Malaria is a tropical disease that infects human red blood cells caused by infection with the plasmodium parasite. Plasmodium parasites spread to humans through female Anopheles mosquitoes and can reproduce in human blood cells. Malaria is a health problem that is at risk of causing other health problems such as anemia and even death. The current gold standard for malaria diagnosis is laboratory diagnosis by microscopic examination to find the malaria parasite through the blood cells of the patient. However, the diagnosis of malaria through microscopic observation of blood cells has the potential to take a long time, because the plasmodium parasite has a very small size. The malaria detection system using the Convolutional Neural Network (CNN) method is designed to detect malaria in human blood cells. CNN is a machine learning method designed to classify objects in an image. The system was built in three stages of development, namely the development of a CNN model for malaria detection, software development and hardware development. The hardware components used in the system include Raspberry pi, Raspberry Pi camera module, and LCD. The results of the malaria detection test using the CNN model gave an accuracy of 98.76% which was tested on blood cell images from a microscope

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Human development progress in Indonesia is characterized by the increasing score of Human Development Index (HDI). HDI is an important indicator in measuring efforts to build the quality and equity of human life. HDI consists of four variables including life expectancy at birth, school continuity, average of school continuity and expenditure per capita. In this study, we classify districts or cities on the island of Sumatra based on HDI into three categories; high, middle, and low area. We use cluster analysis for the research. Cluster analysis is a class of multivariate techniques that are used to classify objects or cases into relative groups called clusters. One of the cluster analysis methods is k-means. The result of this research divided into three Cluster. The first cluster or the middle area contained 41 cities. The second cluster or the high area contained 21 regencies/cities. The third cluster or the low area contained 92 regencies /cities. Areas with low scores are of more concern because all indicators are below the average value, these areas are like Pidie, Nias Utara, Pesisir Barat and etc.