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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Indonesian Journal of Information System PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Lya Hulliyyatus Suadaa
Politeknik Statistika STIS
Computer Science & IT Control & Systems Engineering

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Journal : IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

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Transfer Learning of Pre-trained Transformers for Covid-19 Hoax Detection in Indonesian Language Lya Hulliyyatus Suadaa; Ibnu Santoso; Amanda Tabitha Bulan Panjaitan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.66205

Abstract

Nowadays, internet has become the most popular source of news. However, the validity of the online news articles is difficult to assess, whether it is a fact or a hoax. Hoaxes related to Covid-19 brought a problematic effect to human life. An accurate hoax detection system is important to filter abundant information on the internet.  In this research, a Covid-19 hoax detection system was proposed by transfer learning of pre-trained transformer models. Fine-tuned original pre-trained BERT, multilingual pre-trained mBERT, and monolingual pre-trained IndoBERT were used to solve the classification task in the hoax detection system. Based on the experimental results, fine-tuned IndoBERT models trained on monolingual Indonesian corpus outperform fine-tuned original and multilingual BERT with uncased versions. However, the fine-tuned mBERT cased model trained on a larger corpus achieved the best performance.
Aspect-Based Sentiment Analysis in Bromo Tengger Semeru National Park Indonesia Based on Google Maps User Reviews Cynthia As Bahri; Lya Hulliyyatus Suadaa
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 1 (2023): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.77354

Abstract

Technology can influence and shape a person's behavior patterns when planning tours, traveling, and after traveling. Visitors' reviews can be used as evaluation material to improve the quality of tourist destinations and become a determining factor for other tourists to visit or revisit the destinations. The process of utilizing these reviews can be done by assessing the aspects of tourist destinations based on reviews from visitors. This study aims to conduct an aspect-based sentiment analysis on one of the tourist destinations in Indonesia, namely Bromo Tengger Semeru National Park, based on reviews of Google Maps users. The aspects consist of attractions, facilities, access, and price. The sentiment classification model used is a machine learning model consisting of SVM, Complement Naïve Bayes, Logistic Regression, and transfer learning from pre-trained BERT, IndoBERT, and mBERT. Based on the experimental results, transfer learning from the IndoBERT model achieved the best performance with accuracy and F1-Score of 91.48% and 71.56%, respectively. In addition, among the machine learning models used, the SVM model gives the best results with an accuracy of 89.16% and an F1-Score of 62.23%.
Co-Authors Amanda Tabitha Bulan Panjaitan Cynthia As Bahri Hana Raihanatul Jannah Ibnu Santoso Iftitah Athiyyah Rahma Indah Simbolon Muhammad Farhan Nicholas H Manurung Nur Ainun Daulay Renata De La Rosa Manik Rifqi Ramadhan Rizka Maulida Yanti Sukma Andini