PENERAPAN ALGORITMA KLASIFIKASI TERHADAP SENTIMEN MASYARAKAT MENGGUNAKAN TEXT MINING PADA TOKO ONLINE LAZADA

Putri Anglenia, - (2020) PENERAPAN ALGORITMA KLASIFIKASI TERHADAP SENTIMEN MASYARAKAT MENGGUNAKAN TEXT MINING PADA TOKO ONLINE LAZADA. Skripsi thesis, UIN SULTAN SYARIF KASIM RIAU.

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Abstract

ABSTRACT Twitter is a social media that is widely used by the people of Indonesia. Twitter uses the concept of microblogging that Poouler currently has more than 500 million users with 400 million tweets per day. The tweet contains various opinions and public sentiments regarding reviews, one of which is about the official Lazada online store account. Lazada an e-commerce company that offers a variety of products in various categories. According to the 2019 iPrice E-commerce Merchants Award (iEMA) survey, Lazada’s online store has the most followers on Twitter, 372.950 million followers. Tweets or comments about Lazada online stores can be material or data that can be processed and analyzed using the concept of text mining. Text mining is a technique for retrieving information from a number of high quality unstructured data and obtaining problem data in the form of text or documents from a particular topic. The results of this research aims to look at the comparison using 3 classification algorithms, that is Na¨ıve Bayes Classifier (NBC), K-Nearest Neighbor (KNN) and Probabilistic Neural Network (PNN). The division of data in this study used 10 K on the K-fold Cross Validation which then calculated its accuracy for a comparison of superior accuracy. The results of KNN accuracy are higher than NBC and PNN with KNN accuracy of 72.85% while NBC 66.66% and PNN 67.14%. Keywords: Accuracy, Na¨ıve Bayes Classifier, K-Nearest Neighbor, Probabilistic Neural Network, Text Mining

Item Type: Thesis (Skripsi)
Subjects: 000 Karya Umum > 003 Sistem-sistem
Divisions: Fakultas Sains dan Teknologi > Sistem Informasi
Depositing User: fsains -
Date Deposited: 08 Oct 2020 04:22
Last Modified: 08 Oct 2020 04:23
URI: http://repository.uin-suska.ac.id/id/eprint/30735

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