Klasifikasi Pesan Biasa, Operator, Spam, dan Debt Collector Menggunakan K-Nearest Neighbor.docx: Indonesia - Arisona, Dian Christien; Adhi Wibowo, Gusti Ngurah; Siswanto, Siswanto; Gunawan, Gunawan (Prodi Sistem Informasi UIN Alauddin, 2023)
This research implements a K-Nearest Neighbor (KNN) classifier to predict messages that recognized as ordinary messages, operator messages, spam messages, and debt collector messages. KNN is one of the classification algorithms that can be used to do a text classification, the prediction is made with consideration of distances between the object of observations. The data is messages that have been collected from SMS, whatsapp, and email therefore preprocessing using casefolding, stemming, tokenizing, and stopwords is necessary to do a modelling using KNN methods. The results of this research showed that the accuracy achieved from the training set was 93% and if we just focus on the messages that clasify as messages from debt collector then recall score from the testing set was 83%. This research is expected for further improvement and can be applied to recognizing messages from debt collector so that the victim can feel more comfortable.