Yohannes Primary Author mixed material bibliography Bandung Maranatha University Press 2021 Indonesia

Artikel Jurnal

hlm : 498-507 Abstract White blood cells are cells that makeup blood components that function to fight various diseases from the body (immune system). White blood cells are divided into five types, namely basophils, eosinophils, neutrophils, lymphocytes, and monocytes. Detection of white blood cell types is done in a laboratory which requires more effort and time. One solution that can be done is to use machine learning such as Support Vector Machine (SVM) with Scale Invariant Feature Transform (SIFT) feature extraction. This study uses a dataset of white blood cell images that previously carried out a pre-processing stage consisting of cropping, resizing, and saliency. The saliency method can take a significant part in image data and. The SIFT feature extraction method can provide the location of the keypoint points that SVM can use in studying and recognizing white blood cell objects. The use of region-contrast saliency with kernel radial basis function (RBF) yields the best accuracy, precision, and recall results. Based on the test results obtained in this study, saliency can improve the accuracy, precision, and recall of SVM on the white blood cell image dataset compared to without saliency. Informatika Sistem Informasi JUTISI 24432210 Perpustakaan Teknik UPI YAI JUTISI V7N2 Agustus 2021 JUTISI5a-016 Perpustakaan FT UPI YAI JUTISI V7N2 Agustus 2021 JUTISI5b-016 Perpustakaan FT UPI YAI JUTISI V7N2 Agustus 2021 cover_issue_174_en_US.png.png 59278 2023-02-28 13:38:02 2023-02-28 13:38:02 machine generated