A Diagnostic Framework for Staged AI Adoption in Batik Motif Recognition: Integrating CNN Evidence and Implementation Readiness

Abstract

This study proposes a diagnostic dual-layer decision-support framework for staged artificial intelligence adoption in batik motif recognition. The objective is to examine whether technical evidence from convolutional neural network classification and perceived implementation-side readiness can be jointly interpreted to prevent premature deployment in cultural-heritage recognition. The contribution of the study is not a new classifier architecture, but an operational diagnostic logic that treats model performance, class-level instability, readiness perception, governance, security, and feedback mechanisms as complementary but non-substitutable evidence. Methodologically, the technical layer evaluated three transfer-learning baselines, namely VGGNet-16, ResNet50, and MobileNetV2, using 983 batik images across 20 motif classes. The implementation layer assessed perceived readiness among 173 information technology practitioners using the Technology-Organization-Environment-Human plus Feedback dimensions. The integration layer then mapped technical-readiness evidence and readiness perception into explicit staged-adoption decisions rather than averaging them as interchangeable indicators.  The analysis used performance summaries, readiness profiles, decision matrices, security checklists, learning curves, and confusion-matrix diagnostics to connect empirical observations with staged adoption recommendations. The best-performing baseline was ResNet50, with 45% accuracy and a macro F1-score of 0.40, showing low technical readiness and substantial motif-specific instability. In contrast, the readiness survey indicated high perceived implementation-side readiness, with an average agreement score of 78.7%. This mismatch reveals a readiness asymmetry: implementation support may exist even when the recognition model remains technically immature. The findings imply that batik-recognition systems should prioritize dataset expansion, expert label validation, model refinement, moderated feedback, security governance, and controlled pilot testing before operational deployment. The framework provides a transparent basis for risk-aware, staged adoption decisions in artificial-intelligence-assisted cultural heritage preservation.