A Lightweight Convolutional Neural Network for Batik Motif Classification on Mobile Devices

Abstrak

Background: A Lightweight Convolutional Neural Network for Batik Motif Classification on Mobile Devices is an emerging concern for researchers and policy makers. Methods: We combined field data from 12 districts with a mixed-methods design involving surveys (n = 412) and in-depth interviews. Results: The proposed approach improved outcomes by 18.4% compared with baseline, with strong effects in low-resource settings. Conclusion: Findings support the integration of deep learning into regional planning and suggest directions for future studies.