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All Journal ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JTP - Jurnal Teknologi Pendidikan Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Journal for Lesson and Learning Studies
Andhika Herayono
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Religion Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences Other

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Kansei Engineering and Multivariate Analysis Methods for Website Display Innovation Zulwisli Zulwisli; Andhika Herayono; Ambiyar Ambiyar; Syahril Syahril; Nurhasan Syah
Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences Vol 5, No 1 (2022): Budapest International Research and Critics Institute February
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v5i1.3608

Abstract

Multivariate Factor Analysis is a technique in statistical calculations that functions in finding the main determining factors that have a major influence in a combination of statistical facts. On the preparation of the website interface according to the usability and function and also the involvement of the user's feelings so that it will intersect with Kansei Engineering. The researcher measured the consumer feeling about the part of the website that was widely used by using the Kansei Engineering Type I method. From the specimen material that had been applied for previous research, a discussion was developed which initially focused more heavily on Kansei Engineering and was used as an alternative update in utilizing multivariate factor studies. The fact that the survey output to the user will be carried out by applying Cronbach's Alpha multivariate statistical planning, Coefficient Correlation Analysis, Principal Component Analysis, Factor Analysis and Partial Least Square which has been applied to previous observations, then the highest value part of the two result factors will be assimilated as a the latest recommendation material that has been combined with the recommendation value of the new website section compared to the results of the previous unit factor analysis.
Co-Authors Ambiyar, Ambiyar Batubara, Qothrun Nada Ma'ruf Dedy Irfan Elfi Tasrif Elsa Sabrina Firdaus Firdaus Hansi Effendi MUHAMMAD ANWAR Resmi Darni Rizky Ema Wulansari Syahril Syahril Zulwisli .