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All Journal IJISTECH (International Journal Of Information System & Technology) IJISTECH
Rico Budiyanto
Universitas Nusa Mandiri
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Text Mining for Customer Sentiment Using Naive Bayes and SMOTE Methods on TokopediaCare Twitter Rico Budiyanto; Indah Purnamasari; Dedi Dwi Saputra
IJISTECH (International Journal of Information System and Technology) Vol 6, No 1 (2022): June
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2004.592 KB) | DOI: 10.30645/ijistech.v6i1.221

Abstract

At this time, buying and selling online has become part of the lives of the Indonesian people and the world, especially during the pandemic, marketplace users are increasing and slowly replacing traditional markets. Tokopedia as one of the largest marketplaces in Indonesia has the largest users in the 3rd quarter of 2019. Customer complaints to Tokopedia services can be submitted through Social Media such as Twitter and also other media. Complaints submitted via Twitter to TokopediaCare are still manually identified by Tokopedia customer service so it takes a long time to respond to customer complaints because customer services need so much time to classify where is a complaint or not complaint tweeted. Text mining is used to process customer complaint data through text or sentences submitted by tweets using the Naïve Bayes method and the Synthetic Minority Oversampling Technique Method (SMOTE) feature for the implementation of machine learning can help identify the classification of complaints submitted via Twitter automatically. The use of the Naive Bayes method is added with the Syntethic Minority Oversampling Method feature which is considered better for generating predictions on tweets submitted by customers..
Text Mining for Customer Sentiment Using Naive Bayes and SMOTE Methods on TokopediaCare Twitter Rico Budiyanto; Indah Purnamasari; Dedi Dwi Saputra
IJISTECH (International Journal of Information System and Technology) Vol 6, No 1 (2022): June
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i1.221

Abstract

At this time, buying and selling online has become part of the lives of the Indonesian people and the world, especially during the pandemic, marketplace users are increasing and slowly replacing traditional markets. Tokopedia as one of the largest marketplaces in Indonesia has the largest users in the 3rd quarter of 2019. Customer complaints to Tokopedia services can be submitted through Social Media such as Twitter and also other media. Complaints submitted via Twitter to TokopediaCare are still manually identified by Tokopedia customer service so it takes a long time to respond to customer complaints because customer services need so much time to classify where is a complaint or not complaint tweeted. Text mining is used to process customer complaint data through text or sentences submitted by tweets using the Naïve Bayes method and the Synthetic Minority Oversampling Technique Method (SMOTE) feature for the implementation of machine learning can help identify the classification of complaints submitted via Twitter automatically. The use of the Naive Bayes method is added with the Syntethic Minority Oversampling Method feature which is considered better for generating predictions on tweets submitted by customers..
Co-Authors Dedi Dwi Saputra Indah Purnamasari