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All Journal GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling Indonesian Journal of Applied Technology and Innovation Science
Mahmudah, Arifah Nur
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Social Sciences

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The Impact of Personality Trait on Final-Year Students’Flourishing: A Path-Analysis Approach Mahmudah, Arifah Nur; Darmayanti, Kusumasari Kartika Hima
GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling Vol 13, No 2 (2023)
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/gdn.v13i2.6862

Abstract

An individual’s personality influences flourishing. Therefore, this study purposed to determine the influence of personality traits on the flourishing of final-year students. This data was collected using the PERMA-Profiler and Big Five Inventory. This study was a quantitative study with a non-experimental research design. The sampling technique used purposive sampling on 300 final-year students. The data analysis method was multiple linear regression techniques assisted by the Jamovi 2.3.18. The findings showed that the significant five personality effect flourished by R2 = 32,2%. In addition, it was found that agreeableness was the most influential variable in the flourishing of final-year students, followed by conscientiousness and extraversion. On the other hand, this study confirmed that openness and neuroticism had no significant effect on flourishing. The results contributed to interventions from the university to pay attention to agreeableness, conscientiousness, and extraversion to develop flourishing in final-year students.
Prediction of Fetal Health Using Machine Learning Algorithms Mustika, Dinda; Putri, Rindiani Suhadi; Alhady, M. Naufal Dzaky; Khairunnisa, Kharisma Ummi; Mahmudah, Arifah Nur
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 3 No. 1 (2026): IJATIS February 2026
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v3i1.2496

Abstract

This study evaluates several machine learning algorithms for predicting fetal health conditions using cardiotocography (CTG) data. The dataset contains 2,126 records with 22 numerical features obtained from Kaggle and is classified into three categories: normal, suspect, and pathological. Four classification models Decision Tree, Random Forest, Support Vector Machine, and Logistic Regression were implemented and evaluated using two data split scenarios (80:20 and 70:30). Model performance was assessed using precision, recall, and F1-score. The results show that Random Forest achieves the best performance with an F1-score of 91% in both split scenarios, indicating stable and accurate classification compared with other models. The contribution of this study is to provide a comparative evaluation of classical machine learning algorithms for CTG-based fetal health prediction. The findings can support the development of decision-support tools to help medical personnel detect and monitor fetal health risks early.
Co-Authors Alhady, M. Naufal Dzaky Khairunnisa, Kharisma Ummi Kusumasari Kartika Hima Darmayanti Mustika, Dinda Putri, Rindiani Suhadi