Pengaruh Image Engagement pada Aplikasi Parasit Malaria

The mosquito's parasite is a group of single-celled microorganisms in the plasmodium type that can cause malaria by attacking human blood cells. In this study, a parasitic detection application of Plasmodium Falciparum type at thropozoite, scizont and gametocyte stage was designed using Android Studio 2.2.2 and OpenCV 2.4.9 library. Image detection process begins with preprocessing, then feature extraction and classification phase using Haar Cascade Classifier, then the last stage 3 types of boost compared, namely Gentle Adaboost, Discrete Adaboost, and Real Adaboost. Image Enhancement is one of the preliminary processes in preprocessing that aims to clarify certain features or features of the image to be more easily analyzed carefully in the feature selection process. The results of this study prove that image enhancement can be used to improve image quality so that the information available on the image can be seen more clearly.

Analysis of Generalization Ability for Different AdaBoost Variants Based on Classification and Regression Trees

As a machine learning method, AdaBoost is widely applied to data classification and object detection because of its robustness and efficiency. AdaBoost constructs a global and optimal combination of weak classifiers based on a sample reweighting. It is known that this kind of combination improves the classification performance tremendously. As the popularity of AdaBoost increases, many variants have been proposed to improve the performance of AdaBoost. Then, a lot of comparison and review studies for AdaBoost variants have also been published. Some researchers compared different AdaBoost variants by experiments in their own fields, and others reviewed various AdaBoost variants by basically introducing these algorithms. However, there is a lack of mathematical analysis of the generalization abilities for different AdaBoost variants. In this paper, we analyze the generalization abilities of six AdaBoost variants in terms of classification margins. The six compared variants are Real AdaBoost, Gentle AdaBoost, Modest AdaBoost, Parameterized AdaBoost, Margin-pruning Boost, and Penalized AdaBoost. Finally, we use experiments to verify our analyses.