The Effectiveness of the "Image Processing" Learning Practice Learning Model in Informatics Engineering Diploma Program

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Abstract

This study aims to analyze the effectiveness of practical learning models for Image Processing Subjects in the Diploma III Informatics Engineering program that suits the needs of the industrial community. The research method used in this study is ex post facto with data collection techniques using a questionnaire. Research results and data analysis from the effectiveness test of learning model learning. showed that the results of the study were obtained in the amount of 12.5% of respondents rated the practice learning model in the image processing course included in the category of quite effective, other respondents in the amount of 25% rated that the learning model used was included in the effective category. Whereas 62.5% considered that the learning model developed was included in the very effective category. The analysis shows that the largest percentage is 62.5% in the analysis of the effectiveness of learning using the learning model included in the category of very effective. Thus it can be concluded that the learning model developed is included in the very effective category. Reliability test for the two validators after validating the model of teaching practice in Image Processing courses, in the process of producing the first table of statistical reliability which shows the output if estimated using the Alpha coefficient. The table shows a satisfactory Alpha reliability of 0.385. In the second table is the output of the analysis through ANOVA. From these outputs, there is no difference in ratings between rater (p> 0.05) and the third table shows ICC output with less satisfactory inter-rater reliability, ie rxx = 0.015, thus the results of the reliability test of the teaching practice model of reliable Image Processing courses .

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The Effectiveness of the “Image Processing” Learning Practice Learning Model in Informatics Engineering Diploma Program. (2019). International Journal of Recent Technology and Engineering, 8(4), 5760–5763. https://doi.org/10.35940/ijrte.d8536.118419

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