Skripsi
KLASIFIKASI EMOSI PADA TEKS BAHASA INDONESIA MENGGUNAKAN K - NEAREST NEIGHBOR
As social beings, humans can communicate in two ways, namely verbal and non-verbal. One way of non-verbal communication is to use text. However, communication using text cannot show one's emotions. Therefore, it is necessary to classify texts in Indonesian. The first step in classifying text is to preprocess the data which consists of casefolding, tokenizing, filtering, stemming, then weighting the words using TF-IDF. In this study, text classification was carried out on conversational texts using the K - Nearest Neighbor method to classify words into four classes, namely, happy, sad, angry, afraid. The test was carried out on 20 test data and obtained an accuracy of 45.0%.
Inventory Code | Barcode | Call Number | Location | Status |
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2107002619 | T54341 | T543412021 | Central Library (Referens) | Available but not for loan - Not for Loan |
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