A multi-label classification on topics of Indonesian news using K-Nearest Neighbor

Abstract

News has become a basic human need along with technological and internet developments. This causes the process of disseminating information on the news that switched from print media to the digital era. Another problem that appears when classifying news is multi-label. Multi-label classification is different from single label classification. A single label classification will classify documents into one label only. While multi-label classification can group documents into more than one label. For example, news articles that discuss in detail the early detection of ovarian cancer with a bioinformatics approach may have more than one label such as health, bioinformatics, and women. In this paper, a classification model is developed that can identify classes in each multi-label news article using K-Nearest Neighbor. The advantages of K-Nearest Neighbor are algorithms that are very suitable for multi-label cases; even KNN can be superior to other classifiers. From the system created, the results of the value of system performance as measured by the size of the closeness are the comparison between Manhattan Distance, Euclidean Distance and Supremum Distance using the K = 11 parameters, resulting in a Hamming Loss value of 11.16.%.