Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Hanan, Dimas Afryzal
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Sentiment Study of ChatGPT on Twitter Data with Hybrid K-Means and LSTM Hanan, Dimas Afryzal; Husodo, Ario Yudo; Rassy, Regania Pasca
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 2 (2025)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4791

Abstract

The rapid evolution of artificial intelligence (AI) has transformed the way people interact with technology, with ChatGPT emerging as a standout innovation in natural language processing (NLP). While it offers immense benefits, such as improving productivity and accessibility, it has also sparked debates about trust, transparency, and user experience. This makes understanding public sentiment about ChatGPT both timely and essential.This study explores user sentiments by combining K-Means clustering and Long Short-Term Memory (LSTM) models for analysis. The research utilized a dataset from Kaggle, which underwent extensive preprocessing, including text cleaning, tokenization, and lemmatization. Key features were extracted using TF-IDF and Word2Vec techniques, while clustering was refined with the Elbow Method and Silhouette Score. The data was grouped into three clusters focusing on ChatGPT’s functions, its developers, and user activities. Sentiment analysis using LSTM achieved an impressive accuracy of 98% after five training cycles. The findings highlight that negative sentiments, particularly around technical challenges and transparency, dominate user feedback, signaling areas for improvement. While positive sentiments exist, they remain overshadowed by critical perspectives. This study underscores the importance of enhancing user trust and experience while ensuring ethical and transparent AI development. The insights provided aim to guide developers and policymakers in creating AI technologies that are more user-focused and socially responsible. Future research should include multilingual and cross-platform data to paint a more comprehensive picture.
Sentiment Study of ChatGPT on Twitter Data with Hybrid K-Means and LSTM: Analisis Sentimen Berdasarkan Hasil Klasterisasi K-Means pada Data Pengguna ChatGPT Menggunakan LSTM Hanan, Dimas Afryzal; Husodo, Ario Yudo; Rassy, Regania Pasca
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4791

Abstract

The rapid evolution of artificial intelligence (AI) has transformed the way people interact with technology, with ChatGPT emerging as a standout innovation in natural language processing (NLP). While it offers immense benefits, such as improving productivity and accessibility, it has also sparked debates about trust, transparency, and user experience. This makes understanding public sentiment about ChatGPT both timely and essential.This study explores user sentiments by combining K-Means clustering and Long Short-Term Memory (LSTM) models for analysis. The research utilized a dataset from Kaggle, which underwent extensive preprocessing, including text cleaning, tokenization, and lemmatization. Key features were extracted using TF-IDF and Word2Vec techniques, while clustering was refined with the Elbow Method and Silhouette Score. The data was grouped into three clusters focusing on ChatGPT’s functions, its developers, and user activities. Sentiment analysis using LSTM achieved an impressive accuracy of 98% after five training cycles. The findings highlight that negative sentiments, particularly around technical challenges and transparency, dominate user feedback, signaling areas for improvement. While positive sentiments exist, they remain overshadowed by critical perspectives. This study underscores the importance of enhancing user trust and experience while ensuring ethical and transparent AI development. The insights provided aim to guide developers and policymakers in creating AI technologies that are more user-focused and socially responsible. Future research should include multilingual and cross-platform data to paint a more comprehensive picture.
Co-Authors Ario Yudo Husodo, Ario Yudo Rassy, Regania Pasca