Smart Mobile Application for Detecting Balinese Masks to Introduce Balinese Culture to World Tourism

Putu Gede Surya Cipta Nugraha; Putu Satria Udyana Putra; I Putu Agus Eka Darma Udayana; I Putu Dwi Guna Ambara Putra

Authors

Putu Gede Surya Cipta Nugraha Institut Bisnis dan Teknologi Indonesia (INSTIKI)
Putu Satria Udyana Putra Institut Bisnis dan Teknologi Indonesia (INSTIKI)
I Putu Agus Eka Darma Udayana Institut Bisnis dan Teknologi Indonesia (INSTIKI)
I Putu Dwi Guna Ambara Putra Institut Bisnis dan Teknologi Indonesia (INSTIKI)

Keywords:

Mask recognition, Balinese masks, Deep Learning, Cultural Tourism, VGG 16

Abstract

Indonesia, known for its remarkable cultural diversity, encompasses many ethnic groups, each preserving its distinct cultural heritage. Among Indonesia's cultural treasures is the ancient art of traditional mask-making, referred to as "topeng." Bali, in particular, stands out as a hub for topeng artistry, with roots tracing back to prehistoric eras, serving as a profound representation of Bali's rich cultural values. Bali showcases a broad spectrum of traditional dances incorporating masks as props. The use of shows in these performances often piques the curiosity of residents and tourists, prompting questions about the symbolism and cultural context surrounding their use. People recognize the physical appearance of masks but need to gain knowledge of their names and deeper cultural meanings. The inherent similarities among mask forms further confound both locals and foreign visitors in distinguishing between various types of Balinese masks. This research endeavors to tackle this issue by developing an Artificial Intelligence (AI) system that is integrated into a mobile application using the CNN (VGG-16) method. The primary objective is to introduce and promote the captivating Balinese mask culture and artistry, bolstering cultural tourism through cutting-edge mobile technology. The anticipated outcomes include a nationally accredited journal publication, a user-friendly mobile application, and the acquisition of intellectual property rights. This research constitutes a transition from Technology Readiness Level (TRL) 2 to TRL 3, wherein the AI framework will be rigorously validated, incorporating accuracy, precision, recall, and F1-score assessments, all seamlessly integrated within the mobile system.

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