Klasifikasi Opini Masyarakat Terhadap Jasa Ekspedisi JNE dengan Naïve Bayes

*Fithri Selva Jumeilah  -  STMIK Global Informatika MDP, Indonesia
Received: 7 Mar 2018; Published: 30 Apr 2018.
Open Access
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Section: Research Articles
Language: EN
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Data Set Data Mention Akun JNECare
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Abstract

The large number of online sales transactions has increased the number of service users. One of the companies engaged in the delivery service in Indonesia is Tiki Nugraha Ekakurir or more known JNE. Currently, JNE service users reach 14.000.000 per month. JNE has used many media communications with its customers one of them with Twitter. The number of followers of JNECare is 108,000 and the number of tweets is 375,000. The number of comments for people who can be used to see what they think of JNE is an inseparable comment is a negative, positive or neutral category. To simplify the grouping of comments, the data will be classified using the Naïve Bayes method present in Rstudio. The amount data used on the internet is 1725 tweets. The data will be divided into allegations of 70% data training as much as 1208 data and 30% data testing or as many as 517 data. Before the data is classified the previous data must go through the process of preprocessing that is changing all the letters into lowercase and other letters other than letters and spaces (case folding), tokenizing words, and the removal of the word common (stopword remove). After the data is cleared the data will be labeled manually one by one and new data can be used for the training process to get the probability model for each category. Probailitas obtained by using Naïve bayes algorithm. Models obtained from the training will be used using data testing. The categories obtained from the test will be used to process the data used by using the confusion matrix and will calculate the accuracy, precision and recall. From the results of the classification of JNE comments obtained that Naïve Bayes was able to classify the data well. This is evidenced by the average percentage accuracy of 85%, 78% precision and 67% recall.

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Keywords
Text Mining; Classification; Naïve bayes; Rstudio; Crawling

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