Kharisma Kharisma, Setyo Yanuartuti, Indar Sabri

Pembelajaran Deep Learning Berbasis Android di Sanggar Tari Tari Army Dance Production Surabaya

Introduction

Pembelajaran deep learning berbasis android di sanggar tari tari army dance production surabaya. Jelajahi pembelajaran deep learning Android di sanggar tari Surabaya. Tingkatkan kualitas belajar, keterlibatan siswa, dan koreksi teknik melalui dokumentasi video & refleksi diri.

Abstract

This study examines the implementation of Android-based deep learning in dance education at Army Dance Production (ADP) Studio in Surabaya, Indonesia, and investigates the extent to which this technological approach can enhance the quality of learning and student engagement. The primary focus lies in leveraging Android devices as tools for recording, documenting, and replaying dance movements, thereby serving as a medium for evaluation and self-directed reflection among dancers. Through systematic visual documentation, trainees are able to observe movement deficiencies, refine techniques, and improve both expression and ensemble cohesion, either independently or under instructor guidance. This study employs a descriptive qualitative research design. Data were collected through structured observation, in-depth interviews, and documentation involving the studio owner, instructors, and visual records of the learning process. Findings reveal that the integration of Android devices as a documentation medium yields a significantly positive impact on practice quality and student engagement. Students, including those with special needs, demonstrated accelerated movement memorization and improved technique correction through video-based self-evaluation. Furthermore, the systematic uploading of practice sessions to social media platforms such as YouTube, TikTok, and Instagram proved effective as both a documentation archive and a motivational tool. In conclusion, technology-supported dance learning can serve as a foundational step toward broader digital integration in the performing arts, offering a scalable, accessible, and pedagogically sound model for nonformal arts education institutions.

Review

This study presents a compelling qualitative investigation into the integration of Android-based technology to enhance dance education at the Army Dance Production (ADP) Studio in Surabaya. The paper effectively outlines how mobile devices can serve as accessible and effective tools for visual documentation, self-evaluation, and refined instruction in a non-formal arts setting. The descriptive qualitative methodology, employing observations, interviews, and documentation, provides a robust framework for understanding the practical implementation and immediate benefits of this approach. A key strength lies in its focus on a real-world application, demonstrating how readily available technology can be repurposed to address specific pedagogical needs in the performing arts. The findings significantly highlight the positive impact of this technological integration on practice quality and student engagement. The abstract details accelerated movement memorization, improved technique correction through video-based self-evaluation, and notable benefits for students with special needs, underscoring the inclusivity potential of such tools. The strategic use of social media platforms for archival purposes and motivation further illustrates a comprehensive understanding of contemporary digital habits. The study successfully positions Android devices as facilitators of a more reflective and self-directed learning process, which in turn fosters deeper understanding and skill acquisition among dancers. This approach is commendably presented as a foundational step toward broader digital integration in the performing arts, offering a scalable and pedagogically sound model. While the study offers valuable insights, some areas could benefit from further clarification or exploration. The term "Deep Learning" in the title, if intended to refer to artificial intelligence and machine learning algorithms, is not explicitly supported by the methodology described in the abstract, which focuses on human observation and self-reflection facilitated by video recording. Clarifying whether the term refers to the *pedagogical depth* of learning or specific AI/ML technologies would enhance precision. Future research could explore quantitative measures to substantiate the observed improvements (e.g., pre/post-assessment scores, comparative studies with non-tech-integrated groups). Additionally, the abstract could briefly touch upon potential challenges encountered during implementation, such as technical issues, instructor training needs, or student initial resistance, to provide a more balanced perspective. Expanding on the generalizability of these findings to other dance forms or arts disciplines, and discussing the long-term sustainability and cost-effectiveness of this model, would further strengthen the paper's contribution.

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