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Batik motif classification has attracted growing attention in visual computing due to its role in cultural heritage preservation, textile informatics, museum documentation, and automated cataloging. Although many studies report high classification accuracy, robustness under real-world acquisition conditions remains insufficiently understood. Batik images are frequently affected by illumination variation, blur, folds, watermark overlays, wearable deformation, scale inconsistency, and background clutter, creating challenges that extend beyond conventional image-noise assumptions. Existing studies largely focus on improving classification performance, while the interactions among acquisition variability, feature representation, evaluation practice, and deployment constraints remain fragmented. This systematic literature review addresses this gap by synthesizing batik classification research through a robustness-aware perspective. Using query expansion, backward and forward citation chaining, relevance screening, and thematic coding, 116 candidate records were identified, resulting in 50 highly relevant studies for detailed analysis. The review reveals that robustness is shaped less by denoising alone than by the combined effects of acquisition conditions, representation design, evaluation realism, and deployment context. Handcrafted descriptors remain competitive for small datasets and structured motifs due to their data efficiency and interpretability, whereas deep learning models achieve the highest reported accuracy when supported by sufficient data diversity and realistic augmentation. Hybrid representations emerge as the most consistently balanced approach, combining local texture stability with higher-level abstraction across heterogeneous acquisition settings. The review further identifies recurring robustness failure patterns, including background dependency, illumination instability, motif-scale inconsistency, wearable deformation, and source-shift vulnerability. Based on these findings, a robustness-oriented research agenda is proposed, emphasizing cross-acquisition evaluation, representation-stability analysis, batik-specific robustness benchmarks, acquisition-aware augmentation, and deployable lightweight or hybrid architectures. The study contributes a domain-specific synthesis that reframes batik motif classification from an accuracy-centric task toward a robustness-aware visual recognition problem.

Inclusive education in the context of Islamic religious education reflects the implementation of Islamic values that promote respect for diversity and the protection of individual rights. Islamic Religious Education teachers play a crucial role in ensuring that all students, including those with special needs in inclusive schools, can access and understand Islamic teachings at the Madrasah Tsanawiyah level. Inclusive education also functions as a catalyst for educational transformation by promoting equality and fairness in the learning process without discrimination among students. In this context, teachers are required to adapt teaching strategies, learning materials, and classroom interactions according to the diverse needs and abilities of each learner. Their role is not only to deliver religious knowledge but also to foster an inclusive learning environment that supports participation and understanding for all students. This study contributes to a deeper understanding of the role of Islamic Religious Education teachers within inclusive education settings and provides insights that may support future research and development of inclusive education practices in Indonesia.