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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.

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 rapid development of technology in the globalization era has significantly impacted various aspects of life, including the healthcare sector. RSU Bidadari Binjai, as a healthcare provider, faces challenges in diagnosing and preventing Gastroesophageal Reflux Disease (GERD), a condition with high prevalence and serious complications such as Barrett’s esophagus and esophageal cancer. Therefore, a predictive system capable of early detection is needed to ensure quicker and more effective medical intervention. This research develops a computer-based predictive system using the backpropagation method in artificial neural networks to assist in diagnosing GERD by processing patient symptom data. The system's test results show an accuracy rate of 100% in predicting GERD complications based on the given symptoms, thus supporting more timely and accurate medical interventions.    

The Apriori method is one of the algorithms used in data mining to find association patterns, such as "association rules", in large data sets. This method was developed by Rakesh Agrawal and Ramakrishnan Srikant in 1994. The purpose is to test the correlation between facial skin problems and the type of product used by finding min support and min confidence using the apriori method.The results obtained based on this analysis are that there are 2 rules that meet the minimum requirements to form a combination of 2 itemsets with a minimum support value of 95% and a minimum confidence of 100%.  

This research aims to evaluate the governance of the Breksi Cliff Tourism destination in Yogyakarta based on the Sapta Pesona indicators. Breksi Cliff, which was originally a mining site, has developed into an attractive geological tourism destination thanks to the creative efforts of the local community. This research used a survey method by distributing questionnaires to 170 tourists who visited Tebing Breksi. The analysis was carried out using Importance Performance Analysis (IPA) to identify gaps between the level of importance and performance of Sapta Pesona indicators consisting of security, order, cleanliness, coolness, beauty, friendliness and memories. The results of the analysis show that most of the indicators are in quadrants I and II, which means they are considered important by tourists but their performance varies. Indicators such as staff knowledge, cleanliness of toilets and environmental planning are considered important and have good performance, while road access and prompt service require improvement. Based on these results, it is recommended to maintain good performance and improve aspects that have not met tourist expectations in order to improve the quality of tourism and the competitiveness of Tebing Breksi as a geological tourism destination.

The rapid development of technology increases the need for effective and efficient information. Information that is not managed properly loses value, especially when large amounts of data are available, making conventional methods no longer adequate to analyze the potential of the data. Therefore, a system capable of analyzing, summarizing, and extracting data into useful information is required. The Department of Agriculture and Food Security, as an agency that handles food security, agriculture, animal husbandry, animal health, and fisheries, is responsible for supporting the increase in agricultural yields to meet the food needs of the population and encourage economic growth. To achieve this goal, the agency needs to utilize technology to process agricultural data quickly and accurately. The system built using the apriori method can analyze data efficiently and provide recommendations for increasing agricultural yields. Based on the test results, a support value of 9% and a confidence of 68% were obtained, with the rule If the crop is Cassava, then the production yield is 6000-8000 tons.

This research aims to cluster disease data based on patient age using the K-Means method at RSUD Dr. RM. Djoelham. In this case study, the clustering method with the K-Means algorithm is used to group patients based on patient age, address and type of disease. With this method, information can be obtained regarding patient grouping patterns based on age at Dr. RM. Djoelham, who helps identify the closest relationships between patient groups and provides insight into the distribution of disease across age groups, regions and types of disease suffered.This research was conducted at RSUD Dr. RM. Djoelham by loading data from patients treated at the hospital. The data used is 1,100 patient data from 2022-2024 which has been recorded by the hospital. This patient data will be analyzed using 3 variables in the research, namely Patient Age (C1), Address (C2), and Type of Disease (C3). With the results, cluster 1 contains 320 data, cluster 2 contains 326 data, and cluster 3 contains 454 data.

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.

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%.

Business legality is the identity of a business that legalizes a business so that it is recognized by the community. Business legality must be valid according to applicable laws and regulations so that the business can be protected by various documents that are valid in the eyes of the law. One of the supporting factors for the sustainability of a business is influenced by the existence of legal elements of the business being run. Business permits that must be owned by the community are a business establishment deed, business entity NPWP, trade business license (SIUP), company domicile certificate (SKDP) and business registration number (NIB). The increase in community businesses in Sei Bingai District, Langkat Regency has triggered many business permits that are not directly supervised by the local government. Community business permits are important documents in supervising the running of these community businesses. The types of businesses in Sei Bingai District also vary, such as tourism, C mining, trade, factories and so on.

Personal Protective Equipment (PPE) is essential for worker safety, especially in oil palm harvesting activities. PT Langkat Nusantara Kepong faces major challenges related to work safety, with analysis showing that work accidents still occur frequently in the Padang Brahrang Plantation. This indicates the need for an in-depth evaluation of the use of Personal Protective Equipment (PPE) to reduce the risk of work accidents. By using the clustering method to group data based on the type of Personal Protective Equipment (PPE) used and aims to provide recommendations for optimizing the use of personal protective equipment based on risk management and reducing the incidence of work accidents. From testing the results of cluster 3, cluster 4 and cluster 5 it can be concluded that clustering with 5 clusters provides the most efficient and precise results, followed by 4 clusters, while 3 clusters provide greater variation within clusters, indicating that clustering with fewer clusters is less able to capture subtle differences in the data.

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.

The development of Information Technology is now entering various sectors, including health, and is implemented in Bidadari Binjai Hospital. As a health institution that is committed to excellent service and quality, Bidadari Binjai Hospital needs to innovate technology. One health issue that requires attention is rubella, an airborne infectious disease that has the potential to cause serious disorders such as hearing loss, cataracts, speech delay, and heart failure in toddlers and children. The initial symptoms of rubella are often similar to other common diseases, so public understanding of these symptoms is very important for quick treatment. This research aims to develop an information technology-based system that is able to predict rubella using the backpropagation method. This method is expected to improve the accuracy of diagnosis and make it easier for people to recognize rubella symptoms early on. The proposed system aims to provide better diagnosis support at Bidadari Binjai Hospital, as well as increase public awareness and knowledge about rubella disease. From the research conducted, the results of the accuracy rate obtained when conducting a test program were selected according to the symptoms and the results obtained were rubella disease with a 100% accuracy rate.

This study aims to explore and analyze in-depth the transformation of the Tamsu community due to the presence of gold mining activities in the surrounding area. The research was conducted from May 2024 to July 2024 using a qualitative descriptive method. Data was collected through observation and interviews with key informants such as community leaders, village officials, miners, and the general public around the Tamsu Gold Mining Site. The results show that the presence of large-scale gold mining using modern equipment such as excavators has significantly transformed the socio-economic aspects of the Tamsu community. On one hand, there has been an increase in income for some community members who work in the mines. However, this has also led to negative impacts, such as illegal logging, loss of agricultural land, and water pollution.

This study aims to determine the relationship between leverage, company characteristics, and financial performance with illegal tax aggressiveness in mining companies listed on the Indonesia Stock Exchange in 2019-2022. The research method used is quantitative with secondary data. The population of this study are mining sector companies listed on the Indonesia Stock Exchange in 2019-2022. The sample obtained was 10 companies using purposive sampling method. Data analysis using SmartPLS. The results showed that leverage, company characteristics, and financial performance had no significant effect on tax aggressiveness. This means that mining companies that have high leverage, poor company characteristics, and poor financial performance do not tend to practice tax aggressiveness. In addition, good corporate governance cannot moderate the relationship between leverage, company characteristics and financial performance with tax aggressiveness. This means that leverage, company characteristics and financial performance still have a significant influence on tax aggressiveness, even though the company has good corporate governance.

mining activities are an important sector in the extractive industry which has the potential to make a significant economic contribution. However, in Gondang sub-district, Mojokerto Regency, East Java, C excavation mining activities also face challenges in terms of legal accountability. This research aims to analyze legal aspects related to the accountability of C excavation mining business actors in the area. The research methods used are literature studies and interviews with related parties, such as local government, business actors and the community. The research results show that there are a number of legal problems related to licensing, implementation of activities, and environmental impacts. Business actors often ignore legal obligations related to business permits, environmental protection and corporate social responsibility. The monitoring and law enforcement system at the local level also still experiences obstacles which result in a lack of effective sanctions for violations. Recommendations from this research include increasing coordination between relevant parties, implementing stricter environmental audits, and stricter law enforcement to ensure compliance with applicable regulations. With these steps, it is hoped that excavation C mining activities in Gondang District can take place sustainably and provide optimal benefits without harming the environment and society.

A library is a facility or place that provides reading materials. Good book arrangement can help the library in obtaining good reading sources. The arrangement of library service book collections based on borrowing patterns, there is an alignment between user needs and the availability of reading materials available in the library. Analysis of book borrowing patterns provides valuable insights for library staff in determining the books that are most in demand and often needed by users. Data mining is defined as mining data or efforts to dig up valuable and useful information in a very large database. The most important thing in data mining techniques is the rule for finding high frequency patterns between sets of itemsets called Association Rules. The method used in this study is Apriori (Association Rule). This technique is used to find relationships or associations between items or variables in data. Well-known algorithms such as Apriori and Eclat are used to find association rules in transactional data. The purpose of this study is to find out library visitor data using the Apriori Algorithm method and to find out the application of data mining for compiling book collections based on borrowing patterns. The results of this study are the multiplication of support and confidence, choose the one with the largest multiplication result. The largest result of the multiplication of these multiplications is the rule used when borrowing books. Because the results of the multiplication of the 4 borrowings have the same value, all of them can be used as rules.  

Healthy lifestyles are habits of doing something, be it food, healthy behavior so as to avoid the disturbance of all kinds of diseases, both physical and non-physical diseases, as well as birth control users must also strive for a healthy lifestyle, such as managing a healthy diet, rest, exercise, eating vegetables and fruits, doing optimal physical activity, not consuming alcohol, and maintaining a healthy body. In this problem, many family planning users do not pay attention to a healthy lifestyle because they think that the family planning tools used have no risk to health, but the use of family planning has side effects on health such as menstruation is not smooth, the body is obese, the body feels warm or feverish, there are blood clots, nausea, bloating, changes in vision, difficulty in getting back to normal, headaches, and others. To be able to attract the attention of the community in implementing a healthy lifestyle for family planning users, it is very necessary to have a system that can help people in changing their unhealthy lifestyle to a healthier one by grouping family planning user data based on variables that have been determined using the clustering method, to group data on healthy lifestyles for family planning users which later the results of this study can be used as input and guidance for a healthy lifestyle for family planning users, so that family planning users are more careful and have a healthy life. Of the 20 data, there are 3 groups, namely group 1 there are 4 data and group 2 there are 4 data and group 3 there are 12 data from the above results it can be seen that in cluster 3 is a group on family planning users based on a lot with a total of 12 data and is located in the contraceptive type group (X) is injectable birth control, and for the lifestyle group (Y), namely Frequent Night Baths and Risk (Z), namely Decreased Bone Strength.  

In Indonesia, people with disabilities are often overlooked and underestimated because they do not have perfect physical abilities to do certain jobs or activities. The majority of them come from underprivileged families and are often underdeveloped. The unstructured process of distributing assistance can result in the assistance provided is not in accordance with the needs, so it is not optimal in improving the welfare of persons with disabilities. In addition, without a clear grouping, it is difficult for the government to design a more specific and targeted assistance program. Therefore, to overcome this problem, the agency needs to have an additional system to be able to assist in overcoming the problem of disability assistance recipients, namely by using the clustering method to group beneficiary data based on age, type of disability, and type of assistance. Thus, this clustering is expected to provide information and a clearer picture of the needs of each disability group, so that the assistance program provided can be distributed more optimally according to what people with disabilities need. After calculating using the existing cluster formula4, iteration 2 is the same as in iteration 1 and there is no data that moves groups anymore so the calculation can be stopped. So that a cluster graph can be made grouping data on beneficiaries of assistance for disabilities in Binjai City using the K-Means algorithm clustering method.

The main goal of designing the Society 5.0 concept is to build a human-centered society, balance economic and social progress, and create new value through technological developments. The development of human resources (HR) in the era of digitalization has become the focus of attention of many companies. In this era, information and communication technology has changed the way of working and interaction between individuals and organizations. Therefore, the main focus of this article is to find out how the relevance of the goals to be achieved in the Society 5.0 era to the aspects needed in human resource management, namely Complex Problem Solving (CPS), Social Skills (SS), and Process Skills (PS) in Human Resource Behavior (PSDM) and Digital Marketing (PD) which are studied using qualitative research methods. The results of the study show that in synthesis, Complex Problem Solving, Social Skill, Marketing, and Process Skill are three very important abilities in HR behavior in the Society 5.0 era. They help HR to solve complex problems, interact with others, and manage industrial processes effectively and efficiently. The relevance of the goals of the Society 5.0 era to Complex Problem Solving, Social Skills, Marketing, and Process Skills in PSDM and PD is very significant. The Society 5.0 era prioritizes the use of technology to improve the quality of human life, so human resources must have the ability to adapt to technological changes and build competencies that are in accordance with the needs of organizations in the mining world.