Klasifikasi Tingkat Stres Berdasarkan Tweet pada Akun Twitter menggunakan Metode Improved k-Nearest Neighbor dan Seleksi Fitur Chi-square - Mohammad Imron Maulana; Indriati Indriati; Arief Andy Soebroto (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Today, social media has become a new lifestyle for modern society. One of the most popular social media is Twitter. The character limitation when tweeting on Twitter makes the message conveyed by its users short, solid and clear. Thus, the complaints of users as outlined in a tweet can be analyzed using text classification which can then be used as a new method to determine the stress level of a person. In which up to this time, to determine the stress level is still done manually using a questionnaire system. The text classification used in this study is the Improved k-Nearest Neighbor method which is an improvisation of the k-Nearest Neighbor method which has a weakness in the use of k values for all classes. In addition, to improve the accuracy of the system and eliminate less relevant features, chi-square feature selection is used which can eliminate less relevant features without reducing the accuracy of the system. From 5 feature ratio tests, the best value is obtained at the feature ratio of 25% and k-value = 20 with an average precision value of 70%, the average recall is 67.2%, the average accuracy is 83.3%, and the average f-measure is 66.3%. From this research, it can be concluded that feature selection can increase the average precision, recall, accuracy, and f-measure.