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All Journal International Journal Software Engineering and Computer Science (IJSECS) JURNAL TEKNIK INFORMATIKA DAN KOMPUTER
Mia Kamayani
Universitas Muhammadiyah Prof Dr Hamka
Computer Science & IT

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ANALISIS SENTIMEN TERHADAP ULASAN PENGGUNAAN SHOPEE MELALUI TWEET PADA TWITTER MENGGUNAKAN ALGORITMA NAÏVE BAYES Hilmy Zhafran Muflih; Hafizh Dhery Al Assyam; Faisal Akbar Pangestu; Mia Kamayani
Jurnal Teknik Informatika dan Komputer Vol. 2 No. 2 (2023): Jurnal Teknik Informatika dan Komputer
Publisher : UHAMKA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/jutikom.v2i2.12199

Abstract

The increasing use of the internet among the public is because it is a means to carry out various activities, one of which is buying and selling online or known as e-commerce. One of the largest e-commerce in Indonesia is Shopee. Shopee offers various features for its users. The large number of shopee users results in the large number of responses given to shopee, so the researcher wants to carry out a sentiment analysis process regarding user responses to shopee, whether the response of shopee users is negative or positive. The responses or opinions of Shopee users are taken from tweets in the Twitter application. Tweets typed and written and published by Twitter users about shopee. In this study, researchers used the RapidMiner application to collect tweets data from Twitter users and to apply the Naïve Bayes algorithm. The researcher collected 200 data regarding shopee from Twitter. The results obtained from sentiment analysis using the Naïve Bayes algorithm get 78% negative sentiment and 22% positive sentiment from 200 datasets. The process of testing the Naïve Bayes algorithm using the confusion matrix obtains an accuracy value of 77.50%.
Social Media Sentiment Analysis of Twitter Regarding People's Housing Savings (TAPERA) Using Naïve Bayes Avry Liyanah Dewy; Mia Kamayani
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4126

Abstract

The advancement of technology has transformed how people interact and express opinions on social media platforms. This research examines Twitter conversations regarding Indonesia's government-initiated Housing Savings Program (TAPERA) through sentiment analysis. The study employed Naïve Bayes classification methodology, with data acquisition conducted via Google Colab platform utilizing the tweet-harvest library. The collection process yielded 1,800 tweets matching predetermined search parameters. Data underwent rigorous preprocessing, including text cleaning and manual sentiment annotation to establish reliable training datasets. Examination of 720 test tweets revealed 473 (65.69%) expressed negative sentiment while 247 (34.31%) conveyed positive sentiment toward the program. The implemented Naïve Bayes model achieved 84.17% accuracy, with negative class precision at 88.71% and recall at 88.60%, while positive class precision reached 78.54% with 76.08% recall. Results indicate the Naïve Bayes approach effectively categorizes public sentiment regarding the TAPERA program, offering valuable feedback for stakeholders responsible for program assessment and enhancement.
Co-Authors Avry Liyanah Dewy Faisal Akbar Pangestu Hafizh Dhery Al Assyam Hilmy Zhafran Muflih