Parasite detection is important for the diagnosis of many blood-borne diseases including malaria. As part of a program to develop a fast, accurate, and affordable automatic device for diagnosing malaria, a critical step is to automatically classify individual red blood cells in thin blood smear images. To automatically recognize malaria parasites in an image, this paper presents a red blood cell classification study for malaria diagnosis. To diagnose malaria, the threshold-based segmentation is implemented using the Otsu’s method succeeded by the distance transform and statistical classifier. The methods are applied to red blood cell images obtained from Kaggle. These experimental results show that the classification recognizes malaria parasite with 94.60% accuracy, 96.20% specificity, and 93% sensitivity.

Smart Micro Grid in household areas aims to meet electricity needs through the integration between state power plant with renewable energy sources so that the electricity used does not depend entirely on state utility. Smart Micro Grid also enables the availability of energy management services supported by Machine Learning (ML) technology, Big Data, Artificial Intelligence (AI), Internet of Things (IoT) and smart sensors so that consumer use of electricity is more efficient. To improve energy management services and distribution of renewable energy sources, new innovations in ML technology are needed to produce accurate learning models that can be used in the energy analysis process, such as monitoring, prediction, forecasting, scheduling and decision-making. However, the complexity of the problems in the smart grid system, which includes uncertainty and non-linearity, affects the more complex the energy data structure generated. Therefore, the simple ML method will not be able to perform the Learning process because it is limited to simple raw data processing. Therefore, the Deep Learning (DL) method can be used as a Learning method on data that has a complex and large structure. In this paper, Deep Neural Network (DNN) method will be developed using Long Short-Term Memory (LSTM) as a Learning model to provide Future Accurate Prediction (FAP) on electricity use and on renewable energy plants. Prediction test using Confusion Matrix accuracy value and RMSE error value

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