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All Journal Journal of Educational, Health and Community Psychology Journal of Applied Data Sciences Jurnal Psikologi: Indonesian Journal of Psychology
Anugriaty Indah Asmarany
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Public Health Social Sciences

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Bias Gender Sebagai Prediktor Kekerasan Dalam Rumah Tangga Anugriaty Indah Asmarany
Jurnal Psikologi Vol 35, No 1 (2008)
Publisher : Faculty of Psychology, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (349.403 KB) | DOI: 10.22146/jpsi.7096

Abstract

This article presents a meta‐analysis of the survey literature that has examined gender bias as predictors of domestic violence. A meta – analysis of 62 studies from 35 articles, 33 researchers and 53423 subjects evaluated the correlation between gender bias and domestic violence. This analysis extends previous work by directly correcting error of sampling and measurements. Summary analysis provided support for the hypothesis that gender bias has a correlation on domestic violence. The results showed gender bias have been identified as predictors of the domestic violence Keywords: meta‐analysis, domestic violence, gender bias
Academic Engagement in Students: The Role of Lecturers' Social Support Mediated by Academic Self-Efficacy and Growth Mindset Fakhrurrozi, Muhammad; Wahyu Rahardjo; Anugriaty Indah Asmarany; Johanna, Jesicca
Journal of Educational, Health and Community Psychology Vol 14 No 2 June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jehcp.vi.30224

Abstract

Academic engagement is a critical topic within the field of educational psychology, particularly in the context of higher education, due to its far-reaching positive and negative implications. Among the key antecedents believed to shape academic engagement are lecturer social support, academic self-efficacy, and a growth mindset. This study aimed to examine the extent to which academic self-efficacy and a growth mindset mediate the relationship between lecturer social support and academic engagement. The research involved 451 undergraduate students from the Greater Jakarta area, utilizing path analysis as the primary data analysis technique. The findings revealed two main outcomes: (1) lecturer social support has a significant direct effect on academic engagement, and (2) academic self-efficacy and growth mindset do not serve as strong mediators in this relationship. These results highlight the importance of enhancing lecturer support and suggest the need for targeted interventions to strengthen students’ academic self-efficacy and growth mindset within higher education institutions.  
Development of A Deep Learning Model for Mental Health Classification and Early Screening through Draw a Person (DAP) Test Images Nurasiah Nurasiah; Achmad Benny Mutiara; Tristyanti Yusnitasari; Anugriaty Indah Asmarany
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.700

Abstract

Mental health, as defined by the World Health Organization (WHO), is a fundamental aspect of overall well-being. The increasing complexity of modern society, coupled with rising levels of competition and stress, significantly impacts individuals’ mental health. The DAP test is a psychological assessment tool that uses human figure drawings to gain insights into an individual’s personality and mental condition. YOLO (You Only Look Once) is a deep learning algorithm based on Convolutional Neural Networks (CNNs) designed for real-time object detection. This study utilizes a DAP image dataset contributed by adolescents aged 12 to 16 years to develop a model for detecting and classifying objects in DAP images using the YOLOv8 algorithm. Optimal training results were achieved after 150 epochs, yielding a Precision of 0.821, Recall of 0.799, and mAP50 of 0.88. The model evaluation demonstrated an F1-Score of 0.78, indicating a balanced performance between Precision and Recall. Psychological analysis was conducted based on symptoms extracted from the characteristics of DAP images. Mental health conditions were classified according to severity levels consisting of minor, medium, and serious, based on weighted symptomatology derived from DAP image characteristics. The successful development of this model highlights its capability to classify various mental health conditions based on psychological analysis of DAP images. The findings suggest that mental health classification using DAP test images has the potential to support early screening and psychological assessment by providing an innovative and objective approach to identifying psychological indicators.
Co-Authors Achmad Benny Mutiara Johanna, Jesicca Muhammad Fakhrurrozi Nurasiah Nurasiah Tristyanti Yusnitasari Wahyu Rahardjo