Windy Adira Istiqhfarani
Fakultas Ilmu Komputer, Universitas Brawijaya

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Klasifikasi Penyakit Dental caries menggunakan Algoritme Modified K-Nearest Neighbor Windy Adira Istiqhfarani; Imam Cholissodin; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 5 (2020): Mei 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Dental caries or commonly called cavities is a disease in which bacteria can damage the structure of dental tissue such as enamel, dentin, and cementum. Cause of occurrence dental caries is demineralization of tissue on the surface of the teeth of organic acid caused of foods that contain sugar. Caries that are not treated or controlled early can cause tooth decay that is getting worse and eventually tooth extraction. To find out more about the class of caries disease, a dental caries classification system was created using the Modified K-Nearest Neighbor (MKNN) algorithm. This method is a method that developed from the KNN method. The difference between the KNN algorithm and MKNN is calculation of the validity of training data and weight voting. In this study there were 6 classes and 8 symptoms or variables used. The test results of this study include testing the effect of k values, testing the effect of the amount of training data and test data, and its effect on distance. The results of the average accuracy of testing the value of k by 86% with the highest average of 90.66% when k = 3. Testing the effect of the amount of training data get an average accuracy of 71.1% with the highest accuracy of 86.7% on the amount of training data of 70 and for testing the effect of the amount of test data get an average accuracy of 82.2% with the highest accuracy of 86.7% on the amount of test data of 30. Testing the effect of distance get the same results of accuracy in the distance of Manhattan and Minkowski by 86.7%.