Penerapan Faster RCNN + ResNet 50 untuk Mengidentifikasi Spesies dan Stadium Parasit Plasmodium Malaria
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Alifia Revan Prananda
*
Universitas Tidar, Magelang,
Indonesia
- Suamanda Ika Novichasari Universitas Tidar, Magelang, Indonesia
- Bagus Fatkhurrozi Universitas Tidar, Magelang, Indonesia
- Muhammad Nurkholis Abdillah Universitas Tidar, Indonesia
- Eka Legya Frannita Politeknik ATK Yogyakarta, Yogyakarta, Indonesia
- Zharifa Nur Majidah Universitas Tidar, Magelang, Indonesia
- Fadhila Syahida Wibowo Universitas Tidar, Magelang, Indonesia (*) Corresponding Author
Abstract
Malaria is one of the epidemic health diseases and is well-known as a serious infectious disease. The malaria examination process had occurred by analyzing the digital microscopic images using a microscope. Those examination procedures were conducted manually, which lead to some hurdles such as misinterpretation, misdiagnosis and may produce subjective results. This research aims to develop a method for detecting the Plasmodium parasite and identifying the species and stage of Plasmodium parasite. The proposed method was performed into 488 raw data comprising of 538 parasites. The proposed method was started by conducting a data augmentation process for balancing the number of data, training model, testing model, evaluation. In this study, both the training and testing processes were performed by applying Faster RCNN + ResNet-50. The result of the testing process shows that Faster RCNN + ResNet-50 successfully achieved mAP of 0,603. It also achieved accuracy of 93.91%, sensitivity of 66.20%, specificity of 96.10%, PPV of 60.14% and NPV of 97.30%. This result indicates that the proposed method is powerful for detecting Plasmodium parasites and identifying all species and stadiums.
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References
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Copyright (c) 2025 Alifia Revan Prananda, Suamanda Ika Novichasari, Bagus Fatkhurrozi, Muhammad Nurkholis Abdillah, Eka Legya Frannita, Zharifa Nur Majidah, Fadhila Syahida Wibowo
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