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Acute myeloid leukemia (AML) M4, M5, and M7 are subtypes of leukemia derived from myeloid cell derivatives that influences the results of the identification of AMLs, which includes myeloblast, monoblast, and megakaryoblast. Furthermore, they are divided into more specific types, including myeloblasts, promyelocytes, monoblasts, promonocytes, monocytes, and megakaryoblasts, which must be clearly identified in order to further calculate the ratio value in the blood. Therefore, this research aims to classify these cell types using the K-nearest neighbor (KNN) algorithm. Three distance metrics are tested, namely, Euclidean, Chebychev, and Minkowski, and both the weighted and unweighted were tested. The features used as parameters are area, nucleus ratio, circularity, perimeter, mean, and standard deviation, and about 1,450 objects are used as training and testing data. In addition, to ensure that the classification is not overfitting, K-fold cross validation was conducted. The results show that the unweighted Minkowski distance acquired about 240 of 290 objects at K = 19, which is the best. Therefore, the unweighted Minkowski distance is selected for further analysis. The accuracy, recall, and precision values of KNN with unweighted Minkowski distance obtained from fivefold cross validation are 80.552, 44.145, and 42.592%, respectively.

Museum is a place of education and learning in the field of culture and history for all levels of society. As one of the first and largest museums in Lampung, Museum Lampung presents a variety of collections that are conditional on cultural values and are very useful if they can be identified through digital media. Speeded-Up Robust Feature (SURF) and Oriented FAST and Rotated BRIEF (ORB) methods are two examples of feature extraction methods that are relatively robust for object recognition in images by finding key points. Digital media determined by the value of key points in this study are images that are classified as having low intensity with an intensity value of <2250 Lux. This study compares the two methods using the digital object media of museum objects. The image of the museum object is given treatment in terms of rotation, scaling, and cropping to test the durability of the image matching process. In terms of feature matching time, the best time is achieved by SURF with 0.16 seconds in testing of 1/3 image region. Meanwhile, the highest matching percentage was also obtained by SURF method from rotational distortion at an angle of 90 degrees which is 76.79% instead of to 63.79% of the percentage obtained by ORB.

Various types of algorithms have been widely used for image segmentation in digital image processing. Every algorithm has features that make it unique to be applied to specific cases. One of the applications of image segmentation is to detect white blood cells. Certain objects such as blood cells must be able to be well segmented because their existence is very crucial to support the accuracy of disease detection related to haematology or the branch of medical science that studies the morphology of blood and blood-forming tissues. Three image segmentation algorithms were compared through this study: Seed Region Growing, Otsu Thresholding and Active Contour Without Edge. Comparative analysis of the three algorithms was done by counting the number of white blood cell objects that were successfully segmented with the actual number of cells that were counted manually. A total of 30 images of blood smears were taken from people suffering from acute myeloid leukemia M1. The average accuracy values from each algorithm were used to determine which image segmentation algorithm is the most suitable for application in the case of white blood cells segmentation. The results showed that Active Contour Without Edge is the most appropriate among the other algorithms