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Abstract

Toraja carvings are an important part of Indonesia’s cultural heritage, rich in symbolic, aesthetic, and philosophical meaning. However, the identification and preservation of carving motifs still rely on subjective, time-consuming manual processes, limiting scalability and inconsistent knowledge transmission. From a Knowledge Engineering and Cognitive Data Science perspective, this challenge highlights the need for mechanisms that can transform visual cultural artifacts into structured, machine-interpretable knowledge. This study investigates the use of the YOLO11m model as a data-driven approach for modeling cultural knowledge through automated detection of three Toraja carving motifs: pa_tedong, pa_kapu_baka, and pa_manu_londongan using original images collected directly from traditional Tongkonan houses. A total of 222 high-resolution images were manually annotated and preprocessed through resizing to 640×640 pixels and auto-orientation for input standardization, then divided into 70% training, 20% validation, and 10% testing sets. The model was trained on an NVIDIA A100 GPU using the Ultralytics framework in Google Colab and evaluated using precision, recall, F1-score, and mean Average Precision (mAP). Experimental results show that YOLO11m achieved an mAP@0.5 of 96.5%, demonstrating robust performance in capturing complex visual and semantic patterns despite challenges such as motif similarity and data imbalance. Beyond detection accuracy, the findings indicate that deep learning–based object detection can support the systematic documentation, interpretation, and reuse of cultural knowledge, contributing to scalable digital preservation of traditional cultural artifacts. Future work will explore larger datasets, optimized hyperparameters, and advanced detection models to further enhance the robustness of cultural knowledge representations further.

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