The increasing new prospective students in a University to make the stack more and more data, departing from it then conducted a search for new knowledge with data mining. Grouping data for prospective new students will be made by the method Clustering and used the algorithm k-means. In this penmaru there are 5 data attributes are used i.e., hometown, gender, status to qualify for selection, driveways, and majors. This analysis is performed using WEKA software and the source data taken from admissions data (penmaru) in the form of a data warehouse. Class from the use of this method is the attribute of the majors. Iteration performed as many as 3 times and the number of a cluster at the Faculty of medicine and health sciences, i.e. 4 clusters, Faculty of social and political science 3 clusters. Method Clustering can be applied to the classification of data for prospective new students. Another thing that can be analyzed from the results of the grouping candidate data, promotion strategies from each Department to increase the quantity and quality.
The development of education in Indonesia has increased very rapidly. One of the things that have become a benchmark for success in the quality of education at the university is the kind of job getting graduates after graduation. This research aims to identify factors that have an impact on the type of job classification method based on the C 4.5 alumni algorithm. The methodology of this research begins with the study of literature, the identification of a process of data extraction, data selection, data collection, data processing, data testing, and DA conclusion. This research uses some features of the data on a few faculty members, the year of graduation, the annual completion rate, and the strength as a classification performance parameter. Graduates data used up to 259, and consisted of 3 faculties of Economics, medicine and engineering forces from 2001-2013 and graduated from 2011-2016. The research results that have been done is if it comes from the Faculty of Economics, in 2011 and 2012 the majority of work in the private sector has passed, if it comes from the Faculty of Medicine with the years 2011 and 2012 graduated with a cumulative labor rate of between 3 to 3.5 majority working in The private sector, 2012 with a GPA between 3 and 3.5 working in the Private Sector. Finally, the C4.5 algorithm is suitable for the classification of alumni work types.
The selection process among outstanding students in a department has a big problem. This process is not fair because only involve one criteria and ignore the other criteria. We need the best student to participate in a competition held by the Indonesia Security Incident Response Team on Internet Infrastructure (ID SIRTII) of the Ministry of Communication and Information. This process uses Weka software to calculate the best student. It provides the various method to explore the data. One of them is clustering method. There are many algorithms in clustering method. In this research, we will investigate widely about one of that algorithms. Its name is K-Means. This algorithm (K-Means) will give the recommendations about the best student based on the cluster. It will represent the many clusters of a student group. The best cluster can be calculated more to get the names of the best students group. They are eligible to enter the competition. K-means involve the GPA (Grade Point Average) and related course to support the academic skill in order to get the best student. This research helps the teacher select the best student to enter the competition. Many similar cases can use this algorithm in order to get the best student.
Barepan Village is one of the villages in Cawas sub-district, Klaten, Central Java. In 2017 Barepan Village has owned a new building as a means of village administration and obtains assets that support the implementation of government such as computers, tables, chairs, and others. However, asset management is currently not going well and does not have a unique asset database to facilitate the management and tracking of its assets. This caused the difficulty of conducting asset track which made it difficult for asset managers to know the condition of the asset was right, damaged or lost. Therefore, an information system is needed which has the objective to be able to run the business process of asset management to be neat and structured so that asset managers can efficiently manage and track assets. The process of designing an Asset Management Information System using the waterfall model software development method begins with needs analysis, design, coding, testing, and maintenance. From the design stage, it was then created with Code Igniter in the form of a PHP framework with the MVC concept so that a website-based Information System with the MVC concept was produced. Verification and validation are then carried out to determine the suitability of the system design with the Asset Management Information System final results that have been made. Finally, an asset information system can be obtained that fits your needs and archives assets well.
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