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This study aims to analyze the influence of social support and soft skills on the work readiness of the 2022 cohort students in Tasikmalaya City. Work readiness is an essential aspect that students must possess to face increasingly competitive labor market demands. This research employed a quantitative approach using a survey method. Data were collected through an online questionnaire distributed to 110 respondents selected from a population of 150 students using the Slovin formula with a 5% margin of error and a simple random sampling technique. Data analysis was conducted using multiple linear regression with the assistance of IBM SPSS Statistics 25. Prior to hypothesis testing, validity and reliability tests were performed to ensure the quality of the research instruments. In addition, classical assumption tests, including normality, multicollinearity, heteroscedasticity, autocorrelation, and linearity tests, were conducted to verify the suitability of the regression model. The findings indicate that both social support and soft skills have a positive and significant effect on students’ work readiness. Support from family, peers, and the academic environment enhances students’ confidence in preparing for employment. Furthermore, communication skills, teamwork, problem-solving abilities, and responsibility as components of soft skills strengthen students’ readiness to enter the professional workforce. These findings highlight the importance of developing soft skills and strengthening social support to improve students’ work readiness.

The quick advancement of digital technology has drastically changed the social and religious life of Indonesian teenagers. The purpose of this study is to investigate how pupils at Al Muslim Junior High School's daily worship practices, self-control, and fear of missing out (FoMO) affect the principles of Islamic Religious Education (PAI). The study used a quantitative methodology with a causal and correlational design. All students in grades VII, VIII, and IX made up the study population for the 2025–2026 school year. Using the Slovin formula, a proportionate stratified sample of 171 students with a 5% margin of error was chosen. A five-point Likert scale questionnaire was used to gather data. The Pearson Product-Moment correlation (r > 0.30) was used to evaluate validity, while Cronbach's Alpha (α > 0.70) was used to test reliability. Multiple linear regression using SPSS version 26 was used for quantitative analysis, beginning with traditional assumption tests for heteroscedasticity (Glejser), multicollinearity (VIF), and normality (Kolmogorov-Smirnov). According to the analysis results, self-control had a substantial, favorable impact on the practice of PAI values, but FoMO had no significant influence (β = -0.034, p = 0.530).

Scientific writing is one of the essential skills that college students need to support research activities and the development of knowledge. However, students still encounter various challenges during the writing process. This study aimed to identify students’ responses toward scientific activities at FMIPA Universitas Negeri Surabaya and to examine the difficulties experienced by students in preparing scientific papers. The study employed a qualitative descriptive method using a purposive sampling technique. The research subjects consisted of 36 students from various study programs at FMIPA Universitas Negeri Surabaya who had been involved in research activities and scientific proposal writing. Data were collected through student response questionnaires consisting of closed-ended and open-ended questions. The data were analyzed descriptively and presented in tables and narrative descriptions. The results showed that most students had understood the existing student research programs and were familiar with the use of research guidelines. However, students still faced difficulties in systematically organizing research ideas, determining research variables and methods, finding relevant literature reviews, and connecting theories from various references. In addition, time management and the use of citation management applications were also obstacles for some students. Therefore, more structured assistance is needed through scientific writing training, reference searching training, and the use of citation management applications to improve the quality of students’ scientific writing.

This study aims to compare the performance of Multiple Linear Regression (MLR) and Random Forest Regression (RFR) in predicting student performance based on academic scores. Student performance is defined as the average of math scores, Reading Scores, and writing scores. This study uses a quantitative approach with a comparative design based on predictive modeling. The data used is secondary data from the Student Prediction dataset obtained through the Kaggle platform, which was processed using the Python programming language through the Google Colab platform. The analysis stages included the formation of performance variables, the separation of training and test data with a ratio of 80:20, model training, and evaluation using the Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R²) metrics. The results show that the Multiple Linear Regression model produced an MSE value of 2.74 × 10⁻²⁸, an MAE of 1.51 × 10⁻¹⁴, and an R² of 1.000. Meanwhile, Random Forest Regression produced an MSE of 0.296, an MAE of 0.375, and an R² of 0.998. These findings indicate that both models have a very high level of accuracy, but Multiple Linear Regression provides the best performance. This is due to the strong linear relationship between the input variables and the target variables formed directly from the combination of academic values. Thus, the linear regression model is proven to be more suitable for use in data structures that have simple linear relationships compared to ensemble-based models.