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The Indonesian government has implemented various programs to improve public welfare; however, social assistance often misses its target, primarily due to a lack of accurate data. Sirapit Subdistrict, as a government institution, has access to important population data for policy development, particularly in the distribution of aid based on community welfare levels. Factors such as education, age, number of dependents, and income play a significant role in determining an individual's welfare. To address this issue, this study proposes the use of the Apriori method to analyze the factors affecting population welfare. The Apriori method is a data mining algorithm useful for discovering association patterns within a dataset. The study results show that with a support value of 3% and a confidence level of 100%, a pattern was found where residents with a high school education, 1-2 dependents, aged 35-45 years, earning Rp 500,000 - Rp 999,999, and with a low welfare level tend to work as laborers. These findings are expected to serve as a foundation for formulating more targeted policies to improve community welfare in Sirapit Subdistrict.

Beauty clinics are currently a primary need for women and men. This is proven by the increasing number of beauty clinics that are expanding in Binjai City. This makes consumers have to be more selective in choosing a beauty clinic so they don't have to waste a lot of time and money. Beauty Clinics are included in service businesses that aim to provide satisfaction for customers or consumers. With the emergence of various types of Beauty Clinics, competition between one Beauty Clinic and another has become increasingly fierce, thus encouraging each Beauty Clinic to try to improve the quality of its products and services.

Crime is a problem experienced by humans from time to time, crime often occurs because of several factors, one of which is due to the lack of security of the address so that many criminal acts occur. Hamparan Perak Police is trying to increase its commitment to safeguard and protect the community through efforts that are organized consistently and continuously. The rise of criminal acts that occur, such as motorcycle theft, persecution, and the rise of robbery in the middle of the road makes residents feel unsafe and always feel threatened at certain addresses. Therefore, to determine the vulnerable pattern of crimes committed, it is necessary to determine the group to determine the vulnerable area or not using the clustering method, which aims to be able to assist the police in conducting socialization and actions for public security by combining objects in a group with each other and different from objects in other groups. From the tests carried out using the clustering method with the K-Means algorithm, it can be seen that the group of criminal data that has the highest group and most often appears when processed is the criminal act of theft, the pattern of criminal acts in quiet areas, has been monitored and planned in klambir village.

The decline in water quality is a serious environmental issue, particularly in urban areas. This research aims to develop an Internet of Things (IoT)-based water quality monitoring system using ESP32, pH, TDS, and turbidity sensors. The system is designed to monitor water quality parameters in real-time and transmit data to a cloud platform for further analysis. The system prototype was tested with water samples from various sources, and the results demonstrated high accuracy, with a maximum deviation of ±0.5% compared to laboratory results. Thus, this system offers an efficient and easy-to-implement solution for continuous water quality monitoring, which can aid in water resource management in urban environments.

This research aims to develop an expert system that can diagnose diseases related to brain tumors using the Case Based Reasoning (CBR) method. The CBR method works by comparing new cases with previous cases that have been stored in the database to provide appropriate diagnoses and treatment recommendations. This system is designed to assist medical personnel in analyzing patient symptoms, thus speeding up the process of identifying the type of brain tumor. In addition, the system is also equipped with a knowledge base obtained from real cases that have been validated by medical experts. Test results show that the diagnostic accuracy of this system reaches a fairly high level, especially in detecting frequently encountered types of brain tumors. Thus, this system has the potential to be an effective tool in the medical diagnosis process, especially in patients who show symptoms of brain tumors.

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.

The Merdeka Belajar Kampus Merdeka (MBKM) program provides students the opportunity to study for one semester outside of their major, aiming to develop the soft and hard skills required in the workforce. One key component of this program is internships or practical work, which gives students hands-on experience in the professional world and the chance to build professional networks. This research uses the K-Nearest Neighbor (K-NN) method to predict the impact of MBKM activities on undergraduate students at STMIK Kaputama. Using the RapidMiner application, student data was tested to obtain the accuracy of predicting students' engagement in the MBKM program in the future. The test results show that the K-NN model has an accuracy of 75.34%, indicating that the model is fairly good at predicting the impact of the MBKM program on students.    

Birth problems are one of the problems that have not been resolved in various regions, where an average mother gives birth to three to four children. The increase in population due to births will also affect various aspects of development, and pose a big risk to ensuring community welfare. For example, opportunities to obtain educational facilities, job opportunities, health insurance, housing and can increase opportunities for increasing poverty and crime. To find out which factors influence the birth of a baby, an association rule is needed to find out which factors influence the birth of a baby which can be seen from several criteria such as a woman who has a low level of education or a bachelor's degree, a woman who marries at an old age. , or women who marry underage, give birth naturally or surgically. Association rules are a data mining technique for determining the relationship between items in a set of data that has been determined. By determining min support 0.01, confidence 0.1 and 7 itemsets, the results obtained are 25 data items with varying min support and confidence with a maximum support x confidence result of 100%. By using the a priori method, 14 of the best rules were produced by producing the most up-to-date information.

This study investigated college students' anxiety levels in completing their final projects, which is an important requirement for graduation. Anxiety is a common problem faced by students, which is often caused by the long and complicated process of preparing a final project. Using the Zung Self-Assessment Anxiety Scale (SAS/SRAS), this study aims to measure the level of anxiety and identify the main factors that contribute to it. The Rough Set Method, an efficient technique for analyzing uncertainty, was applied to identify patterns and relationships between factors influencing college students' anxiety. Data was collected through questionnaires from students who are currently completing their final projects. By applying the Rough Set Method, this research succeeded in identifying significant factors that influence anxiety levels, such as psychological, physical and positive responses. These findings provide valuable insight for educators and counselors to better understand and address college students' anxiety during the final years.

Learning achievement is every learning activity carried out by students which will result in a change in themselves. The learning outcomes obtained by students are measured based on differences in behavior before and after learning is carried out. The economic conditions of students' families at SMP Negeri 2 Binjai have a significant influence on student learning achievement. Many students who come from families with economically disadvantaged backgrounds face various challenges that hinder the learning process. Financial limitations often mean they do not have adequate access to educational resources, such as books, the internet, and additional tutoring which can help improve understanding of subject matter. This research uses the Apriori method as a problem solving method, namely to correlate between Family Socio-Economics, Activities Students Outside the School Environment and Level of Student Learning Motivation with Student Achievement in class. If data A, G, K → O with Support 30% and Confident 100% and S*C value 30%. So, if a student from a family with an income of less than Rp. 1,000,000 who take part in extracurricular activities outside of school, and have family-driven motivation, will have academic achievement with good report cards. This research indicates that family socio-economic conditions have a significant impact on student academic achievement. Through data analysis, it can be seen that factors such as family income, student activities outside the school environment and the level of student motivation to learn can influence the extent to which students can achieve higher academic achievement.

This study addresses the issue of Indonesian migrant workers (TKI) whose characteristics do not match the jobs assigned abroad, often leading to complaints from agencies and companies. This mismatch is caused by incorrect job placements and insufficient training, which prompts TKI to leave their assigned jobs. The research aims to better understand the characteristics of TKI that influence successful job placement. The **apriori** method was used to identify patterns and relationships between TKI characteristics, destination countries, and suitable job types. Based on a 30% minimum support, 3 and 4 itemset combinations were produced, showing correlations between TKI characteristics and job positions. Using lowerboundminsupport 0.001 and minmetric 0.1, this study generated 6 itemsets from 13 data points, providing significant correlations between TKI characteristics and more accurate job placements.

Police Sector (Polsek) is one of the agencies that provide protection, order and ensure public safety in the sunggal area. The number of cases of criminal acts that occur makes residents feel unsafe and always feel threatened in certain areas in the Sunggal sub-district, the pattern of criminal acts that often occur due to several factors, one of which is due to the lack of security in the area so that many criminal acts occur as well as behaviour that has been planned by the perpetrator to achieve their goals by planning, preparing, implementing, disposing of evidence, even hiding or escaping depending on the type of crime committed based on the characteristics of the perpetrator, and the situation or context in which the crime occurred. Therefore, it is necessary to analyse techniques from existing criminal data using the a priori algorithm method to find patterns of relationships between variables that can assist agencies in taking action for public safety. Based on the research conducted, the above case is tested with a minimum support = 10%, confidence = 100% so that the results of the rule that meets the support and confidence values are obtained: ‘If the criminal act is theft then the job is self-employed’, then giving value is successful with 15% support, 100% confidence. And ‘If the age of 17-25 years, the criminal act is Theft then the job is unemployed’, then giving value is successful with 10% support, 100% confidence.

Respiratory tract disease is a common condition that can affect anyone regardless of age. Starting from relatively mild symptoms to alarming symptoms. Although some respiratory diseases are not life-threatening, they should not be taken lightly as they can cause serious complications. What often happens is that it is difficult for a patient to see a specialist doctor because of the limited number of respiratory specialists who cannot fully serve patients, so people often have difficulty if they want to consult directly. This triggers the habit of the community to treat complaints on their own with simple drugs bought freely at drugstores or pharmacies without knowing for sure the disease they suffer, as well as the length of waiting for queues, consultation fees that are quite expensive and not everyone has a short distance to the hospital prefer not to go to a specialist. Like other organs of the human body, breathing is also prone to various diseases. Respiratory organs will be disrupted and can even cause death. By using the Naïve Bayes method above, it is known that the diagnosis of respiratory disease is that the young female patient is diagnosed with a type of respiratory disease called Farangitis (P05) with a percentage of 47.44%.

Grouping is a process or activity to develop a system that is more organized and easy to understand, making it easier to analyze, identify or manage data and can also be used to explore information so that it becomes new knowledge for anyone who wants to obtain it. and in this case the information we want to explore is about MSME data in Binjai City. Namely, it is difficult to know how to identify existing business development patterns, whether they are not yet developed, less developed, already developed, and very developed. Offline and online promotions have not been optimal in increasing the growth and change of a business from time to time. And most MSMEs still don't understand how to market their products and services effectively and efficiently. MSMEs are one of the most numerous community business groups in Binjai City. To obtain this information, one solution that can be implemented is by utilizing data mining using input data in the form of Binjai City MSME data. This data will be processed using the clustering method with the k-means algorithm using MSME business type variables, sales type variables and development pattern variables. .Based on the results of grouping Binjai City MSMEs using the K-Means Clustering Method from 20 grouped data, 3 clusters and 2 iterations were obtained where cluster 1 contained 4 data and was located in the MSME business type group, namely the businesses included in this cluster were businesses in the field of Fashion, for the sales type group, uses online and offline types, and for business development patterns, it has a development pattern that has developed. cluster 2 has 11 and is located in the MSME business type group, namely the businesses included in this cluster are businesses in the culinary sector, for the sales type group the offline type is used, and for the business development pattern it has a development pattern that has developed. and cluster 3 has 5 data and is located in the MSME business type group, namely the businesses included in this cluster are businesses in the culinary sector, for the sales type group it is using the offline type, and for the business development pattern it has a less developed development pattern. so it can be concluded that the pattern of business development of Binjai City MSMEs produces relevant data so as to produce designs that can be used for this research.

Bullying is a verbal or non-verbal bullying activity through the media (cyber bullying) or directly, carried out by a child or group of children against other children. Aggressive behavior such as bullying among teenagers results in problems such as anxiety. The problem at SMKN 2 Binjai in 2024 is the difficulty of identifying children's anxiety about bullying disorders because there is no strong reference as evidence for cases of bullying carried out by perpetrators against victims. So research is needed to cluster gender, type of bullying and children's anxiety levels, with many parties still not monitoring children's activities enough to see how big the impact is on children who experience bullying. The aim of this research is to determine gender, type of bullying and different levels of anxiety among children who experience bullying. Based on the results of grouping bullying cases using the K-Means algorithm, 3 clusters and 3 iterations were obtained, where cluster 1 contained 9 data. , cluster 2 has 4 data and cluster 3 has 7 data, so it can be concluded that bullying cases tend to occur in women who experience types of bullying in the form of cyber and psychological with a mild level of anxiety.

The Binjai District Court is a government agency that has the duty and authority to receive, examine and decide every case registered at the Binjai District Court. The Binjai District Court handles many gambling cases, but data management is still not fast and accurate because it still uses manual methods, so the agency needs to implement an application system. To solve this problem, you can use data mining applications, namely by utilizing existing data to dig up new information. One of the techniques in data mining is clustering. Clustering was chosen because it can group data according to the desired characteristics, in this research it means grouping gambling data in the Binjai City area. The clustering algorithm used is K-Means Clustering integrated into a desktop-based programming application. The conclusion obtained is that the system designed has proven successful in grouping gambling data into 3 clusters (groups). The process using MATLAB R2014a obtained results in group 1 which amounted to 276 data with a data centroid center (6.92; 2.41; 4.33) including the category of low levels of gambling, group 2 which amounted to 337 data with a data centroid center (7.56 ; 2.10; 14.48) is included in the category of moderate level of gambling and group 3 which amounts to 387 data with the centroid data (7.56; 2.10; 28.02) is included in the category of high level of gambling.    

Promotion is an element used to inform and persuade the market about new products or services at the company through advertising, personal sales, sales promotions, or publications. However, due to the large selection of promotional media nowadays, agencies are required to continue to make decisions well, precisely and quickly, and to be accountable in making decisions. SMP-IT Mutia Rahma is one of the private schools in the Tanjung Perak sub-district. Until now, promotional activities for student admissions have only been carried out by visiting promotional destinations, such as putting up banners in public places, as well as distributing brochures to the public to help with activities. promotion. The method currently used is still considered very slow in disseminating information regarding promotion strategies for new student admissions, apart from that, this method also requires very large costs to increase promotional media, such as brochures and banners. The school should be able to see opportunities to promote promotional activities on social media. In addition, in the last two years there has been a decline in the number of students registering.