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Diabetes is a serious chronic disease that occurs because the pancreas does not produce enough insulin (a hormone that regulates blood sugar or glucose), or when the body cannot effectively use the insulin it produces. WHO data shows that the incidence of non-communicable diseases in 2004 reached 48 , 30% is slightly higher than the incidence rate of infectious diseases, namely 47.50% [1]. According to the Ministry of Health in 2012 diabetes caused 1.5 million deaths. Some Indonesian people, this disease is better known as diabetes or blood sugar. This research was developed through secondary data processing from the Pima Indians Diabetes Dataset health database which was taken from the Kaggle dataset and can be accessed through https://www.kaggle.com/uciml/pima-indians-diabetes-database. Where the data itself consists of 768 records with several medical predictor variables (Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Diabetes Pedigree Function, Age and Outcome). Then the data will be processed using the Particle Swarm Optimization (PSO) feature selection to increase the accuracy value and the Naive Bayes algorithm to determine the accuracy results of the diagnosis of diabetes. From the results of research that has been done for the accuracy of the classification algorithm Naive Bayes is 74.61%, while the accuracy of the classification algorithm with Particle Swarm Optimization is 77.34% with an accuracy difference of 2.73%. So it can be concluded that the application of the Particle Swarm Optimization technique is able to select attributes in the Naive Bayes Algorithm, and can produce a better level of diabetes diagnosis accuracy than using only the individual method, namely the Naive Bayes algorithm. Keywords: Diabetes, Particle Swarm Optimization, Naive Bayes Algorithm

A house for every human being is the main and most important need compared to others needs in general. A financial institution is an institution engaged in the financial sector where its customers are people from various walks of life with various behaviors. Lending is a business activity that carries a high risk and affects the business continuity of a banking company. The problem that is often faced in providing home loans is determining the decision to extend credit to prospective customers, while another problem is that not all home loan payments by customers can run well or commonly known as bad credit. One of the causes of bad credit is an assessment error in making credit decisions. Data mining is a process used to analyze cases in order to find the best performance of an algorithm being tested. One way to get information or patterns from a large data set is to use techniques in data mining. There are many classification methods that can be used to produce precise accuracy values. In this study, two classification algotihm methods are used in classifying the home crediting dataset, namely the C4.5 decision tree algorithm and the Naïve Bayes algorithm. The comparison of the two algorithms produces an accuracy value fo the Naïve Bayes algorithm of 36.36% and the Decision Tree C4.5 algorithm has an accuracy rate of 59.54%.

Classification is one of the most widely used techniques in machine learning. Text classification is the process of classifying data according to pre-determined groups or classes. Where in most cases, text classification uses labeled training data to obtain the rules used to classify test data into predefined groups. In this study, it is proposed to use CountVectorizer for Indonesian text classification which will be compared with TF-IDF Term Weighting and its three feature levels, namely Character Level, Word Level and N-gram Level as feature extraction which is implemented together with Naive Bayes classification and the BPPPTIndToEngCorpusHalfM dataset. To compare the classification performance, this study uses 10-Fold Cross Validation and Split Data using a ratio of 90:10, while to evaluate the accuracy of the authors using the F1-Score and AUC with the hope that this study will get good accuracy results so that it can be used as a reference to be developed using another method. The F1-Score accuracy obtained in this study was 0.93 and the AUC score was 0.95.

Chronic kidney failure is the failure of kidney function in maintaining metabolism and fluid and electrolyte balance in the body. Chronic kidney disease initially does not show significant symptoms and signs but can develop rapidly into kidney failure. Kidney disease can be prevented and treated if known earlier. One way to find out chronic kidney failure is to detect using data mining. Iterative Dichotomiser 3 (ID3) algorithm is one of the classification methods and is a type of method that can map or separate two or more different classes. Based on the measurement of performance classification of 80% of training data from 400 data used, it shows that the accuracy value reached 96.25%. It can be concluded that the ID3 Algorithm method is feasible to be used in research predictions for chronic kidney failure.