Article Per Year (5 Year)
p-Index From 2020 - 2025
1.36
P-Index
This Author published in this journals
All Journal Media Statistika JOURNAL OF APPLIED INFORMATICS AND COMPUTING MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Informatika: Jurnal Pengembangan IT Parameter: Jurnal Matematika, Statistika dan Terapannya Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications
Suhaeni, Cici
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Indonesian Journal of Statistics and Its Applications

 1 
Exploring a Large Language Model on the ChatGPT Platform for Indonesian Text Preprocessing Tasks Suhaeni, Cici; Kamila, Sabrina Adnin; Fahira, Fani; Yusran, Muhammad; Alfa Dito, Gerry
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p100-116

Abstract

Preprocessing is a crucial step in Natural Language Processing, especially for informal languages like Indonesian, which contain complex morphology, slang, abbreviations, and non-standard expressions. Traditional rule-based tools such as regex, IndoNLP, and Sastrawi are commonly used but often fall short in handling noisy, user-generated text. This study explores the capability of Large Language Model, particularly ChatGPT-o3, in performing Indonesian text preprocessing tasks, namely text cleaning, normalization, stopword removal, and stemming/lemmatization, and compares it to conventional rule-based approaches. Using two types of datasets, consisting of a small example dataset of five manually constructed sentences and a real-world dataset of 100 tweets about the Indonesian “Makan Bergizi Gratis” program, both preprocessing methods were applied and evaluated. Results show that ChatGPT-o3 performs equally well in text cleaning and significantly better in normalization. However, rule-based methods like IndoNLP and Sastrawi still outperform ChatGPT-o3 in stopword removal and stemming. These findings indicate that while ChatGPT-o3 demonstrates strong contextual understanding and linguistic flexibility, they may underperform in rigid, token-based operations without fine-tuning. This study provides initial insights into using Large Language Models as an alternative preprocessing engine for Indonesian text and highlights the need for hybrid approaches or improved prompt design in future applications.
Sentiment Classification on the 2024 Indonesian Presidential Candidate Dataset Using Deep Learning Approaches Suhaeni, Cici; Wijayanto, Hari; Kurnia, Anang
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p83-94

Abstract

This study aims to compare the performance of three deep learning models (LSTM, BiLSTM, and GRU) in the task of sentiment classification for the 2024 Indonesian Presidential Candidate dataset, focusing specifically on the case of Prabowo Subianto. The dataset comprises social media X posts sourced from kaggle, and the analysis investigates the effectiveness of different variants of recurrent neural network architectures in identifying public sentiment. The models were evaluated on accuracy and F1 score. The results demonstrate that BiLSTM outperformed both LSTM and GRU models in all metrics, achieving a testing accuracy of 80.70% and an F1 score of 86.86%, compared to LSTM and GRU which both achieved a testing accuracy of 72.56% and an F1 score of approximately 84%. The higher performance of BiLSTM is attributed to its ability to capture bidirectional context within the text, thereby understanding complex sentiment patterns more effectively. LSTM and GRU models displayed similar performance, therefore BiLSTM is the best model for this dataset. These results indicate that BiLSTM is especially well-suited for analyzing public sentiment towards political figures like Prabowo Subianto, offering significant insights into public discussions surrounding the 2024 Indonesian Presidential Election. This study recommends exploring transformer-based models like BERT or GPT variants to enhance sentiment classification accuracy in this domain.
Co-Authors Abdullah, Adib Roisilmi Agus Mohamad Soleh Alfa Dito, Gerry Anang Kurnia Andespa, Reyuli Bagus Sartono Dito, Gerry Alfa Fahira, Fani Hari Wijayanto Kamila, Sabrina Adnin Kusman Sadik Muhammad Yusran Nisrina Az-Zahra, Putri NURADILLA, SITI Saleh, Agus Muhammad Sari, Jefita Resti Sentana Putra, I Gusti Ngurah Zahra, Latifah