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All Journal Jurnal Sistem Cerdas The Indonesian Journal of Computer Science
Khairil Ahsyar, Tengku
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Sentiment Analysis ChatGPT Using the Multinominal Naïve Bayes Classifier (NBC) Algorithm Sri Rahayu, Dwi; Novita, Rice; Khairil Ahsyar, Tengku; Zarnelly
Jurnal Sistem Cerdas Vol. 7 No. 1 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i1.388

Abstract

Chatbots have become one of the popular solutions for improving customer service. One well-known chatbot is ChatGPT, a language model developed by OpenAI. As time goes by and more and more people use ChatGPT, sentiment analysis is needed about users' opinions about the ChatGPT service. Therefore, it is necessary to carry out sentiment analysis of the ChatGPT service on Twitter to find out how users respond to this chatbot service. In this research, the results showed positive sentiment of 57%, negative sentiment of 29% and neutral sentiment of 14%. Topics for each sentiment were also obtained and sentiment prediction results from 40% of the test data with results of 96% positive, 3.5% negative and 0.5% neutral with a test accuracy of 63%.
analisis Analisis Pengaruh Dimensi Budaya Terhadap Penggunaan Aplikasi Capcut Menggunakan UTAUT2 Ariyansir, Sigit; Syaifullah; Khairil Ahsyar, Tengku; Jazman, Muhammad; Marsal, Arif
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4430

Abstract

This research aims to determine the factors that influence the intention and use of the Capcut application, using Hofstede's extended UTAUT2 model with cultural variables. Data was collected through observation, interviews, document review and questionnaires. The analytical method used is PLS-SEM, with external models, internal models and hypothesis testing. The research results show that of the many hypotheses tested, only H3 (social influence on behavioral intentions), H7b (habits on usage behavior) and H14 (passion/restraint usage behavior) were accepted. Other hypotheses, such as performance expectations, effort expectations, and other cultural dimensions, did not show significant influence on usage intentions and behavior. In conclusion, habitual factors and social influence are the main drivers for adopting the Capcut application, while other factors do not have much influence in the context of this research
Co-Authors Arif Marsal Ariyansir, Sigit Jazman, Muhammad Rahayu, Dwi Sri Rice Novita Syaifullah Zarnelly