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.
Baja karbon adalah logam yang paling banyak digunakan pada dunia industri dan untuk memenuhi kebutuhan hidup manusia. Salah satu jenis baja yang paling banyak digunakan adalah baja AISI 1045 atau baja karbon sedang. Baja AISI 1045 dibuat dan dibentuk komponen, sparepart, atau alat-alat sesuai dengan kebutuhan di dunia industri, maka muncul upaya untuk memperbaiki sifat mekanik dan ketahanan terhadap korosi. Tujuan dari penelitian ini adalah untuk mengetahui pengaruh temperatur dan media pendingin pada proses heat treatment terhadap nilai kekerasan baja AISI 1045, mengetahui pengaruh temperatur dan media pendingin pada proses heat treatment terhadap laju korosi baja AISI 1045. Pada penelitian ini spesimen dipanaskan menggunakan tungku pemanas dengan temperatur7500C, 8500C, dan 9500C dengan holding time selama 30 menit. Kemudian masing-masing material dilakukan quenching pada media air mineral dan oli SAE 10w-40. Selanjutnya material dilakukan uji kekerasan dan uji korosi. Hasilnya material mengalami perubahan kekerasan dan laju korosi. Nilai kekerasan tertinggi terjadi pada media pendingin air mineral yaitu 58,2 HRC pada variasi temperatur 8500C dan nilai kekerasan tertinggi media pendingin oli adalah 33,4 HRC pada variasi temperatur 9500C. Laju korosi tertinggi media pendingin air mineral adalah 3,998 ipy pada variasi temperatur 9500C, dan 4,086 ipy pada media pendingin oli dengan variasi temperatur 9500C.Kata kunci: Temperatur, media pendingin, heat treatment, kekerasan, dan laju korosi.
Special Klangon is the presence of a natural viewpoint of Mount Merapi from close range and become a favorite location for the biker mountain bike rider (mountain bike / MTB). The number of visitors who come to make managers overwhelmed to serve visitors because it still uses the management system manually by using stationery and books as a medium of recording transactions every day. The booking system there is still using sms and telfon to make mistakes when logging. The research that will be done is to convert management system in Klangon Tourism Village from manual system to computerized system. With the development of software development technology in the form of framework bebasis PHP is widely used that is Codeigniter (CI). The CI framework was developed to make it easier to develop applications. The structure of the CI Framework has implemented objectoriented programming concepts and the Models-Views-Controller (MVC) approach. This research uses CI in developing this application with Waterfall method. Results Making Information System Booking Klangon Tour is an online booking process that provides information about any product that is in Klangon tourism and provide tour package information and price list of the package, Users can choose existing tour packages. Finally implementation can provide the functional benefits of online ticket management.
Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to evaluate the pronunciation of the Arabic alphabet. Voice data from six school children are recorded and used to test the performance of the proposed method. The padding technique has been used to augment the voice data before feeding the data to the CNN structure to developed the classification model. In addition, three other feature extraction techniques have been introduced to enable the comparison of the proposed method which employs padding technique. The performance of the proposed method with padding technique is at par with the spectrogram but better than mel-spectrogram and mel-frequency cepstral coefficients. Results also show that the proposed method was able to distinguish the Arabic alphabets that are difficult to pronounce. The proposed method with padding technique may be extended to address other voice pronunciation ability other than the Arabic alphabets.
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