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Competition in the business world is increasingly diverse, encouraging companies to compete in competing markets and consumers. This is what happened to the Takoto UMKM. This study aims to determine the marketing strategies used by UMKM Takoto to increase sales. This type of research is conducted using descriptive qualitative methods in the form of case studies on the Takoto UMKM business. This study aims: (1) to study the marketing strategy (marketing mix) of products at UMKM Takoto. (2) provide alternative marketing suggestions for UMKM Takoto products. The conclusions of this study are: (1) the product is considered good by consumers and already has 5 elements of product strategy such as product attributes, brand, packaging, and labels, but the product does not last long because it uses ingredients that do not contain preservatives; (2) the pricing strategy is oriented towards production costs so that in the future it can be considered for pricing by looking at the prices of competitors' products; (3) the promotion strategy of Takoto's UMKM pastries is still limited because it uses more personal selling rather than intense promotion through advertising; (4) Takoto's UMKM pastry products are marketed directly to consumers and through retailers, however the dominant marketing is direct to consumers so that the bargaining position is stronger because it does not depend on retailers.

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

Entrepreneurial intention in a person can be formed by the external and internal environment. This study examines the attributes entrepreneurial intentionin students : role models, entrepreneurial attitude, subjective norms, and emotional competence. The purpose of this study is to determine a model of entrepreneurial intention among exact and non-exact college students. in Brebes and Tegal, Central Java. Respondents observed were exact and non-exact students at three tertiary institutions, namely Muhadi Setiabudi University (UMUS) Brebes, Polteknik Harapan Bersama (Poltek Harber) Tegal, and Pancasakti University (UPS) Tegal with a total of 301 students using a cluster random sampling. This research uses a quantitative analysis approach with data analysis techniques Structural Equation Model (SEM), a method to test the attributes that make up the entrepreneurial intention model. The results showed that the exact entrepreneurial intention of the student group was formed by the attributes of entrepreneurial attitude, and emotional competence. Meanwhile, the non-exact student group was formed by the attributes of role models and entrepreneurial attitudes. The implication of this research is that universities can form entrepreneurial characters among students through a conducive and dynamic curriculum and academic atmosphere.

Abstract In this study that was used as the object of research in classifying red wine based on the quality influenced by each red wine or red wine based on the content of each type of wine, from each attribute containing the composition in the wine seen which attributes most affect the quality of red wine, so that it will be known ingridents that can improve the quality of the wine, in this study was carried out by the application of Machine learning by comparing three algorithms of mining data that is , Decission Tree, Random Forest and Support Vector Machine (SVM), from the results of research that has been done by comparing the three algorithms, Random Forest produced the best accuracy among other algorithms that have been tested. Random Forest with accuracy results of 0.7468 makes this algorithm best used to classify the quality of red wine. And in the second order Decission Tree with accuracy results of 0.7031, while Support Vector Machine (SVM) get an accuracy result of 0.65. So in the research that has been done to classify the quality of red wine based on its composition Random Forest becomes the best algorithm to use..