Sentiment Analysis of Whatsapp Application User Satisfaction Using the Naive Bayes Algorithm and Support Vector Machine

@is The Best : Accounting Information Systems and Information Technology Business Enterprise

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Title Sentiment Analysis of Whatsapp Application User Satisfaction Using the Naive Bayes Algorithm and Support Vector Machine
Analisis Sentimen Kepuasan Pengguna Aplikasi Whatsapp Menggunakan Algoritma Naïve Bayes Dan Support Vector Machine
 
Creator Saepulrohman, Acep
Saepudin, Sudin
Gustian, Dudih
 
Description Information and communication technology was currently growing rapidly, one of which was chat or instant messaging applications such as whatsapp, line and telegram.  In october 2020, the majority of instant messaging app users was whatsapp app users, with a total of 2 billion users.  Even though the whatsapp application was in the top ranking and got the highest score, but this could not been used as a measured of satisfaction because there were still negative views on the whatsapp application, some users assumed that whatsapp often had errors when used, then other problems that arise such as the network that the user used was unstable.  To conduct an analysis of this, a sentiment analysis approached was needed to categorize user comments into positive or negative.  This studied used the naïve bayes algorithm with support vector machine in analyzing positive and negative comments on the satisfaction of users of the whatsapp application on the google played store.  From the results of tests carried out on 1500 user commented data, the evaluation of the model used 10 folded crossed validation shows that the leveled of accuracy for whatsapp application user satisfaction based on the naïve bayes algorithm was 70. 40% and support vector machine was 77. 00%, while the auc valued naïve bayes was 0. 585 and support vector machine was 0. 876. From these results, the svm algorithm could been used for researched with the same data characteristics.
Teknologi informasi dan komunikasi saat ini sangat berkembang pesat, salah satunya Aplikasi Chat atau pesan instan seperti WhatsApp, Line dan Telegram. Pada bulan Oktober 2020, mayoritas pengguna aplikasi pesan instan adalah pengguna aplikasi WhatsApp, dengan total 2 miliar pengguna. Sekalipun aplikasi whatsapp tersebut masuk dalam peringkat teratas dan mendapat skor tertinggi, akan tetapi hal tersebut tidak dapat dijadikan tolak ukur kepuasan karena masih terdapat pandangan yang negatif terhadap aplikasi whatsapp, sebagian pengguna menganggap bahwa whatsapp seringkali eror pada saat digunakan, kemudian masalah lain yang muncul seperti jaringan yang digunakan pengguna tidak stabil. Untuk melakukan analisis mengenai hal tersebut diperlukan pendekatan analisis sentimen guna mengkategorikan komentar pengguna menjadi positif atau negatif. Penelitian ini menggunakan algoritma Naïve Bayes dengan Support Vector Machine dalam menganalisa komentar positif dan negatif terhadap kepuasan pengguna aplikasi Whatsapp di Google Play Store. Dari hasil pengujian yang dilakukan terhadap 1500 data komentar pengguna, evaluasi model menggunakan 10 Fold Cross Validation menunjukan bahwa tingkat keakurasian untuk kepuasan pengguna aplikasi whatsapp berdasarkan algoritma Naïve Bayes adalah sebesar 70,40% dan Support Vector Machine sebesar 77,00%, sedangkan nilai AUC Naïve Bayes sebesar 0,585 dan Support Vector Machine adalah  0,876. Dari hasil tersebut algoritma Support Vector Machine dapat digunakan untuk penelitian dengan karakteristik  data yang sama.
 
Publisher Labkat Press KA FTIK UNIKOM
 
Date 2021-12-31
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
 
Format application/pdf
 
Identifier https://ojs.unikom.ac.id/index.php/aisthebest/article/view/4919
10.34010/aisthebest.v6i2.4919
 
Source @is The Best : Accounting Information Systems and Information Technology Business Enterprise; Vol 6 No 2 (2021): @is The Best : Accounting Information Systems and Information Technology Business Enterprise; 91-105
@is The Best : Accounting Information Systems and Information Technology Business Enterprise; Vol 6 No 2 (2021): @is The Best : Accounting Information Systems and Information Technology Business Enterprise; 91-105
2656-808X
2252-9853
10.34010/aisthebest.v6i2
 
Language ind
 
Relation https://ojs.unikom.ac.id/index.php/aisthebest/article/view/4919/2506
 
Rights Copyright (c) 2021 Acep Saepulrohman, Sudin Saepudin, Dudih Gustian
http://creativecommons.org/licenses/by-sa/4.0
 

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