This research discusses the meaning of work and career for Generation Z. The research was conducted using qualitative methods and a phenomenon study approach. The research was conducted in Yogyakarta by involving 5 resource persons with fulfilling criteria, namely Generation Z who are workers and work in the Sleman area, Yogyakarta and are alumni of universities in Yogyakarta. The data analysis method uses Data Triangulation, namely, data reduction, data display and conclusions. Generation Z, known as digital natives, are proficient in using technology, have high creativity, future orientation, and the ability to collaborate. Access to technology allows them to explore a variety of work options that match personal interests and values. The mismatch between work meanings often makes Gen Z less likely to stay in their jobs. The issues addressed in this study are the meaning of work, the meaning of career, and the factors that influence careers for Generation Z. The research findings show that: 1) The meaning of work for Gen Z in the findings, on job orientation, is to earn money to fulfill life needs. In career orientation, it emphasizes the importance of seeking experience, self-development, increasing social status and expanding relationships to prepare for their future careers. While in calling orientation, describing work as a form of worship and giving benefits to others. 2) The meaning of career for gen Z in the findings is a means to achieve professional advancement, apply knowledge, develop skills, and contribute to positive change. 3) Factors that influence careers include internal factors including interests, talents, and knowledge, and external factors including family, friends, social media, work environment, and boyfriend or girlfriend.

Entrepreneurs engaged in the shopping sector have promising prospects because they can serve the lower and upper middle classes and provide convenience for people to buy everyday goods without having to go to supermarkets or convenience stores. However, if the availability of goods or materials needed is not optimally guaranteed, there may be a shortage of goods or materials needed. This also happens in some stores, where customers often run out of stock of various products and equipment they are looking for, but this is due to the lack of inventory management habits in the store. In this case, it is about finding out what products and needs are needed by store customers. This dataset uses several variables such as transaction date, product name, and sales or purchase amount by applying the apriori algorithm. The apriori algorithm is a type of association rule in data mining that is used to analyze and find correlation patterns. The data used in this study is a sample of 100 sales transaction data. The final association rule obtained from the transaction data is "If consumers buy Flour, they will buy Oil and Eggs" with a support percentage of 54% and a confidence of 96%. These results provide data on the names of the best-selling products, which can be used as an inventory estimate to avoid empty seats that can result in customer disappointment.

Sleep disorders, including insomnia, can be influenced by various lifestyle factors, such as sleep duration, sleep quality, physical activity, and individual health conditions. This study aims to categorize the risk level of insomnia based on lifestyle using the K-Means clustering algorithm. The data used include sleep duration, sleep quality, heart rate, and daily step count. Through the implementation of the K-Means algorithm, the data is analyzed to group individuals into several categories based on existing lifestyle patterns. The results of the study show a correlation between a healthy lifestyle and better sleep quality. In addition, the resulting clusters provide insight into lifestyle characteristics that affect the risk of insomnia, so that they can be the basis for recommendations for more targeted health interventions. This study is expected to contribute to the development of data-based sleep disorder management strategies by utilizing machine learning methods, especially the K-Means algorithm, to support efforts to improve the quality of life of the community.

The research aims to explore and understand energy consumption patterns in households. By using the Decision Tree algorithm, to classify the level of electrical energy consumption. And data on household electrical energy consumption can be obtained from various sources, such as Household electricity meter. Survey or questionnaire filled out by homeowners regarding the use of electrical appliances. Based on the image above, the application of the Decision Tree algorithm in analyzing risk factors for  The classification of household electrical energy consumption produces an accuracy value of 100.00%. From the displayed confusion matrix, we can see the distribution of predicted and actual values for various classes. For example, in the class "true 110 25," there are 17052 correct predictions. The evaluation results also show the precision and recall values for each class. The highest precision was achieved in the "true 2205" class with 100% recall, while the precision was found in the "true 122.5" class of 100.00%.

In the competitive world of business, identifying high-risk customers is critical to minimizing churn rates and increasing profitability. This research uses data mining techniques using the C4.5 decision tree algorithm to classify customers based on their churn risk. The research stages include data collection, cleaning, data processing, as well as dividing the data into training and testing sets. The implementation of this algorithm was carried out using RapidMiner software, which facilitates customer clustering and predicting behavior based on historical attributes. The evaluation results show the model has an accuracy of 74.59%, with precision and recall indicating the model's ability to identify high-risk customers. Thus, the Decision Tree C4.5 algorithm is proven to be effective in supporting decision making for customer churn risk mitigation strategies.

Grouping student learning outcomes is a strategic step to improve the quality of learning by understanding student achievement patterns in more depth. This study aims to analyze student learning outcomes at SDN 3 Ardirejo by applying the K-Means clustering method, which is designed to group data based on similarities in academic value characteristics from various subjects during one semester. The clustering results show the effectiveness of this algorithm in dividing students into high, medium, and low achievement clusters, making it easier for teachers to design adaptive learning strategies that suit the needs of each group. In addition, the information generated provides valuable insights for planning intervention programs, such as remedial learning for low-achieving students or enrichment materials for high-achieving students. This study contributes to a more systematic management of educational data at the elementary school level and is expected to be a reference for more effective decision-making, both at the school level and by educational stakeholders.

Clustering is a data mining technique used for grouping data based on specific similarities. This study implements K-Means Clustering to analyze foreign tourist visit data in Indonesia in 2024. Using the Knowledge Discovery in Database (KDD) methodology, the research involves five stages: Data Selection, preprocessing, Transformation, data mining, and Evaluation. Data Clustering was conducted using RapidMiner software, experimenting with different cluster counts (k=2 to k=7) to determine the optimal number of clusters. Results indicate that three clusters (k=3) with the smallest Davies-Bouldin Index (DBI) value were optimal. This Clustering approach categorizes tourists into low, medium, and high visit groups, assisting policymakers in strategic tourism development. The findings support capacity planning and seasonal marketing strategies to optimize Indonesia's tourism sector.

The research conducted is entitled Efforts to Develop Emotional Regulation Through Group Guidance with Role Playing Techniques for Students of SMK LPI Semarang. The research on Efforts to Develop Emotional Regulation Through Group Guidance Services with Role Playing Techniques for Class XI Students of SMK LPI Semarang is motivated by the low level of emotional regulation of students as indicated by the exclusion of these students from their peers, causing students to become sensitive to suggestions and criticisms from others. The purpose of this study was to determine Efforts to Develop Emotional Regulation Through Group Guidance Services with Role Playing Techniques for Class XI Students of SMK LPI Semarang. This type of research is quantitative research using the true-experimental method with a pre-test and post-test design model. The sample in this study was 21 students who then became the experimental group and the control group. The data collection tool used the emotional regulation scale. The results of the hypothesis test obtained significance (2-tailed) <0.05 indicating a significant difference between the control group and the experimental group. Meanwhile, the t-test result of 0.0064 showed a difference in the treatment given to the experimental group compared to the control group. It can be concluded that group guidance services with role playing techniques can help develop emotional regulation of SMK LPI Semarang students. Based on the results of this study, the suggestion that can be conveyed is that group guidance services with role playing techniques can be used as one of the treatment methods to develop emotional regulation.

This study analyzes the performance patterns of students at Madrasah I’dadiyah Sukorejo using data mining methods, specifically clustering. The analyzed factors include exam scores and participation in extracurricular activities, as both are considered to significantly influence academic performance. Exam scores reflect mastery of subjects, while extracurricular activities often positively impact students' social skills and learning motivation.[1] The K-Means algorithm was selected to classify students into three main groups: high-performing, average-performing, and low-performing students. The clustering results are expected to provide strategic guidance for the school to improve the quality of education. Low-performing students can receive additional guidance or motivational training, while average-performing students can be encouraged to participate more actively in extracurricular activities to enhance interpersonal skills. Understanding these performance patterns helps the school design more effective programs to maximize students’ academic potential based on their needs. This study also opens opportunities for further exploration of other factors affecting academic performance, such as family conditions and the home learning environment. Thus, this approach becomes an essential step in creating a more inclusive and high-quality education system.

To improve the evaluation process in assessing student progress, predicting the best grades plays a crucial role in enhancing the quality of education. By identifying the top-performing students, educational institutions can refine their teaching methods and create targeted strategies to foster better learning outcomes. This step is vital for ensuring that the learning process aligns with the institution's goals to produce highly skilled and knowledgeable students. In this research, we focused on utilizing the C4.5 algorithm, a widely recognized decision tree method in data mining, to predict student achievements. The C4.5 algorithm is known for its ability to classify and uncover hidden patterns within datasets, making it a powerful tool for educational data analysis. Through this approach, we aim to analyze the factors influencing student success and provide actionable insights for educators and administrators. The study was conducted on students from Madrasah Ta’hiliyah Ibrahimy, where we applied the decision tree algorithm to predict the best grades based on historical academic data. The experiment resulted in three distinct rules or patterns derived from the data, with an overall accuracy of 74.17%. These findings demonstrate the potential of data-driven approaches in supporting academic decision-making and guiding future interventions to further enhance student performance.