Javanese Philosophy-Based Islamic Religious Education: Synergy of Tepa Selira and Cablaka Values in Developing Students’ Mental Health
Bibliographic record
Abstract
This article explores the synergy between Islamic religious education and Javanese philosophy, specifically the values of tepa selira (empathy and tolerance) and cablaka (honesty and openness), as a strategy to foster the mental health of Generation Z students. Amid the rapid wave of digitalization and social pressures affecting today’s youth, mental health has become a critical issue that can no longer be overlooked. Generation Z, living in an instant and highly competitive environment, is vulnerable to stress, anxiety, and identity crises. Therefore, an educational approach is needed one that not only emphasizes cognitive religious aspects but also considers emotional and social balance. This study uses a qualitative approach through a literature review and critical analysis of sources related to Islamic education, educational psychology, and Javanese culture. The findings show that tepa selira cultivates students’ empathy, social awareness, and emotional regulation skills. Meanwhile, cablaka encourages the courage to express oneself honestly and supports psychological well-being. These two values align with Islamic teachings on noble character and inner balance, making them highly relevant for integration into the Islamic Religious Education curriculum. This integration allows students to develop mental health not only through spiritual rituals but also through cultural values. Javanese-philosophy-based Islamic religious education has the potential to become a contextual, humanistic educational model that addresses the essential aspects of youth development in the digital era.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".