http://repository.potensi-utama.ac.id/jspui/jspui/handle/123456789/3266 2026-04-27T14:01:32Z http://repository.potensi-utama.ac.id/jspui/jspui/handle/123456789/3352 Title: Combination of Thresholding and Otsu Method in Increasing Results of Identification of Malaria Parasite Type in Thin Blood Smear Image Authors: Rosnelly, Rika Abstract: Separation of objects that is not optimal affects the results of subsequent image calculations and greatly affects the accuracy of the identification results. Various methods are used to separate objects (foreground) and background (background), especially in the parasitic image that is on the image of a smear of red blood cells. However, the thresholding method has not been able to optimally separate objects in the malaria parasite image to identify the type of malaria parasite because determining the pixel values for the threshold is done manually, so the identification process shows results that are less than the maximum accuracy. This research is very important by combining the thresholding method with the otsu method to improve the results of identification of malaria parasites based on digital image processing. Otsu determines the pixel for the threshold automatically using a determinant. To identify using four criteria: area, perimeter, mean intensity, and eccentricity. The results showed that the combination of thresholding - Otsu was superior compared to the performance of the thresholding method. The results of the binary value calculation on the combination of the Otsu thresholding method produce higher accuracy values than the thresholding method. Thus, the combination of the Otsu thresholding method can be used as a proposed segmentation method for the identification of malaria parasite types based on digital image processing. 2020-02-26T00:00:00Z http://repository.potensi-utama.ac.id/jspui/jspui/handle/123456789/3351 Title: Aplication Of AHP Method In Selection Of Food Criteria In Medan City Authors: Rosnelly, Rika Abstract: Many foods are on the market but not all the food can be liked or liked by consumers, it all depends on several things that support and meet the expected target. Consumers have many difficulties in choosing what foods they will consume so that in choosing the food is only based on one criteria only and not some criteria. To overcome these problems it is necessary a method that can be used in decision making. The method used in Decision Support System for this research problem is Analityc Hierarchy Process method where in this method is a method of decision making by making pairwise comparisons between choice criteria and pairwise comparisons between the options available so as to get a ranking where the types of food are very to get rank where type of food which is very much in demand. From the results of this study, Durian get the highest weight of 20.98%, then Fried Rice 19.99%, Noodles 19.90%, Soto 19.56% and Meatballs 19.45%. 2019-01-01T00:00:00Z http://repository.potensi-utama.ac.id/jspui/jspui/handle/123456789/3342 Title: Pengembangan Sistem ldetifrkasi Penyakit Malaria Berdasarkan Pengolahan Citra Digital Authors: Rosnelly, Rika 2019-10-04T00:00:00Z http://repository.potensi-utama.ac.id/jspui/jspui/handle/123456789/3341 Title: A Comparison of Mamdani and Sugeno method for Optimization Prediction of Traffic Noise Levels Authors: Rosnelly, Rika Abstract: The traffic noise is a disruption that leads to noise pollution if the noise is very loud. The traffic noise is caused by vehicle exhaust noise, engine and passenger of the transportation. The problem is the traffic noise has disturbed the comfort and health of living beings which are around the neighborhood. It is important that the strategy for controlling the noise of the traffic on the highway, so the noise pollution can be minimized. One of them is to control over the noise path. To predict the noise level of traffic on the highway, so in this study, researchers tested the approach using fuzzy inference systems that compare models mamdani and Sugeno in calculating the level of traffic noise based on the number of vehicles, the correction factor and the width of the road. From the data examined in this study, showed a percentage error of 1.77% for Fuzzy Mamdani models and 5.68% for the Model Sugeno, Fuzzy Mamdani models considered more accurate and effective than Sugeno models in predicting the traffic noise levels. 2018-10-04T00:00:00Z