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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
Agus Hartoyo
Telkom University, Bandung
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Explainable AI: Identification of Writing from Famous Figures in Indonesia Using BERT and Naive Bayes Methods Firdaus Putra Kurniyanto; Agus Hartoyo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5392

Abstract

Identifying the writings of well-known figures in Indonesia is a form of appreciation for the writing itself. By knowing the language style used by every famous figure in Indonesia, we can know the uniqueness of each writer, and it can help us understand the thoughts, ideas, and ideas they convey. This research has yet to be done, so it is still interesting to do further research. In this study, only a few writers were used, so it is still impossible to know the overall language style used by every famous figure in Indonesia. In this study, a system was built to determine the language style used by well-known figures in Indonesia based on their writing using the BERT, Naïve Bayes, and LIME algorithms for explainable AI processes. The results are that the BERT algorithm is better at classifying text with an accuracy of 92% compared to Naïve Bayes, which has an accuracy of 90%. From this study, it was also found that KH. Abdurrahman Wahid and Emha Ainun Nadjib have almost the same style of language in which their writings contain many words with political and religious elements. Dahlan Iskan, his writing contains many words with political and socio-cultural elements, while Pramoedya Ananta Toer's writing uses many pronouns.
AI Explanation related Covid Hoax Detection Using Support Vector Machine and Logistics Regression Methods Naufal Haritsah Luthfi; Agus Hartoyo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5386

Abstract

Hoax news about Covid is still circulating in society. Especially on social media, this phenomenon still occurs. The existence of this disinformation can cause divisions between communities. Currently, technology can classify hoax news and non-hoax news. But no system can see the reasons for a model to classify hoax news and non-hoax news. Therefore, in this study, a system was developed that can see words on a system that detects hoax and non-hoax news using the Support Vector Machine and Logistic Regression methods. Meanwhile, the Explainable AI method is Local Interpretable Model-agnostic Explanations (LIME). The test results show that the SVM and Logistic Regression methods have the highest accuracy of 91% and 95%. The words collected in the dataset are sufficient to differentiate between a hoax and non-hoax news. It was found that hoax news about Covid-19 has many words related to Covid-19, religion, politics, medical, and words that are not related to Covid-19. Among them are "lockdown", "masjid", "rezim", "ventilator", and "kiamat". Meanwhile, non-hoax news about Covid-19 has many words related to Covid-19, government, and medical. Among them are "protokol", "isolasi", "infeksi", "menteri", and "nakes".
Co-Authors Firdaus Putra Kurniyanto Naufal Haritsah Luthfi