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All Journal Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) SemanTIK : Teknik Informasi Jurnal Teknik Komputer AMIK BSI SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan INTECOMS: Journal of Information Technology and Computer Science Informatika : Jurnal Informatika, Manajemen dan Komputer Jurnal Tekno Kompak Jurnal Mantik Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Tunas Jurnal Multidisiplin Dehasen (MUDE) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Herny Februariyanti
Universitas Stikubank
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Journal : Jurnal Mantik

 1 
SENTIMENT ANALYSIS ON SHOPEE E-COMMERCE USING THE NAÏVE BAYES CLASSIFIER ALGORITHM Yulinar Rizkyani Saputri; Herny Februariyanti
Jurnal Mantik Vol. 6 No. 2 (2022): August: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v6i2.2397

Abstract

The growth of information and technology is causing individuals to live a more digital lifestyle, and one example of this is in the buying and selling activities that already use E-Commerce as a venue to facilitate them. Using the RStudio application and the Naive Bayes Classifier Algorithm, this study aims to find or analyze both positive and negative reviews of the e-commerce application through user reviews on Google Play. The technique employed is text mining and text processing, which involves stemming, tokenizing, case folding, normalization, and filtering steps. Review data for the Shopee app on Google Play is gathered through data scraping utilizing the web application appfollow, and review data may be saved in csv format. The acquired data will be processed using text processing, first by translating reviews in other languages to Indonesian, then by normalizing or cleaning the content to remove emoticons. The normalized data will then be uniformly converted to lower case letters as a whole in the case folding process, which comes next. Each word that has no influence or is independent, which is referred to as a token, will be isolated from the uniform data. Calculating the presence of words in the data and their frequency of occurrence is made easier by the tokenizing procedure. The Nave Bayes Classifier Method is used to compare training data and test data after text has undergone text processing to produce positive and negative sentiment classes based on the number of word frequencies.
Product Review Sentiment Analysis At Online Store Jiniso Official Shop Using Naive Bayes Classifier (Nbc) Method Efta Riana Putri; Herny Februariyanti
Jurnal Mantik Vol. 6 No. 3 (2022): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

It is undeniable that online shopping is the choice of many people during the pandemic, because online shopping is an easy and safe solution for people who are required to carry out social distancing by the government. Jiniso Official shop is one of the online stores that currently sell through Shopee. This study was conducted to categorize and analyze customer views of the product by using Jiniso product review data from the comments column on the Shopee application. In this study, researchers will utilize the text mining process using classification techniques, the algorithm that will be used is the Naive Bayes algorithm. Naive Bayes allows classification based on the assumption of separate conditions between the predicted attributes of a particular class. For this reason, the Naive Bayes Classifier is a very competent classification, it works quite well in classification tasks so many researchers are trying to improve the performance of Naive Bayes [3]. The results of sentiment analysis using NBC produces an accuracy rate of 0.941747572815534 or 94%. From the results of this study, positive sentimental reviews can be used as a reference to maintain things that make customers feel satisfied. while negative reviews can be used as motivation to improve services and products.
Co-Authors Andi Harpepen Arief Fajar Kurniawan Arief Jananto Bagus Andre Nathanael Desiana Mirtasari Dwi Agus Diartono Eddy Nurraharjo Efta Riana Putri Eko Nur Wahyudi Elisa Febriyani Eri Zuliarso Erwin Nofiyanto Fatoni Batari Agung Larasati Firman Ardiansyah Ekoanindiyo Hersatoto Listiyono Jati Sasongko Wibowo Jati Sasongko Wibowo Mahardika Tania Nitami Mardi Siswo Utomo Muhammad Firmansyah Muji Sukur R. Soelistijadi Rizal Adi Saputro Saufika Sukmawati Sulastri Yulinar Rizkyani Saputri Yunus Anis, Yunus Zuly Budiarso