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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

The advancement of information technology especially in computerized systems is snowballing. It is also backed by a range of demands from industry/ companies that want to abandon the conventional way of conducting business operations to the use of information technology. MÈMORE is a company engaged in the decoration and crafts industry. The company is now using a computerized system in the form of Microsoft Excel software to monitor the company's income and expense and the company often maintains a separate report in a special book. However, this causes problems for these businesses as there is no more efficient method for data processing or for handling the income and expense. In order to solve the problems of the company, a computerized system of financial management is required that will be tailored to the problems and needs of the owner of the business in order to support the company's growth with reliable, managed, and safe financial data. The system can also differentiate its use between the owner and the employee. It is useful for ensuring the confidentiality and protection of company revenue and expenditure data. 

One of the survey activities carried out at the Baitul Giradh Baburrayyan Cooperative was regarding the coffee production of members who are members of the Baitul Oiradh Baburrayyan Cooperative organization, which aims to speed up the data processing process and make it easier to produce reports on production. To carry out these activities quickly and precisely, it is necessary to make an application as a tool in the data processing and presentation of data that will be needed by data users. This data processing application is made using the gambas programming language.