Penerapan Maximum TF-IDF Normalization Terhadap Metode KNN Untuk Klasifikasi Dataset Multiclass Panichella Pada Review Aplikasi Mobile - Billy Kaleb Hananto; Aryo Pinandito; Agi Putra Kharisma (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Mobile Application's Review is a source for application developer to develop their apps into the way user's want to. There's multiple type of Mobile Application's Review itself, from positive review to negative review, from giving information to just stating problems. Time and effort is needed to classify these review manually. Hence, there's a need for some method to classify these review automatically, especially to unique categories that hopefully can help developer to improve their applications, more than just negative and positive review, one of them being Panichella's Categories which categorize review into 4 categories. Beside those categories, this research will talk about one classification method that really popular, which is Term Frequency - Inverse Document Frequency and K-Nearest Neighbor. Combination of these two method are usually used to categorize texts. This research will try to apply a method called Maximum Term Frequency - Inverse Document Frequency, normalization technique that works by dividing a Term's Frequency by it's Document's highest number of Term, which hopefully will help increase K-Nearest Neighbor's accuracy.