COVID-19 is an infectious disease caused by a family of coronaviruses, namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The fastest method to identify the presence of this virus is a rapid antibody or antigen test, but to confirm the positive status of a COVID-19 patient, further examination is recommended. Lung examination using chest radiography images taken through X-rays of COVID-19 patients can be one of the method to confirm the patient's condition before/after the rapid test. In this paper, a model to detect COVID-19 through chest radiography images is proposed by using a combination of Discrete Wavelet Transform (DWT) and Moment Invariant features, and the Artificial Neural Network (ANN) classifiers. In this case, the haar wavelet transform and seven Hu moments were used to extracting the image's features. The main aim of the work is to find the best features and ANN model for predicting chest radiography images as COVID-19 suspect, pneumonia, or normal. The k-fold crossvalidation test on the best parameters obtained accuracy up to 86.32%, a precision level of 86.35%, and a recall rate of 86.26%.
COVID-19 is an infectious disease caused by thecoronavirus family, namely severe acute respiratorysyndrome coronavirus 2 (SARS-CoV-2). The fastest methodto identify the presence of this virus is a rapid antibody or antigen test, but confirming the positive status of a COVID-19 patient requires further examination. Lung examination using chest X-ray images taken through X-rays of COVID-19patients can be one way to confirm the patient's conditionbefore/after the rapid test. This paper proposes a featureextraction model to detect COVID-19 through chestradiography using a combination of Discrete WaveletTransform (DWT) and Moment Invariant features. In thiscase, haar wavelet transform and seven Hu moments wereused to extract image features in order to find unique featuresthat represent chest radiographic images as suspectedCOVID-19, pneumonia, or normal. To find out theuniqueness of the proposed features, it is coupled with thekNN and generic ANN classification techniques. Based on theperformance parameters assessed, it turns out that thewavelet-based and moment invariant thorax radiographicimage feature model can be used as a unique featureassociated with three categories: Normal, Pneumonia, andCovid-19. This is indicated by the accuracy value of 82.7% inthe kNN classification technique and the accuracy, precision,and recall of 86%, 87%, and 86% respectively with the ANNclassification technique.
Coronavirus 2 (SARS-CoV-2) is the cause of an acute respiratory infectious disease that can cause death, popularly known as Covid-19. Several methods have been used to detect COVID-19-positive patients, such as rapid antigen and PCR. Another method as an alternative to confirming a positive patient for COVID-19 is through a lung examination using a chest X-ray image. Our previous research used the ANN method to distinguish COVID-19 suspect, pneumonia, or expected by using a Haar filter on Discrete Wavelet Transform (DWT) combined with seven Hu Moment Invariants. This work adopted the ANN method's feature sets for the Support Vector Machine (SVM), which aim to find the best SVM model appropriate for DWT and Hu moment-based features. Both approaches demonstrate promising results, but the SVM approach has slightly better results. The SVM's performances improve accuracy to 87.84% compared to the ANN approach with 86% accuracy.
Perkembangan teknologi pada saat ini terjadi sangat pesat dan digunakan oleh banyak bidang seperti perkantoran,pendidikan, kesehatan, instansi pemerintahan dan lain-lain. Dalam perkembangannya, komputer digunakan untukmengolah, menyimpan dan mencari data dengan cepat dan efisien untuk membantu pekerjaan manusia hingga dapatmengurangi terjadinya human error dan informasi yang dihasilkan akan lebih tepat sehingga meningkatkan kualitaskerja suatu perusahaan maupun instansi pemerintahan. Dalam pengelolaan aset di Badan Pengelolaan Keuangan danAset Daerah (BPKAD) Provinsi Nusa Tenggara Barat sendiri, proses pelaporan pengelolaan, dan monitoring stokbarang dari tahun ke tahun masih dilakukan secara manual dan kurang efektif. Dengan adanya permasalahan tersebut,maka dibuatlah “Sistem Informasi Manajemen Aset pada Badan Pengelolaan Keuangan dan Aset Daerah Provinsi NusaTenggara Barat” berbasis website menggunakan framework Laravel untuk membantu pegawai BPKAD Provinsi NTBmenjalankan pekerjaannya dengan lebih efisien. Pengujian dilakukan pada pihak BPKAD Provinsi NTB dengan metodeMean Opinion Score (MOS) dengan hasil bahwa 86,67% responden setuju bahwa sistem informasi yang dibuat dapatmembantu pegawai dan telah berjalan dengan baik.
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