example of image feature extraction used is Red, Green, Blue (RGB). This feature extraction is often used to identify or classify an image. Dental image data that will be used in the classification process are tooth abrasion, anterior crosbite, cavities and gingivitis. K-Nears Neigbor is the simplest data mining algorithm.  The aim of this algorithm is to find the results of the closest distance classification for each object.  In determining the distance, the data is initially divided into two parts, namely training data and testing data. After receiving the training data and testing data, the distance from each testing data (Equilidence Distance) to the training data is calculated. The K-Nearest Neighbors method can be applied to classify dental disease based on images of types of dental disease using Matlab software. As a result of the image data training process, 40 image data were input, training results obtained were 100%.

This study aims to determine the effect of financial literacy and financial self-efficacy on risky credit behavior in using paylater (survey of students in tasikmalaya city). The method used in this research is a quantitative method through a survey approach. With a research sample of 100 students in Tasikmalaya City who use PayLater. The data used is using primary data. The analytical tool used in this research is multiple regression using SPSS 26.0. The results showed that financial literacy has a good assessment classification, financial self-efficacy has a good assessment and risky credit behavior has a good assessment classification for students in Tasikmalaya City who use PayLater. Simultaneously financial literacy and financial self-efficacy have a significant influence on risky credit behavior. Partially, financial literacy does not have a significant effect on risky credit behavior and financial self-efficacy has a significant effect on risky credit behavior in students in Tasikmalaya City who use PayLater.  

The newest renewable energy source in the world and one of the most reasonably priced is solar energy. Because solar energy has so many benefits all year round, it can be a cost-effective energy source when used, especially since it is so abundant globally. to produce electricity by converting sun energy. The equator-based nation of Indonesia boasts an abundance of solar energy resources, with an average daily solar radiation intensity of about 4.8 kwh/m2. However, there is an abundance of solar-based energy sources that can be utilized. Especially in Lolo Wano Village, where the intensity of solar radiation is quite high, it is an option to develop a Solar Power Plant (PLTS) as a solution to electrical energy needs. In order to specifically identify the class of unknown object labels, classification techniques are employed since they are able to identify models that distinguish between different data classes or data ideas. In the meantime, the Naïve Bayes algorithm takes into account multiple factors that will influence a decision's final result in order to forecast future opportunities based on data that has already been collected. The information utilized comes from observations made by the LOLO WANO VILLAGE PLTS Community (PLTS). The data gathered from the satisfaction survey will be divided into two categories: training data and testing data. The testing data's accuracy will be evaluated using the output of the training data model. The classification findings demonstrate that, with the maximum level of accuracy at 87.50%, the Naïve Bayes algorithm is appropriate for gauging student satisfaction with online learning.

Nowadays, robots in the modern world are playing an important and increasingly popular role. MRA (Mobile Robotic Assistant) is a type of mobile robot designed to support humans in many different fields, helping to improve efficiency and safety in daily activities, work, or medical treatment. The number of MRAs is increasing and diverse in function, in addition to the ability to collect and process data, MRAs also have the ability to physically interact with users. Therefore, security is one of the important issues to improve the safety and effective operation of MRA. In this paper, through a comprehensive literature review and detailed analysis of the prominent MRA security attacks in recent years (based on criteria such as: attack targets, technologies used, impact level, feasibility, and contribution to addressing overall MRA security issues), a systematic classification by MRA activity fields is conducted. Security attacks, threats, and vulnerabilities are examined from various perspectives, such as hardware attacks or network/system-level attacks, operating systems/application software. Additionally, corresponding security solutions are proposed, compared, and evaluated to enhance MRA security. The paper also addresses challenges and suggests open research directions for the future.

Background: Hypertension is commonly referred to as high blood pressure. A person has hypertension if his systolic and diastolic blood pressure has risen above 140/90 mmHg. Hypertension is the leading cause of premature death worldwide with more than 1 in 4 men and 1 in 5 women and more than one billion people experiencing the condition (WHO, 2020). Non-pharmacological therapy acupressure therapy can be an option for the elderly to overcome hypertension. Objective: To describe the results of the comparison of blood pressure reduction in the elderly before and after the application of acupressure therapy on 2 (two) respondents. Methods: This study uses a descriptive method, selecting samples according to the inclusion criteria and selecting 2 elderly respondents who experience hypertension. The research instrument used a digital Sphygmomanometer and the research was carried out 15 minutes for 3 consecutive days. Results: There was a decrease in systolic pressure in Mrs. T by 10 mmHg and a decrease in Mrs. P's systolic pressure by 7 mmHg. While the diastolic pressure of Mrs. T was 12 mmHg and Mrs. P decreased by 10 mmHg. The results of the application of acupressure therapy on both respondents showed a change in blood pressure reduction from previously included in the classification of grade 1 hypertension to high normal. Conclusion: There are differences before and after acupressure in elderly hypertension.

This research focuses on using the K-Nearest Neighbor (KNN) algorithm to model student satisfaction with campus services. The study finds that the quality of the dataset strongly influences the accuracy of the KNN classification results. Factors such as data cleanliness, balanced class distribution, and sufficient training data volume are highlighted as crucial for a successful model. The research also emphasizes the significance of proper feature selection in enhancing classification performance, suggesting that irrelevant features can introduce noise and decrease model accuracy. The model was evaluated using a dataset of 1032 data points and K=5, achieving an accuracy of 93.72%. While the model performed well for certain classes such as "Very Good" and "None", challenges were encountered in classifying the "Fair" and "Deficient" classes. The study concludes that KNN is effective in identifying student satisfaction patterns but highlights the need for improvements in accurately classifying these challenging classes. Ultimately, the research underscores the importance of data quality and feature selection in enhancing the performance of classification models for student satisfaction analysis.