Klasifikasi Obat Medis Berdasarkan Ekstraksi Ciri Menggunakan K-Means Clustering

Abstract

The use of drugs and chemicals that should be used in the scope of the pharmaceutical field, but since 2014 the use of these drugs and chemicals has been misused in the community such as PCC (paracetamol), caffeine, carisoprodol, and carnophen. One of the causes of this abuse is due to the community's ignorance in the use and types of drugs, such as not knowing the meaning and meaning of the drug logo or special markings listed on the drug packaging. To make it easier to read and know the names of these types of logos or special marks, it is necessary to develop an application that is able to read logo patterns or special marks using digital image processing. In this study, an application was designed to classify types of medical drug patterns using k-means clustering based on the extraction of shape and texture features. The study began with taking a drug pattern image using a smartphone camera with details of 16 sample images and 24 test images. The image of this result, carried out cropping on the logo to get a detailed image. Furthermore, cropping images which are still in the form of RGB are converted into binary images and grayscale images for feature extraction processes. There are 6 feature parameters used, namely metric, eccentricity, contrast, correlation, energy, and homogeneity. The test results on the test image data, the percentage of success of 91.5% with an accuracy rate of 100% obtained in the test data types of OTC drugs, hard drugs, and narcotics, while OTC images are limited to 66%.