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All Journal Al-Qalam Penamas International Journal of Educational Narratives
Febri Kurnia Manoppo
Hoseo University
Religion Education Social Sciences

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Hierarchy without Domination: Dalihan Natolu as a Relational Model of Justice and Religious Moderation among the Batak Febri Kurnia Manoppo; Jenri Ambarita
Penamas Vol 39 No 1 (2026): Volume 39, Issue 1, January-June 2026
Publisher : Balai Penelitian dan Pengembangan Agama Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31330/penamas.v39i1.1082

Abstract

This study examines the paradox of ‘dalihan natolu’, the Batak kinship system: how a hierarchy based on inherited roles produces egalitarian relationships whilst simultaneously underpinning religious moderation. This paradox is difficult to explain through John Rawls’s liberal theory of justice, which is grounded in individualism and the equality of basic freedoms, and thus regards ascriptive hierarchies as the antithesis of justice. Building on Rawls, this study interprets the dalihan natolu through the paradigm of relational justice and the theory of reciprocal exchange, asking: through what mechanisms and within what limits can different roles produce fairness and equality? Data were collected qualitatively through in-depth interviews, participatory observation and audiovisual content analysis. The findings reveal asymmetrical equality: rotating reciprocal obligations counterbalance an advantage in one relationship with a subordinate position in another, preventing hierarchies from solidifying into permanent domination. This mechanism transcends religious divides, whilst leaving gender tensions and the burden of customary obligations unresolved. Dalihan natolu challenges the liberal assumption that ascriptive roles preclude justice, whilst simultaneously promoting inter-religious, kinship-based religious moderation.
ART IN THE RUMAGHES UMBANUA OF LAIKIT VILLAGE: NEGOTIATING CHRIST TRANSFORMING CULTURE AND COLLABORATIVE PLURALISM Jekson Berdame; Alrik Lapian; Febri Kurnia Manoppo; Tri Oktavia Hartati Silaban
Al-Qalam Vol. 32 No. 1 (2026): Jurnal Al Qalam
Publisher : Balai Penelitian dan Pengembangan Agama Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31969/alq.v32i1.1755

Abstract

This study examines the cultural transformation of Rumaghes Umbanua in Laikit Village, North Minahasa,as a contemporary reconfiguration of the traditional Dumia Umbanua ritual into a public celebration ofgratitude shaped by interreligious participation and artistic performance. While previous studies haveexplored Minahasan thanksgiving traditions from pastoral, culinary, and ritual perspectives, limitedattention has been given to the role of art as a theological and social medium in pluralistic contexts. Thisresearch addresses that gap by analyzing how artistic expressions function as agents of contextual theologyand collaborative pluralism. Using a qualitative descriptive–interpretive approach, data were collectedthrough participatory observation, in-depth interviews with traditional leaders, religious leaders, artists,and community members, as well as document analysis of cultural and historical archives. The findingsindicate that artistic practices including Kabasaran dance, bamboo and kolintang music, interfaith choirperformances, and traditional attire serve not merely as aesthetic components but as performative spacesin which theological meanings, cultural identities, and interreligious relations are actively negotiated.Through the lens of Richard Niebuhr’s Christ Transforming Culture, the study suggests that RumaghesUmbanua represents a dynamic model of contextual theology in which Christian values transform culturalsymbols without entirely erasing their historical significance. At the same time, the findings highlight theambivalent nature of collaborative pluralism, in which inclusion is shaped by implicit power relations andnegotiated boundaries of representation. This study contributes to the discourse on contextual theology bypositioning art as an active theological mediator and a public instrument for social cohesion, identity reconstruction, and intercultural dialogue in multireligious societies
Narrating Minimal Data: Rethinking Cohort-Based GPA Prediction in Low-Resource Higher Education Contexts Berdinata Massang; Rolty Glendy Wowiling; Allin Junikhah; Firmanians Romula Tuerah; Andrew Nathanael Ratag; Febri Kurnia Manoppo
International Journal of Educational Narratives Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijen.v4i1.3506

Abstract

Background. Student performance prediction has become a major topic in educational data mining and learning analytics. However, most previous studies rely on high-dimensional datasets such as attendance records, course-level grades, and learning management system logs, which are often unavailable in institutions with limited digital infrastructure. Purpose. This study aims to evaluate the feasibility of predicting student academic performance using minimal institutional data and to establish a practical baseline for machine learning implementation in low-resource higher education contexts. Rather than maximizing predictive accuracy, this research examines the lower boundary of predictive capability when only simple academic variables are available. Method. A quantitative descriptive–predictive design was applied to 355 student records from the Christian Religious Education Study Program at IAKN Manado, Indonesia. GPA values were categorized into four classes (Poor, Fair, Good, and Very Good). The dataset was split into 75% training and 25% testing subsets, and class imbalance was addressed using SMOTE. Four models were evaluated: Dummy Classifier, Decision Tree, Random Forest, and Neural Network (MLP). Performance was assessed using accuracy and 5-fold cross-validation. Results. The Dummy Classifier achieved an accuracy of 15.73%, establishing a realistic baseline under balanced class conditions. Decision Tree and Random Forest produced the highest accuracy at 46.06%, while the Neural Network achieved 40.44%. However, cross-validation results remained lower, indicating limited generalization and possible overfitting under minimal-feature conditions. Conclusion. This study shows that simple institutional data can still provide non-trivial predictive signals, but predictive performance remains moderate. The main contribution of this study lies in positioning minimal-data prediction as a baseline methodological framework for institutions with constrained academic datasets, rather than as a high-accuracy predictive solution.
Co-Authors Allin Junikhah Alrik Lapian Andrew Nathanael Ratag Firmanians Romula Tuerah Jekson Berdame Jenri Ambarita Massang, Berdinata Rolty Glendy Wowiling Tri Oktavia Hartati Silaban