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All Journal Jurnal Simetris Scientific Journal of Informatics Jurnal Informatika Universitas Pamulang
Achmad Wahid Kurniawan
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Computer Science & IT

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Journal : Scientific Journal of Informatics

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High School Major Classification towards University Students Variable of Score Using Nave Bayes Algorithm Sudibyo, Usman; Astuti, Yani Parti; Kurniawan, Achmad Wahid
Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i2.12017

Abstract

Completeness of data in each institution, such as major in a university, is necessary. Data of former school has important role in the need of students data. However, there is no relationship between data of former school and variable of students score. The suitable classification used in this research is data mining technique which is nave bayes algorithm. This algorithm is able to manage massive data with a relative fast timing. By using this algorithm, the data results 64.77% performances in classifying former major in school towards variable of score. Hence, the researchers optimize selection feature by using Backward Elimination and result 71.71% performances data. It concludes that performance increases with selection feature. The increasing shows that not all variable of score affects the former school major.
High School Major Classification towards University Students Variable of Score Using Naïve Bayes Algorithm Sudibyo, Usman; Astuti, Yani Parti; Kurniawan, Achmad Wahid
Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i2.12017

Abstract

Completeness of data in each institution, such as major in a university, is necessary. Data of former school has important role in the need of students data. However, there is no relationship between data of former school and variable of students’ score. The suitable classification used in this research is data mining technique which is naïve bayes algorithm. This algorithm is able to manage massive data with a relative fast timing. By using this algorithm, the data results 64.77% performances in classifying former major in school towards variable of score. Hence, the researchers optimize selection feature by using Backward Elimination and result 71.71% performances data. It concludes that performance increases with selection feature. The increasing shows that not all variable of score affects the former school major. 
Co-Authors Astuti, Yani Parti Astuti, Yani Parti Astuti, Yani Parti Budi Widjajanto Budi, Setyo Setiawan, Aries Sudibyo, Usman Sudibyo, Usman Sudibyo, Usman Yuniarsi Rahayu