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Diabetes Mellitus is a group of chronic metabolic disorders due to abnormal metabolism of carbohydrates, fats, and proteins, characterized by hyperglycemia which is results in long-term microvascular, macrovascular and neuropathic complications. Oral antidiabetic therapy is the main pharmacological therapy to treat type II diabetes mellitus. This therapy can be oral antidiabetic therapy. Thus, to find out the effectiveness of either combination or single of antidiabetics on diabetes mellitus patients, researchers conducted a studies of inpatients at RSUD Madiun. This study included as non-experimental observational study with a cross-sectional study design at RSUD Madiun. Data were collected from Medical Records in October 2018. The sampling method that used in this study was non probability with purposive sampling technique, so there are 58 patients as a total sample. The results showed that the effectiveness of controlling blood sugar levels when using an effective single ADO was Metformin, because Metfomin was able to reduce blood sugar levels with an average length of stay of 6.17 and the effectiveness of controlling blood sugar levels when using an effective combination of ADO was Metformin and acarbosa, because Metfomin and acarbosa are able to reduce blood sugar  average length of stay 7 days inpatient.

This study aims to create an electronic medical record information design system where an electronic medical record system is a system that provides complete information on patient data and medical records during the maintenance and storage of all patient data. The Archive Management System at the Indonusa Surakarta Polytechnic Medical Records Laboratory is not yet computerized. Patient data entry still uses paper, while the recording and storage of medical records is done manually. This study aims to make applications related to electronic medical record information systems at the Indonusa Surakarta Polytechnic Laboratory. In this descriptive study the researcher used a qualitative approach, what will be done to the person in charge of the medical record laboratory. Based on the study results by conducting questionnaires and observations, data relevant to outpatient outpatient recordings is needed, for example medical information record numbers, patient data info, and disease data info, in designing an electronic medical record information system.    

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