Unraveling FOMO: Exploring the Factors Behind Fear of Missing Out among College Students

The increasing use of social media among college students can trigger mental health problems such as FOMO (Fear of Missing Out). There are not many studies that look at the relationship between the intensity of social media use and FOMO in college students. This study aims to provide an overview of FOMO among college students and assess the factors associated with FOMO. This quantitative study used a cross-sectional design with purposive sampling. Data collection was conducted through an online survey filled out by 104 college students. The variables measured were respondent characteristics, duration of social media use, number of social media accounts, and FOMO scale. The results of this study showed that 73.1% of students were classified as high duration users, 77.9% had less than 10 accounts, and 43.3% had high FOMO. There was a significant relationship between age (p=0.003) and undergraduate program (p=0.001) with FOMO. This study also found the significant relationship between duration (p=0.012) and number of accounts (p=0.007) with FOMO. There was a significant relationship between age, undergraduate program, duration, and number of accounts with FOMO in undergraduate students

The Role of Suicide Literacy and Suicide Stigma in Shaping Attitudes toward Seeking Professional Psychological Help among Indonesian Emerging Adults

Suicide is the fourth leading cause of premature death among emerging adults, with significant implications for public health. In Indonesia, the prevalence of suicide cases has reached 6,544, although this figure likely underrepresents the true extent of the issue. Alarmingly, only a small percentage of Indonesian adolescents, approximately 2.6%, seek psychological assistance despite the pressing need for mental health support. This study investigates the roles of suicide literacy and stigma in shaping attitudes toward seeking professional psychological help. Data were collected through an online survey involving 397 respondents. The analysis utilized multiple linear regression to assess the contributions of suicide literacy, stigma, and various demographic factors to attitudes towards seeking professional psychological help. Findings indicated that while suicide literacy, stigma, and demographic variables collectively contribute to attitudes toward seeking professional help, only suicide stigma and demographic factors (college major and family relation) significantly influenced these attitudes. This study underscores the critical need to address and reduce suicide stigma as a means of fostering positive attitudes toward seeking professional psychological help among emerging adults in Indonesia

Early Mobilization Therapy Robot for Medical Rehabilitation Purpose

Impairments in ambulation may result from neurological dysfunction. The expense of therapy constitutes a substantial obstacle to recovery following neurological disorders. An uncomplicated and cost-effective two-degree-of-freedom early mobilization trainer robot has been conceived and constructed. This device is intended for early training or adaptation before ready for mobilization training on the ground. The early mobilization trainer assists persons with mobility impairments during their early therapy phase. This research analyses the design and construction of an early mobilization trainer positioned within the patient's bed. The experimental findings indicate that in the condition with load at the hip joint, the output of this device can follow the trajectory input precisely. For the knee joint, the output of this device can follow the trajectory input, but with 0.9 degree of a steady-state error. This amount of steady state error does not affect the therapy because it is too small in term of knee movement precision during therapy

An Indonesian Perspective of Father’s Involvement in Children’s Education: The Role of Religiosity, Marital Satisfaction, and Father’s Self-Efficacy

In Indonesia, traditional social norms continue to emphasize the father as the dominant and authoritative figure in family life. However, research on fathers' involvement in children's education and the factors influencing it remains limited. This study investigates the alignment between a theoretical model of father involvement and empirical data, specifically examining the impact of religiosity, marital satisfaction, and fathers' self-efficacy on their participation in children's education. The study surveyed 393 fathers of state junior high school students in Malang, Indonesia, using four standardized instruments: the Father’s Involvement Scale, Religiosity Scale, Marital Satisfaction Scale, and Father’s Self-Efficacy Scale. Structural equation modeling (SEM) analysis confirmed a strong fit between the theoretical framework and empirical findings. Results indicate that religiosity has a direct influence on father involvement and marital satisfaction but does not significantly affect involvement indirectly through marital satisfaction. Marital satisfaction, in turn, has a significant impact on both father involvement and self-efficacy, while self-efficacy directly contributes to greater involvement in children's education. Based on these findings, schools are encouraged to actively engage fathers in educational activities to enhance their role in adolescent education. Given the study's quantitative approach, future research should consider a mixed-methods design to provide a more comprehensive understanding of father involvement in adolescent education

Impact of Feature Selection on XGBoost Model with VGG16 Feature Extraction for Carbon Stock Estimation Using GEE and Drone Imagery

Carbon stocks are critical to climate change mitigation by capturing atmospheric carbon and storing it in biomass. However, carbon stock estimation faces challenges due to data complexity and the need for efficient analytical methods. This study introduces a carbon stock estimation method that integrates the XGBoost algorithm with VGG16 feature extraction and feature selection techniques to analyze GEE and Drone image datasets. The model is evaluated through four scenarios: without feature selection, using Information Gain, using Feature Importance, and using Recursive Feature Elimination. These scenarios aim to compare feature selection methods to identify the best one for processing complex environmental data. The experimental results show that RFE significantly outperforms other methods, achieving an average RMSE of 6651.62, MAE of 2297.57, and R² of 0.7673. These findings underscore the importance of feature selection in optimizing model performance, particularly for high-dimensional environmental datasets. RFE shows superior accuracy and efficiency by retaining the most relevant features but requires more computational resources. For applications that prioritize time and resource efficiency, Information Gain or Feature Importance can serve as a practical alternative with slightly reduced accuracy. This research highlights the value of integrating feature selection techniques into machine learning models for environmental data analysis. Future research could explore alternative feature extraction methods, combine RFE with other approaches, or apply advanced techniques such as Boruta or genetic algorithms. These efforts will further refine carbon stock estimation models, paving the way for broader applications in environmental data analysis

DEVELOPMENT OF A SCIENTIFIC LITERACY TEST INSTRUMENT ON THE IMMUNE SYSTEM

This research aims to develop a scientific literacy test instrument on immune system material. Borg & Gall research and development modified by Sugiyono is the research methodology used.. The questions developed were 25 multiple-choice questions created using 9 indicators from the TOSLS instrument developed by Gormally, Brickman, and Lutz. The questions were tested on 75 class Xl students at SMA Negeri 7 Pontianak who were selected using a simple random sampling technique. Rasch model analysis was used to analyze the questions. The content validity results stated that it was valid (0.92) and had good interrater reliability (0.88). Item validity (item fit) shows 25 valid. Reliability shows the Cronbach's Alpha category is bad (0.50), the person reliability category is weak (0.46), the item reliability value is very good (0.94). The person separation value is 2 and the item separation value is 6. The difficulty of the questions (item measure) demonstrates Six questions are extremely difficult, six questions are difficult, eight questions are easy, and five questions are extremely easy. From the results of the analysis, It can be conclude that the questions are valid and reliable even though there are questions that need to be revised.Keywords: Scientific Literacy test instrument, 9 TOSLS indicators, Rasch Analysi

Tailoring Data Storage Configuration for Efficient Fraud Detection Model Training

The rapid growth of e-commerce in Indonesia, with a record 88.1% growth rate, has been accompanied by a surge in online fraud, leading to an estimated loss of 4.62 trillion rupiahs. Current fraud prevention methods, such as the widely used 3D-Secure system, though effective, result in a high rate of transaction abandonment (approximately 16%), which is undesirable for merchants. To address this, we propose an AI-based fraud detection system that leverages machine learning models to identify potentially fraudulent transactions. By employing a combination of classification algorithms, including logistic regression and neural networks, security protocols are activated only for high-risk transactions, optimizing transaction processing efficiency and improving detection accuracy. Our study focuses on fine-tuning key parameters of the AI-Fraud Detector model, specifically some parameters such as ∆ttrain, ∆tlag and f rac hr pass, to enhance detection performance over time. Simulation performances using ROCAUC, false positive rate (fpr), and true positive rate (tpr) metrics show that a configuration with a training period (∆ttrain) of 180 days, a lag period (∆tlag ) of 90 days, and a high-risk pass fraction (f rac hr pass) of 10% yields a balance between detection efficiency (∼ 50%) and a reduced false positive rate. It means that the model is able to identify approximately 50% of the actual high-risk events while minimizing the number of times it incorrectly identifies a low-risk event as high-risk. However, further research is required to refine these results, explore parameter optimization strategies, and enhance the model’s adaptability to evolving fraud patterns. Future work will focus on optimizing thresholds, improving model robustness over time, and ensuring effective detection of new fraud schemes. This research improves model performance by optimizing key parameters and enhancing detection accuracy while minimizing false positive

Enhancing DenseNet Accuracy in Retinal Disease Classification with Contrast Limited Adaptive Histogram Equalization

Retinal diseases are serious conditions that can cause vision impairment and, in severe cases, blindness, affecting 6.3% to 17.9% of cases per 100,000 people annually worldwide. Early diagnosis is crucial but often time-consuming, prompting the use of Artificial Intelligence (AI) models like DenseNet, part of the Convolutional Neural Network (CNN) architecture, to streamline the process. This study utilizes the Retinal OCT Images dataset from Kaggle, comprising 83,600 images categorized into four classes. To address the low contrast in Optical Coherence Tomography (OCT) images, the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique was applied during preprocessing. Results indicate that DenseNet without CLAHE achieved an accuracy, precision, recall, and F1-score of 95%, while incorporating CLAHE improved these metrics to 98%. The application of CLAHE also reduced classification bias and error, enhancing model reliability despite requiring more training epochs (43 compared to 39 without CLAHE). These findings demonstrate the potential of CLAHE to optimize DenseNet performance in retinal disease classification. Future research could explore other image enhancement techniques or apply the method to different retinal disease datasets, contributing to improved diagnostic accuracy in clinical settings

Validation of Short Form Social and Loneliness Scale for Adults (SELSA-S) Indonesian Version

Dissatisfaction in a relationship leads to both emotional and social loneliness. The emergence of loneliness stems from a mismatch between the quality of the relationship and expectations. A study was conducted to evaluate the validation of the Short Form Social and Emotional Loneliness Scale for Adults (SELSA-S) Bahasa Indonesian version, based on internal structure and its relationship with other variables. Using secondary data from 155 active students, the exploratory factor analysis (EFA) grouped 14 SELSA-S items. The grouping of social, romantic, and family dimensions in the Indonesian SELSA-S aligned with the initial design and the validity of SELSA-S in Brazil, Slovakia, and Turkey. All dimensions correlated significantly with the neuroticism variable, indicating that loneliness is positively associated with anxiety, depression, and emotional instability. The validation was supported by consistent reliability tests, confirming that the Indonesian version of SELSA-S is reliable in accurately measuring loneliness

Evaluating the Physical Mobility Levels and Well Being of Elders: Insights from the Countryside Setting of the Philippines

The pandemic significantly affected the health and well-being of elders, especially in disadvantaged communities. This study examines the physical mobility and well-being of elders aged 60-80 in Samar, Philippines. A descriptive research design was used to survey forty (40) voluntarily participating members of the Senior Citizens’ Organization. Physical mobility in both indoor and outdoor activities was assessed, with well-being self-reported by participants. Results showed a high percentage engaged in stretching and walking exercises, while the majority had irregular sleep patterns. However, many did not engage in household chores, market visits, or entrepreneurial activities. The study recommends establishing targeted educational and community-centric intervention programs among Local Government Units (LGUs) to enhance the physical health, cognitive functioning, and overall well-being of elders