Pebandingan Performa Naïve Bayes dan KNN pada Klasifikasi Teks Sentimen Jasa Ekspedisi

Abstract

This study aims to determine the performance of the naive bayes model and KNN (K-Nearest Neighbor) in classifying sentiment text expedition services. Twitter user reviews on @jntexpressid, @JNE_ID, and @posindonesia will be mined and classified based on positive, neutral, and negative sentiments Data was mined for two months from October1 to December 1 2020, the results obtained on the JNT data are 46.220, JNE 5.364, and Pos Indonesia 11.194. The raw data obtained will be cleaned and labeled before entering using TF-IDF for term weighting. The clean data obtained from the pre-processing of the text will be divided into 70% training data and 30% test data. The over-sampling method is used to balance the dataset and avoid overfitting training data. Naive ayes model and KNN there was a significant increase after the over-sampling method was implemented. The greatest accuracy in naive bayes is found in JNT data of 82%, while KNN on POS data is 71%. In KNN a high K value does not determine the accuracy results, it is proven that K = 2 has the highest accuracy compared to K = 4, K = 6, K = 8, K = 10 after the resampling technique is carried out.

Use Discriminant Analysis to Identify Eroticism-Related Terms in The Lyrics of Dangdut Songs

“…Words with affixes to their base words were returned using stemming. Finally, text data was weighted using TF-IDF to convert it into numerical form [18]. Because it helps balance the weight between words that appear frequently and words that appear rarely, the term frequency-inverse document frequency (TF-IDF) is very commonly used in weighting [19].…”

“…Words with a high TF weight are those that show up frequently in documents. In the meantime, IDF will assign the words that appear in the document the least amount of weight [18]. 1 above demonstrates that there were only 2 columns and 111 instances in the original data dimensions.…”

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