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All Journal IJID (International Journal on Informatics for Development) Journal of Advanced in Information and Industrial Technology (JAIIT)
Putra, Iqbal Kurniawan Asmar
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Improving Osteosarcoma Detection through SMOTE-Driven Machine Learning Approaches Muhammad Ainul Fikri; Ajie Kusuma Wardhana; Yudha Riwanto; Inggrid Yanuar Risca Partiwi; Fauzia Sekar Anis Sekar Ningrum; Putra, Iqbal Kurniawan Asmar
IJID (International Journal on Informatics for Development) Vol. 13 No. 2 (2024): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4890

Abstract

Osteosarcoma is an aggressive and highly malignant bone cancer primarily affecting adolescents and young adults, with males being more commonly affected. Although deep learning models such as YOLO (95.73% accuracy) and VGG19 (95.25% accuracy), have demonstrated effectiveness in osteosarcoma detection, their large model sizes and extensive computational requirements limit their feasibility in resource-constrained environments. This study proposes a lightweight AI approach that optimizes osteosarcoma detection while maintaining high diagnostic accuracy, leveraging machine learning models under 5MB, manually or semi-automatically extracted features, and SMOTE for data balancing. Experimental results show that Random Forest, SVM, and XGBoost achieve accuracies of 94.70%, 94.23%, and 94.39%, respectively, closely matching the performance of YOLO and VGG19 while maintaining computational efficiency. Furthermore, the inference time for SVM is under one second (0.97s), demonstrating the speed advantage of lightweight models. These findings highlight the potential of small-size (lightweight) machine learning models to deliver high diagnostic accuracy with minimal computational requirements, providing a scalable and practical solution for early osteosarcoma detection in resource-limited settings. By balancing simplicity, efficiency, and high performance, this study establishes a new benchmark for achieving state-of-the-art results with lightweight models and paving the way for improved healthcare accessibility in underserved regions.
Implementation of the YOLOv8n Model for Automatic Owl Detection in Swiftlet Farming Buildings Putra, Iqbal Kurniawan Asmar; Apriska Prameswari; Fikri, Muhammad Ainul; Suhari, Ahmad Riznandi
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.733

Abstract

Object detection based on digital images is a rapidly developing field in the application of intelligent systems. This study aims to create an automatic owl detection system utilizing the YOLOv8 deep learning model as a pest mitigation measure in the swiftlet farming industry. Owls are known to enter swiftlet houses at night and prey on the birds, causing economic losses. Owl image datasets were obtained from the Roboflow platform and annotated in YOLO format. The model was trained using the YOLOv8-nano architecture with a 640×640 pixel input resolution. The evaluation results showed that the model achieved a mAP@0.5 of 96.82% and mAP@0.5:0.95 of 70.5%, with a precision of 97.2% and a recall of 93.38%. These results indicate that the YOLOv8 model performs well and has the potential to be implemented as an automatic monitoring system in swiftlet farming environments.
Co-Authors Ajie Kusuma Wardhana Apriska Prameswari Fauzia Sekar Anis Sekar Ningrum Fikri, Muhammad Ainul Inggrid Yanuar Risca Partiwi Muhammad Ainul Fikri Suhari, Ahmad Riznandi Yudha Riwanto