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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sistemasi: Jurnal Sistem Informasi
Daniel Sintong Pardamean Simanjuntak
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Computer Science & IT Electrical & Electronics Engineering

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Word Embedding Feature for Improvement Machine Learning Performance in Sentiment Analysis Disney Plus Hotstar Comments Jasmir Jasmir; Nurhadi Nurhadi; Eni Rohaini; M Riza Pahlevi B; Daniel Sintong Pardamean Simanjuntak
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28799

Abstract

In this research we apply several machine learning methods and word embedding features to process social media data, specifically comments on the Disney Plus Hotstar application. The word embedding features used include Word2Vec, GloVe, and FastText. Our aim is to evaluate the impact of these features on the classification performance of machine learning methods such as Naive Bayes (NB), K-Nearest Neighbor (KNN), and Random Forest (RF). NB is very simple and efficient and very sensitive to feature selection. Meanwhile, KNN is known for its weaknesses such as biased k values, overly complex computations, memory limitations, and ignoring irrelevant attributes. Then RF has a weakness, namely that the evaluation value can change significantly with just a slight change in the data. Feature selection in text classification is crucial for enhancing scalability, efficiency, and accuracy. Our testing results indicate that KNN achieved the highest accuracy both before and after feature selection. The FastText feature led to the highest performance for KNN, yielding balanced accuracy, precision, recall, and F1-score values.
Improving the Accuracy of Public Sentiment Analysis on the Presence of Artificial Intelligence using an NLP-based Hybrid Deep Learning Model Daniel Sintong Pardamean Simanjuntak; FA Bambang Sukoco; Ahmad Husaein; Muhammad Zidan
SISTEMASI Vol 15, No 9 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i9.6737

Abstract

The development of Artificial Intelligence (AI) has generated diverse public responses, which are widely expressed through social media platforms such as Twitter/X, TikTok, and YouTube. Understanding public sentiment toward AI is important because it provides insights into public acceptance, concerns, and perceptions regarding the development of this technology. However, sentiment analysis of Indonesian-language social media text remains challenging due to the informal and unstructured nature of the language, including slang, typographical errors, and substantial morphological variations. This study aims to develop a hybrid sentiment analysis model based on Bidirectional Long Short-Term Memory–Conditional Random Fields (BiLSTM–CRF), integrating Word2Vec and FastText to improve the performance of public sentiment classification toward Artificial Intelligence. The dataset was collected from three social media platforms—Twitter/X, TikTok, and YouTube—and comprised 30,550 comments after preprocessing and labeling into three sentiment categories: positive, neutral, and negative. The experiments compared the proposed models with the baseline CRF, LSTM, and BiLSTM models using accuracy, precision, recall, and F1-score as evaluation metrics. The results show that the BiLSTM–CRF model with FastText achieved the best performance, with an accuracy of 93.12%, precision of 92.74%, recall of 92.31%, and F1-score of 92.52%. This performance exceeded that of BiLSTM–CRF with Word2Vec (91.38%), BiLSTM (87.24%), LSTM (84.15%), and CRF (78.42%). These findings demonstrate that integrating BiLSTM–CRF with FastText's subword-based representation is effective in improving sentiment analysis performance on Indonesian-language social media data. This study contributes to the development of more robust, adaptive, and representative sentiment analysis methods for understanding public opinion toward Artificial Intelligence.
Co-Authors Ahmad Husaein FA Bambang Sukoco Jasmir Jasmir M Riza Pahlevi B Muhammad Zidan Nurhadi Nurhadi Rohaini, Eni