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All Journal Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah Indonesian Journal of Data and Science Buletin Sistem Informasi dan Teknologi Islam
Manga, Abdul Rachman
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics

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Real-time Deep Learning Detection of Toraja Carving Motifs using YOLO11m for Cultural Heritage Preservation Herman, Herman; Mufti, Farid Wajdi; Manga, Abdul Rachman; Nasir, Haidawati
Knowledge Engineering and Data Science
Publisher : citeus

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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.
Optimal Strategy for Handling Unbalanced Medical Datasets: Performance Evaluation of K-NN Algorithm Using Sampling Techniques Salim, Yulita; Utami, Aulia Putri; Manga, Abdul Rachman; Azis, Huzain; Admojo, Fadhila Tangguh
Knowledge Engineering and Data Science
Publisher : citeus

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This study addresses the critical role of medical image classification in enhancing healthcare effectiveness and tackling the challenges of imbalanced medical datasets. It focuses on optimizing classification performance by integrating Canny edge detection for segmentation and Hu-moment feature extraction and applying oversampling and undersampling techniques. Five diverse medical datasets were utilized, covering Alzheimer’s and Parkinson’s diseases, COVID-19, brain tumours, and lung cancer. The K-Nearest Neighbors (K-NN) algorithm was implemented to enhance classification accuracy, aiming to develop a more robust framework for medical image analysis. The evaluation, conducted using cross-validation, demonstrated notable improvements in key metrics. Specifically, oversampling significantly enhanced lung cancer detection accuracy, while undersampling contributed to balanced performance gains in the COVID-19 class. Metrics, including accuracy, precision, recall, and F1-score, provided insights into the model’s effectiveness. These findings highlight the positive impact of data balancing techniques on K-NN performance in imbalanced medical image classification. Continued research is essential to refine these techniques and improve medical diagnostics.
Co-Authors Admojo, Fadhila Tangguh Dewi, Nabila Vita Dolly Indra Hasan, Fadlan JUMARLIS, MILA L, Harlinda lilis nurhayati Lokapitasari Belluano, Poetri Lestari Mufti, Farid Wajdi Muh Syawal Munawir, Ali Nasir, Haidawati Ramdan Satra Salim, Yulita Siska Anraeni Syam, Aminurlah Utami, Aulia Putri