Background: The existing literature on sustainable software and information systems is fragmented, with research often siloed into specific models, applications, or evaluation metrics without a cohesive overview. This fragmentation hinders the development of a unified understanding necessary for researchers, practitioners, and policymakers to effectively implement sustainability principles.

Aims: This study aims to systematically analyze and synthesize research on software and information systems for sustainability. Its scope is to identify the dominant models, primary application domains, and key evaluation metrics used in the field to establish a consolidated understanding and guide future efforts.

Methods: A Systematic Literature Review (SLR) following PRISMA screened 314 Scopus documents (2017–2026) to 25 articles, analyzed using Biblioshiny, VOSviewer, and thematic synthesis.

Result: The analysis reveals a field in a consolidative phase, dominated by systematic review-based research (76%) focused on theoretical synthesis. While geographically diverse, the research centers on "sustainability" and "information systems" as core themes. A critical gap exists between conceptual frameworks and practical application, evidenced by a scarcity of empirical studies (only 4% quantitative) and the absence of standardized evaluation metrics.

Conclusion: This review concludes that while significant progress has been made in mapping the conceptual landscape, the field of software and information systems for sustainability must now prioritize empirical validation, the development of AI-driven systems, and the establishment of uniform measurement standards to bridge the gap between theoretical promise and tangible real-world outcomes.

Background: Despite its high aesthetic value, automatic classification of Parang Surakarta batik is difficult due to the extreme textural similarities between sub-motifs. Standard CNN architectures, including MobileNetV2often fail to detect the subtle textural details that distinguish each variation of the motif.

Aims: This study develops a hybrid classification model that combines manual and automated spatial texture features to improve identification accuracy on motifs with high visual similarity.

Methods: Using a dataset that has been expanded to 120 original images (40 per class) which is then augmented to a total of 1,200 images to ensure stronger model generalization. This methodology hybrid GLCM-MobileNetV2architecture through transfer learning techniques. Features from both methods are combined through feature fusion before being classified using a Dense layer.

Result: The hybrid GLCM-MobileNetV2model achieved an accuracy of 99%. This performance outperformed the pure MobileNetV2 method (66.67%) and GLCM-SVM (85%), demonstrating that texture features provide significant discriminatory power against similar repetitive patterns.

Conclusion: The integration of GLCM and MobileNetV2 is highly effective for classifying visually similar batik motifs, achieving a superior accuracy of 99% compared to the pure MobileNetV2 (66.67%). This hybrid approach provides a robust and efficient solution for digital cultural preservation on mobile devices.