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All Journal International Journal of Computer Science and Humanitarian AI
I Gede Maha Prastya Budi Dharma
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Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Smoker Melanosis Classification Using Oral Photographic Feature Extraction Based On K-Nearest Neighbor I Gede Maha Prastya Budi Dharma; Nada Fitrieyatul Hikmah; Tri Arief Sardjono
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 1 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i1.12418

Abstract

Smoking is one of the causes of various diseases in the body. Smoking can also cause abnormal conditions that are pathological and physiological in the oral cavity, one of which is smoker melanosis. The clinical picture of pigmentation smoker melanosis is the presence of scattered brown spots with a diameter of less than 1 cm and is most often located on the gingiva. The data was taken using the oral photograph image capture method using a 12MP resolution camera, provided that the object distance from the camera was 6 cm and the flash was on. This analysis utilized the Gingiva Pigmentation Index (GPI) classification system proposed by Hedin, which compares the pigmented area, and Dummett's Oral Colour Index (DOPI), which assesses the density of pigmentation. In this study, the classification process was carried out with the KNN algorithm using features from digital image processing in the segmentation area, the average value of the red, green, and blue colour levels. The classification process uses the nearest neighbour value of 3 and a p-value of 2 to measure the distance to the nearest neighbor using the Minkowski distance formula. The results of the test data accuracy (1.0) with F1 scores for each class for test data DOPI 0 = 1.0, DOPI 1 = 1.0, DOPI 2 = 1.0, DOPI 3 = 1.0. Meanwhile, the classification process can use more up-to-date methods, such as CNN to improve classification accuracy.
Co-Authors Nada Fitrieyatul Hikmah Tri Arief Sardjono