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This study aims to examine "Analysis of the influence of dissatisfaction, product attributes, and the need for variation, on the switching of the Asus brand ". This data research used a questionnaire with a scale of 1-5 with a sample of 96 respondents. This sampling technique uses a non probability sampling with technique  purposive sampling . The data obtained were then analyzed with the SPSS . The results of this study partially (1) Dissatisfaction has a positive and significant effect on brand switching based on the value of t count > t table that is 3.575 > 1.662 with a significant 0.001 < 0.05. (2) Product attributes have a positive and significant effect on brand switching based on the value of t count > t table, namely 2.362 > 1.662 with a significant 0.002 < 0.05. (3). The need to look for variations has a positive and significant effect on brand switching based on the t count > t table, namely 6.472 > 1.662 with a significant 0.000 <0.05. (4) Simultaneously, dissatisfaction, product attributes, and the need for variation have a positive and significant effect on brand switching. Based on the calculated F value < F table that is 111.918> 3.945 with a significant 0.000 <0.005. Based on Adjusted R Square that is equal to 0.778. In this case, it means that 77.8% of the variation in the brand switching variable is explained by the variables of dissatisfaction, product attributes, and the need to seek variation. Meanwhile, the remaining 22.2% is explained by other variables outside the model

The purpose of this research is to promote t-shirt products from Sakukata.co, and to increase customer satisfaction with packaging design using the QFD method. This type of research is quantitative research. The population in this research are customers who have purchased Sakukata.co products at least once. The sample of this research was selected using Linear Time Function and obtained 40 respondents. This study used primary data from Sakukata.co and questionnaires, the data were analyzed using the Quality Function Deployment (QFD) method.The packaging attributes at Sakukata.co consist of the design, dimensions, capacity, shape, strength, and durability of the packaging. The Respon Teknis that must be in Sakukata.co is the packaging design is modern, simple, informative, elegant, and identical to Sakukata.co. The dimensions of the packaging are the same size as the product and can accommodate additional items for souvenirs (during the promo). Maximum packing capacity for 2 products. Ergonomic, and easy to open and close. The packaging material is not easily damaged, and can protect the product when it rains. Packaging design for primary, secondary and tertiary packaging.The results of the partial or simultaneous regression test show that the packaging attributes have a significant effect on customer satisfaction..

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..

The aim of the research is to find an empirical evidence concerning the implementation of customer perspective of Balanced Scorecard (BSC) in Assalaam Islamic Modern Boarding School Surakarta (AIMBS Surakarta). It is a case study research. Qualitative research design is conducted using semi-structured interviews with 27 members from different stakeholders. Research findings showed that customer perspective of BSC and attributes in strategy map of BSC have been implemented well in AIMBS Surakarta. Besides, the research found supporting factor of implementation of customer perspective of BSC in AIMBS Surakarta, namely: strong commitment from management to adjust the change. The limitation of the research is that the research findings can not be generalized. For future research, it is suggested that the researcher will add the numbers of research object so that the generalization of the research findings can be achieved. Thus, it will give clearer description about implementation of customers perpective of BSC in Islamic boarding schools

Brand Switching is the time when a customer or a group of customerchange their loyalty from one brand of product to another. Population of thisresearch is SIM Card User in Totalwin Semarang. Sixty eight are chosen assample are fixed as purposive sampling. Data are analyzed using SPSSprogram. Data are collected with questionnaires.From the research we canconclude that validity test and reliability test all questioner are fulfill therequirement. Result and theoretical implication of this research is: thisresearch supports previous researches postulating that promotion, price , andproduct attributes influences to brand switching positively. In managerialterms, this research contributes to decision makers to pay more attention tothe promotion in purpose to improve loyalty customer.

Academic data increases every year in line with the increase of students. Abundant data store is alsoan abundance of information. Data mining technology is a tool for extracting information on largedatabases and has been widely used in many domains. Predicting student performance (study evaluation) isan activity to determine a future state based on existing data. Data in the field of academic research hasbeen done with various methods and algorithms, but the use of algorithm SLIQ (Supervised Learning InQuest) has not been done.SLIQ is an algorithm developed by the IBM's Quest project team in 1996 for mining large datasets.SLIQ algorithm classify and predict the students performance, beginning with the data cleaning, conductedelection training and testing data. By calculating gini index of each attribute and then selecting thesmallest gini index data table is split according to the criteria until find the same class. From the results ofthe calculation process can produce a set of rules that can be used to predict student performance.From the experiment it can be concluded that the algorithm SLIQ with decision tree technique canbe used as an alternative in designing a system datamining applications. Tests conducted system showedthat the constructed model can be used to predict the performance of new students. The resulting accuracyof the model system in fact has a lower score than the accuracy of other applications that are used as acomparison of Tanagra. Advantages of the proposed system is in its design does not need complexcalculations in obtaining the gini index attributes.