Announcement
Starting on July 4, 2018 the Indonesian Publication Index (IPI) has been acquired by the Ministry of Research Technology and Higher Education (RISTEKDIKTI) called GARUDA Garba Rujukan Digital (http://garuda.ristekdikti.go.id)
For further information email to portalgaruda@gmail.com

Thank you
Logo IPI  
Journal > KLIK - KUMPULAN JURNAL ILMU KOMPUTER > K-MEANS UNTUK KLASIFIKASI PENYAKIT KARIES GIGI

 

Full Text PDF (980 kb)
KLIK - KUMPULAN JURNAL ILMU KOMPUTER
Vol 1, No 1 (2014)
K-MEANS UNTUK KLASIFIKASI PENYAKIT KARIES GIGI
Article Info   ABSTRACT
Published date:
05 Apr 2016
 
Oral health problems , a concern which is very important in health development. Results of Household Health Survey report in 2001 showed that oral health in Indonesia are things that need attention. Based on the report of Poly Teeth ITS Medical Center 2009 obtained data characteristics of dental caries and dental caries classes based on the anatomy of the JV Black. From these data can be classification using K - Means clustering method. K -Means clustering method is used for grouping data partitioning system, where data in one group have similar characteristics to each other, and have different characteristics with other groups.
Classification results using K - Means Clustering method will be compared with the results of reports Poly Teeth ITS Medical Center 2009, to compare and get result from accuracy of the K - Means Clustering.

Keywords: K-Means, Caries, Classification

Masalah kesehatan gigi dan mulut, menjadi perhatian yang sangat penting dalam pembangunan kesehatan. Hasil laporan survei Kesehatan Rumah Tangga tahun 2001 menunjukkan bahwa kesehatan gigi dan mulut di Indonesia merupakan hal yang perlu diperhatikan. Berdasarkan hasil laporan Poli Gigi Medical Center ITS 2009 didapatkan data-data berupa data karakteristik karies gigi dan kelas-kelas karies gigi berdasarkan anatomi J. V. Black. Dari data-data tersebut dapat dilakukan pengklasifikasian dengan menggunakan metode Clustering K-Means. Metode Clustering K-Means digunakan karena K-Means melakukan pengelompokkan data dengan sistem partisi, dimana data dalam satu kelompok memiliki karakteristik yang sama satu sama lainnya, dan memiliki karekteristik berbeda dengan kelompok lainnya. Hasil pengklasifikasian metode ClusteringK-Means dibandingkan hasilnya dengan laporan Poli Gigi Medical Center ITS 2009, untuk membandingkan
keakuratanClustering K-Means.

Kata Kunci: K-Means, Karies Gigi, Klasifikasi
Copyrights © 2016