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One of the diseases that is generally characterized by symptoms of an increase in glucose levels in the blood and is one of the body diseases classified as chronic is diabetes. Diabetes suffered by a person from time to time can cause serious damage to other organs such as blood vessels, kidneys, heart and nerves. Machine learning provides various data mining algorithms that can be used to assist medical experts. The accuracy of machine learning algorithms is a measure of the effectiveness of decision support systems. Prediction of diabetes can be seen from the patient's medical record data, therefore the author wants to create a diabetes prediction system independently through a website-based application system. This application system will be combined with data observation, namely the science of data mining using the XGBoost algorithm. The dataset is divided into training data by 80% and testing data by 20%. Before the data modeling was carried out, we carried out various parameter setting scenarios with the hope of evaluating and evaluating the implementation to be applied, the parameters we adjusted were colsample_bytree, gamma, learning_rate, max_depth, n_estimators, reg_alpha, reg_lambda, and subsample. After sharing the data and tuning parameters, the resulting model by applying the XGBoost algorithm has an accuracy of 74.67%, the resulting precision value is 57.40%, the resulting recall value is 65.94%, the resulting specificity value is 78, 50%.

Recently, the research study about fish identification become a very challenging to researchers. Climate and environmental changes have a major impact on fish species and their environment. To identify fish using manual process is time consuming and need effort to gather samples in different environment. The identification of fish species is performed by using feature extraction and a series of features. Generally, the characteristic is divided into two groups namely general characteristics and anatomical features. General characteristics is characteristic that can be seen directly without the aid of tools. The characteristics include color, texture, and fiber direction. Although, manual is performed by expert but is possible that identification is not accurate. Therefore, to overcome the problem, we create a web-based application for identifying fish by using image as input. We use 10 class data with 300 images for each class. Then, we split into training and testing with 80:20 ratio. The application was developed by using the MobileNets- V1 model. The proposed method has accuracy on 89 %, that obtain from training score is 91.04%, validation is 88,96%. This score is higher than other methods that used in this application. Total time for computation process is about 127 minutes.