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Annastasya Nabila Elsa Wulandari
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Computer Science & IT Control & Systems Engineering Engineering Medicine & Pharmacology Public Health

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Virus Host Prediction with Metagenomic Features using Support Vector Machine Algorithm and Grid Search Cross Validation Optimization Purwono, Purwono; Annastasya Nabila Elsa Wulandari; Novieta Hardeani Sari
Journal of Advanced Health Informatics Research Vol. 2 No. 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i3.298

Abstract

Viruses and bacteria continue to evolve alongside humans. Viruses are spreading too fast and causing a huge loss of life in the world. Viruses play an important role as dangerous pathogens that continue to spread various infectious diseases. Metegenomics is the application of large sequencing technology to genetic material obtained directly from one or more environmental samples, resulting in at least 50Mb random samples and multiple long sequences. It is important to identify the origin of the virus to prevent the spread of outbreaks. Understanding the biology of these viruses and how they affect their ecosystems depends on knowing which host they infect. We can use metagenomic features derived from the viral genome to determine the type of virus host. The activity of predicting virus hosts has traditionally taken a lot of time and effort in the process. Technology can be one of the solutions that can be used to predict virus host types. One of the technologies that can be used is machine learning. We chose one of the machine learning algorithms, SVM, to predict viral hosts with metagenomics features, namely genome size, GC% and number of CDS from viral genomes derived from 7326 viral genomes. The SVM model was further optimised with GS and K-CV methods. This optimisation resulted in an increase in the accuracy value of the model when predicting virus hosts from 80% to 84%.
Potential Use of U-Net and Fuzzy Logic in Diabetic Foot Ulcer Segmentation: A Comprehensive Review Rachman Hidayat; Annastasya Nabila Elsa Wulandari; Purwono, Purwono; Khoirun Nisa
Journal of Advanced Health Informatics Research Vol. 2 No. 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i3.299

Abstract

Diabetic foot ulcer (DFU) image segmentation is still an interesting concern of researchers. Various new deep learning-based methods have been proposed to handle this image segmentation problem. Some research problems that are still faced by many researchers are dataset problems that are considered limited and need further clinical trials. The challenges of data problems include heterogeneity and image quality variations in the shape of skin lesions and subjectivity when annotating. The evaluation results from previous studies also show a considerable difference where there are still low accuracy results, but also too high accuracy is still found so that it is considered to have the potential for overfitting. As a result of the review of various related studies, there is an interesting potential of applying fuzzy logic to the U-Net architecture. This architecture has become very popular because it is widely used in medical image segmentation. The application of fuzzy logic can be applied to the U-Net architecture such as encoder, decoder, skip connection to adjust various U-Net parameters.
Co-Authors KHOIRUN NISA Novieta Hardeani Sari Purwono Purwono, Purwono Rachman Hidayat