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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan International Journal of Multidisciplinary: Applied Business and Education Research
Valenzuela, Victoria Pena
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Environmental Science Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation

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The Road to Self-Leadership: Teachers’ Mindful Awareness, Engaging Leadership, Commitment, Work Engagement, and Need Satisfaction Valenzuela, Victoria Pena; Daez, Francisco DP.
International Journal of Multidisciplinary: Applied Business and Education Research Vol. 5 No. 12 (2024): International Journal of Multidisciplinary: Applied Business and Education Res
Publisher : Future Science / FSH-PH Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/ijmaber.05.12.13

Abstract

The purpose of the study is to examine which of the following var-iables: mindful awareness, engaging leadership, and commitment of women teachers significantly influence work engagement and basic needs satisfaction. The study employed the descriptive-correlational research design and N=273 was purposively selected teachers enrolled in the Graduate School of Meycauayan College, Bulacan, Philippines. Mean, weighted mean, Pearson r, and multi-ple linear regression are the statistical tools used in analyzing and interpreting the data. Findings reveal a significant relationship between mindfulness awareness, engaging leadership, and com-mitment to work engagement and basic needs satisfaction of fe-male teachers, The study was able to determine that commitment predicts the teachers' work engagement and basic needs satisfac-tion. Implications for professional development that boost self-leadership and mindfulness training programs are integrated into teachers’ wellness policies to ensure their work engagement and satisfy their basic needs.
A MACHINE LEARNING FRAMEWORK FOR SUICIDAL THOUGHTS PREDICTION USING LOGISTIC REGRESSION AND SMOTE ALGORITHM Berliana, Sarni Maniar; Samosir, Omas Bulan; Karim, Rafidah Abd; Valenzuela, Victoria Pena; Wahyuni, Krismanti Tri; Alfian, Andi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1409-1420

Abstract

Suicide, a global health challenge identified in Goal 3 of the global agenda for enhancing worldwide well-being, demands urgent attention. This study focused on predicting suicidal thoughts using machine learning, leveraging the 2021 National Women's Life Experience Survey (SPHPN) involving women aged 15 to 64. Analyzing 11,305 ever-married women, 504 (4.5%) reported experiencing suicidal thoughts. The outcome variable was binary (1 for suicidal thoughts, 0 for none). The study used seven predictors: age, education level, residence type, physical and sexual violence, smoking frequency, alcohol consumption, and depression. Ordinary logistic regression and SMOTE-based logistic regression were applied. The former identified physical violence, depression, and sexual violence as crucial factors, while the latter emphasized physical violence, sexual violence, and age. In cases of class imbalance, the SMOTE-enhanced model exhibited improved performance in terms of sensitivity, false positive rate, balanced accuracy, and Kappa statistic, with lower standard errors of parameter estimates. The findings highlight the importance of addressing violence and mental health in policies aimed at reducing suicidal thoughts among women. Policymakers can use these insights to develop targeted interventions, and healthcare providers can identify high-risk individuals for timely interventions. Community programs and public health campaigns should promote mental well-being and prevent suicidal behaviors using these findings. Future research should include more predictors, diverse populations, and longitudinal data to better understand causal relationships and timing. Interdisciplinary collaboration and advanced machine learning techniques can enhance predictive accuracy and model interpretability.
Co-Authors Alfian, Andi Daez, Francisco DP. Karim, Rafidah Abd Samosir, Omas Bulan Sarni Maniar Berliana, Sarni Maniar Wahyuni, Krismanti Tri