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All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Computatio : Journal of Computer Science and Information Systems
Stefanny Claudia
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Computer Science & IT Mathematics Other

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ANALISIS REKOMENDASI PEMINATAN MENGGUNAKAN METODE DECISION TREE DENGAN ALGORITMA C4.5 Stefanny Claudia; Tri Sutrisno
Jurnal Ilmu Komputer dan Sistem Informasi Vol 6, No 2 (2018): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (239.165 KB) | DOI: 10.24912/jiksi.v6i2.2655

Abstract

The application created are used to analyze which thesis preference subject suits students academic performance based on their academic grades. The application also provide online academic consultations features which students can use for their academic consultations. To find their thesis preference, the application use decision tree method with C4.5 algorithm. Testing prediction system using students data from 2012 to 2015 who have found their thesis preference. The value data used is 32 mandatory courses in the Faculty of Information Technology before thesis preference. The application can run , use and perform well in accordance with the design made. Testing is to compare the accuracy of the selected tree model build from training data and the thesis preference students have selected. The average accuracy percentage of this a 72,6227%.
ANALISIS REKOMENDASI PEMINATAN MENGGUNAKAN METODE DECISION TREE DENGAN ALGORITMA C4.5 Tri Sutrisno; Stefanny Claudia
Computatio : Journal of Computer Science and Information Systems Vol 2, No 2 (2018): COMPUTATIO : JOURNAL OF COMPUTER SCIENCE AND INFORMATION SYSTEMS
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1022.176 KB) | DOI: 10.24912/computatio.v2i2.2568

Abstract

The application created are used to analyze which thesis preference subject suits students academic performance based on their academic grades. The application also provide online academic consultations features which students can use for their academic consultations. To find their thesis preference, the application use decision tree method with C4.5 algorithm. Testing prediction system using students data from 2012 to 2015 who have found their thesis preference. The value data used is 32 mandatory courses in the Faculty of Information Technology before thesis preference. The application can run , use and perform well in accordance with the design made. Testing is to compare the accuracy of the selected tree model build from training data and the thesis preference students have selected. The average accuracy percentage of this a 72,6227%.
Co-Authors TRI SUTRISNO Tri Sutrisno