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All Journal Psychiatry Nursing Journal (Jurnal Keperawatan Jiwa) Blockchain Frontier Technology (BFRONT)
Heni Nurhaeni
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Computer Science & IT Control & Systems Engineering Education Health Professions Library & Information Science Nursing Public Health

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Enhancing Personalized Learning Using Artificial Intelligence and Machine Learning Approaches Shaumiwaty, Shaumiwaty; Mochamad Heru Riza Chakim; Heni Nurhaeni; Victorianda
Blockchain Frontier Technology Vol. 4 No. 2 (2025): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v4i2.715

Abstract

The convergence of artificial intelligence (AI) and machine learning (ML) technologies has revolutionized the education landscape, shifting paradigms toward individualized and optimized learning environments. By harnessing AI predictive power and ML adaptive capabilities, educational outcomes are enhanced while equipping teachers with data driven insights for informed decision making. The primary objective of this research is to explore how customized learning environments, ML models, performance measurement, and AI algorithms improve educational outcomes and learning experiences. Despite the rapid advancements in AI driven education, a gap exists in the integration of AI powered personalization with statistical validation techniques like SmartPLS, particularly in evaluating its direct impact on student engagement and performance. The novelty of this study lies in its emphasis on AI driven customization in learning, utilizing advanced statistical validation techniques to provide empirical support for personalized education models. The method involves a survey based approach combined with SmartPLS statistical modeling to analyze correlations between AI driven learning adaptations and educational outcomes. The findings from the result and discussion indicate a positive impact of AI algorithms and ML models on academic success, individualized learning, and improved performance measures, with most hypotheses yielding significant results. These insights align with emerging trends in personalized and adaptable learning and technological advancements, such as immersive experiences and the integration of virtual reality. By addressing the research gap and validating AI driven learning models through SmartPLS, this study contributes to the growing body of knowledge in AI enhanced education, demonstrating the effectiveness of intelligent, data-driven learning environments in fostering better academic performance and engagement.
Free Drawing Group Activity Therapy: A Strategy to Reduce Loneliness in Elderly Nursing Home Residents Heni Nurhaeni; Mawar Meilita; Widagdo, Wahyu
Psychiatry Nursing Journal (Jurnal Keperawatan Jiwa) Vol. 7 No. 2 (2025): September 2025
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/pnj.v7i2.75481

Abstract

Introduction: Elderly individuals are particularly vulnerable to psychosocial issues, one of the most common being loneliness. If left unaddressed, loneliness can lead to a reduced quality of life and an increased risk of mental health disorders, including depression and even suicidal behavior. One effective non-pharmacological intervention to reduce loneliness is group activity therapy, such as free drawing. This study aimed to identify the characteristics of the elderly, assess the level of loneliness they experience, and analyze the effect of free drawing activity therapy on reducing loneliness among elderly residents in the Nursing Home. Method: A quasi-experimental design was employed, utilizing a two-group pretest-posttest approach. A total of 60 elderly participants were selected through purposive sampling and divided evenly into intervention and control groups. The intervention group received free drawing activity therapy sessions from March to April 2025 at PSTW Budi Mulia 3. Results: Data were analyzed using paired t-tests and independent t-tests. The findings showed a significant reduction in the mean loneliness score in the intervention group, from 48.57 to 41.87 (p = 0.001), while the control group experienced a slight increase from 50.40 to 50.70 (p = 0.445). Furthermore, there was a statistically significant difference in post-intervention loneliness scores between the two groups (p = 0.001). Conclusions: In conclusion, free drawing activity therapy significantly reduced loneliness among elderly residents in nursing homes and may serve as a practical non-pharmacological strategy to enhance their psychosocial well-being.
Co-Authors Mawar Meilita Mochamad Heru Riza Chakim Shaumiwaty, Shaumiwaty Victorianda Wahyu Widagdo