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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.

This study aimed to know the prediction of rice sales for Employee Cooperatives Republic of Indonesia Bina Warga Benjeng in the following month. Rice sales are often difficult to predict market demand. When consumer demand increases, rice supplies sometimes suffer from shortages. If consumer demand decreases, stock builds too much and results in a decrease in rice quality. In order for the rice sales process to run smoothly, it is necessary to have a sales prediction so that there are no excesses or shortage in rice supplies. The method of discussion used to predict in this study using the Single Moving Average method which is a prediction method that uses new actual data requests to raise the predictive value of the next month’s demand. The best results were using the Single Moving Average methods using rice sales data variant 25 kg variant were successfully implemented with an RMSE value of 9.3% which means this prediction accuracy of 90.7% accurate.

Cases of biological food poisoning can be caused by several causative factors, one of which is a food processing site that does not meet health requirements. According to the BPOM report (2016) cases of food poisoning in Indonesia in 2016 reached 1,068 cases. In 2016, 60 extraordinary events (KLB) of food poisoning were reported by 31 BB/BPOM throughout Indonesia. From the many cases of food poisoning that occur, it is necessary to take action in prevention by processing data on existing cases of poisoning to follow up on existing problems to reduce the number of cases of food poisoning by using a system on a computer so that the managed data can be processed quickly to obtain further information. Therefore the author wants to use a system with the clustering method to assist in processing data on biological poisoning cases grouping objects based on the characteristics of each object. Based on the research conducted, it can be seen that in cluster 2 in the dasta group of biological poisoning cases there are 11 data with centroid point age (x) 2, namely 12-16 years, centroid point on the type of poisoning (y) 6.36, namely sandwiches, and centroid point on the causative factor (z) 2.9, namely Gram-negative rod-shaped bacteria which are usually found in the intestines of humans and warm-blooded animals.

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.

Current developments in technology and information systems This is progressing quite rapidly. Electronic human resource management (E-HRM) as an organizational unit consisting of personnel who process human resource data using computer and non-computer technology. This study uses the literature review method related to business law (BL), economic analysis of law (EAL), and E-HRM research. Literature sources in this study used books, information sources from official government agencies and international organizations as well as scientific articles accredited by Science and Technology Index (SINTA) or SCOPUS. The search strategy uses keywords related to related topics. E-HRM is the embodiment of the intelligent society 5.0 culture in managing the staffing system. Various scopes of personnel management such as planning, administration, payroll and staff evaluation can be carried out effectively and efficiently in mining industry

Sustainability report disclosure is becoming increasingly important for companies, and this can be seen from several perspectives, including corporate social responsibility (CSR), and investor confidence. This study aims to analyze the effect of disclosure of environmental, social, and economic performance on company mining sector on the IDX for the period 2019-2023. Using quantitative method with multiple linear regression through SPSS 25, the sample was taken with purposive sampling with a population of companies in the mining sector listed on the IDX. Sustainability report disclosure is measured using the GRI G4 index, while firm value is measured by Market to Book Value. The results show that environmental, social, and economic performance each has a positive and significant effect on firm value. Simultaneously, the three variables also have a significant influence on firm value.

This study aims to examine the impact of each ESG (environmental, social, governance) aspect on the financial performance of mining companies listed on the Indonesia Stock Exchange, with ownership structure proxied by managerial ownership as a moderating variable. This quantitative research employs purposive sampling, resulting in a sample of 10 mining companies listed on the IDX for the period 2019-2023. Data analysis was conducted using SPSS version 22 with multiple linear regression and Moderate Regression Analysis (MRA) methods. The results show that both partially and simultaneously, all ESG variables affect financial performance, though the social aspect has a negative impact. Additionally, managerial ownership is proven to moderate the relationship between ESG aspects and financial performance.

This research aims to analyze the influence of financial distress, free cash flow, and earning power on earnings management. The sample for this research is mining companies listed on the Indonesia Stock Exchange (BEI), totaling 17 companies during the 2018-2022 period. Data was taken from the company's financial reports using a purposive sampling method. The type of hypothesis testing used is the T test (partial) with multiple linear regression analysis. The research results show that financial distress has a positive effect on earnings management, free cash flow has a negative effect on earnings management, while earning power has no effect on earnings management.

According to academic data, student math ability tests at MTSS PAB 5 Klambir Lima yield mixed results. There are students who understand math well, but there are also those who have difficulty understanding the mathematical concepts themselves. Math teachers at this school have difficulty designing lessons that can meet the needs of students with different levels of understanding. So, it is necessary to group student data to produce educational decision-making and improve learning effectiveness, such as through data mining. Data mining is a semi-automated process that uses machine learning techniques, mathematics, statistics, and artificial intelligence to identify and organize information contained in large databases. The process of finding information can be done by determining the decision rule based based on the level of student understanding in mathematics lessons using the Decision Tree Algorithm C4.5 method. The use of the Decision Tree algorithm C4.5 aims to make it easier to determine decision rules based on gender, Predicate, teacher teaching methods, student learning interest, and level of understanding. Based on the results of the study, it was found that if the teacher's teaching method is good, the predicate value is B, the student's learning interest is less interested, and the gender is male, then the student's level of understanding in mathematics lessons is not understood.

An abcess is a collection of pus in an indefinite space in the body, an abscess can appear on the surface of the skin and can appear in the tissues of an organ. Abscesses occur due to an infectious process or from parasitic bacteria due to foreign bodies, such as splinters, bullet wounds, needles. Many patients come with complaints of pain, swelling, redness, fever and others. Therefore, to overcome this problem, it is necessary to take quick action to help reduce and deal with the problem of abscess disease among the community by using the clustering method do that it can help agencies in conducting socialization so that the communinty knows more about the factors that cause abscess disease and how to handle it. From this research courced at tha Binjai estate Health Center which consists of several variables, namely age, type of abscess disease data that often appears, the abscess disease data that often appears after doing the 2 cluster process is with age is 26-35 years, with the type of abscess disease is dental abscess, and the casual factor is not maintaining dental hygiene.

This research aims to analyze the influence of growth opportunities, accounting conservatism, and company size on the earnings response coefficient. The sample for this research is mining companies listed on the Indonesia Stock Exchange (BEI), totaling 18 companies during the 2018-2022 period. Data was taken from the company's financial reports using a purposive sampling method. The classical assumption tests used consist of Data Normality Test, Multicollinearity Test, Heteroscedasticity Test, and Autocorrelation Test. Meanwhile, the types of hypothesis testing used are the T Test (partial), F Test (simultaneous), and Determination Coefficient Test (R2) with multiple linear regression analysis. The research results show that growth opportunity and accounting conservatism have no effect on the earnings response coefficient, while company size has a negative effect on the earnings response coefficient.

Soft skills are non-technical abilities that make a person able to interact and work effectively with others. This study aims to analyze the relationship between student activities of Internships and Certified Independent Study (MSIB) on improving student soft skills using the Apriori method in data mining analysis. this research uses RapidMiner analysis tools to analyze data collected from a total of 539 student data from all over Indonesia, the best association rule has been formed (best rule) which provides information about improving the soft skills of MSIB students. Tests were conducted by determining the minimum support value of 3% (0.03) and the minimum confidence of 30% (0.3). and resulted in 106 association rules. Based on the results of the analysis, it was found that the best rule of 2 itemsets has a support of 39% and a confidence of 67%, the best rule of 3 itemsets has a support of 13% and a confidence of 81%, the best rule of 4 itemsets has a support of 6% and a confidence of 82%, and the best rule of 5 itemsets has a support of 3% and a confidence of 100%.  After analyzing data using the Apriori method and RapidMiner application on 539 MSIB student soft skills data, it was found that there was a significant relationship between MBKM activities followed by students and the improvement of their soft skills and these findings also show that the less frequent value is set, the more data can be processed, as well as the minimum support value and confidence value, where the smaller the value determined, the more association results will be issued.

Student behavior is the actions of students which are influenced by their attitudes and responsibilities at school. Student behavior is a very important factor in determining student achievement in learning. Students who have personalities that improve skills, knowledge, attitudes, habits, understanding, skills, thinking power and other abilities will more easily increase their concentration in learning, and it will be easier to achieve the students' goals. At SDN 053960 MARYKE there are still students who do not know that student behavior greatly influences their level of achievement. Therefore, it is necessary to educate students from an early age so that students can be more responsible for the rules given by teachers at school, and students must understand that the attitude they carry out at school is assessed in improving their achievement, as well as their presence is very influential. his level of achievement at school. Therefore, there is a need for a solution to overcome the problems that exist at SDN 053960 MARYKE by utilizing data mining to collect data and then it will be processed using the a priori method with variables contained in the correlation between student behavior and student achievement levels. The a priori algorithm is able to determine min support and confidence in these variables will later show the relationship between student behavior and student achievement levels, so that researchers will get the best best rules and be able to produce the latest information..

The policy for using the MyPertamina application issued does not rule out the possibility of differences of opinion due to changes in the policy. There are many positive, neutral, and negative responses to the MyPertamina application implementation policy. To see the public's reaction to the MyPertamina application implementation policy, it can be seen through various media, including social media. Twitter is a social network that is widely used by people in Indonesia. The number of Twitter users in Indonesia reached 18.45 million in 2022, making Indonesia the fifth largest Twitter user country in the world. Researchers conducted a sentiment analysis of the search results for tweets containing the keyword "MyPertamina" using the support vector machine algorithm. 382 tweet data were obtained and classified using the support vector machine algorithm. Support vector machine is a supervised learning algorithm for data classification. SVM is very fast and effective in solving text data problems. Text data is suitable for classification with the SVM algorithm because the basic nature of text tends to be high-dimensional. Of the 382 data analyzed, the support vector machine classification using the RBF kernel with parameter C=2 gave the highest accuracy value of 80.51%, precision value of 81%, recall value of 81%, and F1 score value of 80%.

This study aims to explore the impact of macroeconomic fundamentals on sustainability disclosure in mining companies listed on the Indonesia Stock Exchange. The research utilizes financial performance, company attributes, and ownership structure as linking variables. The approach used is quantitative, employing secondary data from financial statements and sustainability reports of the companies. The population under investigation includes all mining companies registered on the Indonesia Stock Exchange from 2019 to 2022. The data analysis methods applied include path analysis and regression to examine the relationships between the various factors. It is hoped that the findings of this study will provide deeper insights into the factors influencing sustainability disclosure in the mining sector and offer relevant policy implications for companies and regulators in Indonesia.  

This research aims to examine the effect of dividend policy and accounting conservatism on earnings quality in companies in the infrastructure, utilities & transportation and mining sectors listed on the Indonesia Stock Exchange (BEI). This type of research is quantitative research. The sampling method used is a purposive sampling method based on certain criteria. From the specified criteria, a sample of 25 companies was obtained. The analytical method used is multiple linear regression analysis using IBM SPSS statistics 22. The results of this research indicate that the dividend policy variable has no effect on earnings quality. Meanwhile, accounting conservatism has a significant positive effect on earnings quality. The principle of accounting conservatism is applied by companies on the basis of management's caution against exaggerating the profit value of the financial statements.

The process of accepting new students generates a lot of data in the form of profiles of students who register. From year to year there is an increase in the number of prospective new students who come from several areas in Binjai City, Langkat Regency and surrounding areas, so the location of the socialization of new student admissions promotions every year is increasing and wider. And from several schools that have been visited and are expected to provide new prospective students, in fact, it is not proportional to the final number of prospective students who register. In this study, applying the K-Means Clustering algorithm using 3 variables namely, region, school origin, major. In determining the location of new student admissions promotions, the promotion team first identifies what factors will influence the determination of promotional locations ranging from region, school origin and majors that are considered to be set as promotional locations. Based on the results of grouping new student admission data of STMIK Kaputama Binjai using the K-means Clustering method from 20 data that has been processed, 3 clusters and 3 iterations are produced where cluster 1 has 9 data, cluster 2 has 2 data and cluster 3 has 9 data.

Pregnancy is a process in a woman's life, where major changes occur in her physical, mental and social aspects. These changes cannot be separated from the factors that influence them, namely physical factors, psychological factors and environmental, social, cultural and economic factors. One of the nutritional problems of pregnant women is chronic energy deficiency (KEK). Chronic energy deficiency (KEK) is a nutritional problem caused by a lack of food intake over a long period of time, a matter of years. Datar City Health Center is one of the agencies that provides health services for the local community and helps resolve problems with the health and nutritional development of mothers and children to prevent problems with malnutrition in pregnant women. The aim of the research is to make it easier for agencies to manage data and obtain complete information about the nutritional status of pregnant women. From 20 data, 3 groups were obtained, Cluster 1 had 4 data on the nutritional status of pregnant women, Cluster 2 had 4 data on the nutritional status of pregnant women and Cluster 3 had 12 data on the nutritional status of pregnant women. And the largest group obtained was cluster 3 with the data group on the nutritional status of pregnant women found in the gestational age group (X), namely 14-27 weeks old, with screening results (Y) namely adequate nutrition, and the causal factors (Z) that occurred were economic factors

This research aims to determine the influence of bonus mechanisms, debt covenants, and profitability on transfer pricing. The population in this study are mining sector companies listed on the Indonesia Stock Exchange in their financial reports for the 2019-2023 period. The sampling technique in this research used a purposive sampling method and a sample of 57 companies was obtained. The analysis technique used is multiple linear regression analysis using SPSS version 22.0 software. The results of this research show that the bonus and debt covenant mechanism has a positive and significant effect on transfer pricing and profitability has a negative and significant effect on transfer pricing.