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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

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.