Skip to content

Edukasi Anti-Bullying melalui Program "Detektif Teman Baik" untuk Membangun Pertemanan Positif Siswa SDN 006 Sambutan

Sep 2026 · Karunia  Jurnal Hasil Pengabdian Masyarakat Indonesia · Vol 5, pp. 194-204
Student Stress and Coping

Abstract

Peer conflict and bullying behavior, manifested in verbal mocking and physical aggression, present significant challenges in elementary education environments, including at SDN 006 Sambutan. This study aimed to examine the implementation and effectiveness of the "Detektif Teman Baik" anti-bullying education program in improving students' understanding of bullying and fostering positive peer relationships. The intervention employed an interactive experiential learning method combining lecture-based education, dynamic Q&A discussions, the educational game "Boleh atau Tidak Boleh," social skills training, and a creative self-reflective medium called "Kotak Cerita Teman Baik". The findings demonstrated a notable increase in students' cognitive comprehension regarding the forms and consequences of physical, verbal, and cyberbullying, as well as an increased ability to distinguish harmful actions from positive peer habits. Furthermore, post-intervention observations showed an emerging behavioral shift, particularly among previous perpetrators who began to reduce teasing, communicate more respectfully, and appreciate interpersonal boundaries. The practical implication of this program suggests that contextual and participatory anti-bullying education serves as an impactful initial catalyst for creating inclusive, safe, and supportive school climates, though sustained teacher monitoring and continuous school reinforcement remain essential to ensure long-term behavioral consistency.

View source

Similar papers

#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54
#machine learning Review Open access Jun 2014

Why Early-Stage Software Startups Fail: A Behavioral Framework

This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.

Carmine Giardino, Xiaofeng Wang, P. Abrahamsson · 175 citations · ⚡19
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#machine learning Review Open access May 2016

Key Challenges in Software Startups Across Life Cycle Stages

It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.

Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al. · 62 citations · ⚡6

Related blog posts

MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.