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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).

Nahwu (Arabic grammar) is a fundamental discipline in learning the Arabic language, helping to prevent errors in speech and serving as a primary key to clearly understanding Islamic legal texts. However, in practice, the teaching of Nahwu often faces complex challenges, particularly the lack of active participation among female students and their uneven mastery of the subject. Therefore, this study aims to explore the problems in teaching Nahwu at Al-Madani Islamic Boarding School in Cikalong and to identify the factors causing these issues. This study aims to: (1) reveal the problems faced by second-grade female students of KMI in learning Nahwu at Al-Madani Islamic Boarding School in Cikalong, and (2) identify the contributing factors influencing the emergence of these problems. This research employs a descriptive qualitative approach. The researcher used three data collection techniques: classroom observation, in-depth interviews with the Nahwu teacher, and questionnaires distributed to the students. For data analysis, the researcher applied the Miles and Huberman model, which consists of data reduction, data display, and conclusion drawing. To ensure data validity, data triangulation techniques were used. The results of the study indicate that the problems are divided into two main aspects: (1) learning-related problems, including the gap between memorization of grammatical rules and their application (i‘rab), high levels of academic anxiety, and decreased classroom concentration; and (2) contributing factors, including methodological factors, linguistic-psychological factors, and classroom management factors. The researcher suggests that teachers adopt more interactive teaching methods and media, as well as innovative visual tools to reduce the abstract nature of Nahwu materials and enhance students’ functional understanding. Additionally, activating “Halaqah Nahwiyyah” (grammar study circles) in dormitories is recommended to overcome psychological barriers and improve students’ linguistic confidence.

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.