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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) STTAL POSTGRADUATE - INTERNATIONAL CONFERENCE At-Tadbir : jurnal ilmiah manajemen JOURNAL OF APPLIED BUSINESS ADMINISTRATION
Budi Prasetiyo
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Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Journal : Lontar Komputer: Jurnal Ilmiah Teknologi Informasi

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Penerapan Dizcretization dan Teknik Bagging Untuk Meningkatkan Akurasi Klasifikasi Berbasis Ensemble pada Algoritma C4.5 dalam Mendiagnosa Diabetes Mirqotussa’adah Mirqotussa’adah; Much Aziz Muslim; Endang Sugiharti; Budi Prasetiyo; Siti Alimah
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 8, No. 2 Agustus 2017
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (470.406 KB) | DOI: 10.24843/LKJITI.2017.v08.i02.p07

Abstract

In the field of health, data mining can be used to predict a disease from patient medical record data, diabetes. There are several data mining models which one is classification. In the access field, there are many branches that are developing the decision tree (decision tree). One popular decision tree is C4.5. In this study, the data used were pima indian diabetes dataset taken from UCI machine learning repository. In this dataset all attributes are of continuous numeric type and for combined continuous data discretization is used. Accuracy is very important in the classification, ensemble method is a method used to improve the accuracy of classification algorithm by building some classifier of training data. From the research results, by applying discretization and bagging techniques to ensemble-based classification on C4.5 algorithm can increase the accuracy of 6.26%. With an initial accuracy of 68.61%, after applied discretization and bagging techniques to 74.87%..
Co-Authors Ayip Riva'i Azi Wardiana Endang Sugiharti Jumanto Unjung Mirqotussa’adah Mirqotussa’adah Much Aziz Muslim Much Aziz Muslim Muhammad Yasin Nina Fitriani Pristiyono Pristiyono Ryan Supu Tauhid Siti Alimah Tiara Lailatul Nikmah