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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.

BackgroundEvaluation of dental treatment is performed by observing dental periapical radiography to obtain information of filling’s condition, pulp tissue, remain dentin thickness, periodontal ligament, and lamina dura. Nevertheless, the radiographic image used often has low quality due to the level of x-ray radiation made low purposely in order to prevent health problem and limited tools capability. This low quality of the radiographic image, for examples, low image contrast, less brightness, and noise existence cause periapical radiography evaluation hard to be performed. This study aims to improve dental radiographic image quality for assisting pulp capping treatment evaluation.Material and MethodsThe research methodology consists of three main stages, i.e. data collection, image enhancement method production, and result validation. Radiographic image data collection in The Dental Hospital UMY. Image enhancement method has been conducted by comparing several methods: contourlet transform (CT), wavelet transform, contrast stretching (CS), and contrast limited adaptive histogram equalization (CLAHE) to reduce noise, to optimize image contrast, and to enhance image brightness.ResultsThe result of this study is according to mean square error (MSE) and peak signal to noise ratio (PSNR) statistics evaluation, it obtains that the highest scores of MSE and PSNR in row gained from CT method totaled 5.441453 and 40.53652, followed by CLAHE method with the scores are 10.66326 and 38.00736, CS method whose scores are 12.39881 and 39.18518, and the last is wavelet method with the scores are 15.41569 and 36.25343.ConclusionsNonetheless, MSE and PSNR scores are not enough merely to give a recommendation of any suitable methods for improving contrast, therefore, it needs another success parameter coming from the dentist. Key words:Dental radiography, image enhancement, digital image processing.

For export, papaya fruit should be free of defects and damages. Abnormality in papaya fruit shape represents a defective fruit and is used as one of the main criteria to determine suitability of the fruit to be exported. This paper describes a waveletbased technique used to perform feature extraction to extract unique features which are then used in the classification task to discriminate deformed papaya fruits from well formed fruits using image processing approach. The extracted features, when used in the classification task using linear discriminant analysis (LDA), afford accuracy of more than 98%.

This study is aimed to analyze the variables of external environment, organizational resources, organizational capabilities, and business competitiveness. The study priorities strategy and programs as basic for developing the competitiveness of creative industry in Indonesia. The number of respondents who participated in this survey was 200, while the key informants were 10 people. Method of analysis involved descriptive statistics, and analytical hierarchy process (AHP). Then, data were processed by using both IBM SPSS 24, and Expert Choice 11. The results show that creative industry competitiveness has relatively declined during covid-19 pandemic. Although external environment support, organizational resources, and organizational capabilities were at good shape. The priority strategy for competitiveness development should be focused on strengthen the organizational capabilities by considering the dynamics of external environmental factors and internal resource capacity. Then, the priority programs developed sequentially are increasing partnerships with suppliers, distributors and customers, analyzing social and economic aspects, developing human resource capacity, and using information and communication technology in products and services. In addition, another important program is strengthening the supply chain system.