The ever-increasing social media users has dramatically contributed to significant growth as far as the volume of online information is concerned. Often, the contents that these users put in social media can give valuable insights on their personalities (e.g., in terms of predicting job satisfaction, specific preferences, as well as the success of professional and romantic relationship) and getting it without the hassle of taking formal personality test. Termed personality prediction, the process involves extracting the digital content into features and mapping it according to a personality model. Owing to its simplicity and proven capability, a well-known personality model, called the big five personality traits, has often been adopted in the literature as the de facto standard for personality assessment. To date, there are many algorithms that can be used to extract embedded contextualized word from textual data for personality prediction system; some of them are based on ensembled model and deep learning. Although useful, existing algorithms such as RNN and LSTM suffers from the following limitations. Firstly, these algorithms take a long time to train the model owing to its sequential inputs. Secondly, these algorithms also lack the ability to capture the true (semantic) meaning of words; therefore, the context is slightly lost. To address these aforementioned limitations, this paper introduces a new prediction using multi model deep learning architecture combined with multiple pre-trained language model such as BERT, RoBERTa, and XLNet as features extraction method on social media data sources. Finally, the system takes the decision based on model averaging to make prediction. Unlike earlier work which adopts a single social media data with open and close vocabulary extraction method, the proposed work uses multiple social media data sources namely Facebook and Twitter and produce a predictive model for each trait using bidirectional context feature combine with extraction method. Our experience with the proposed work has been encouraging as it has outperformed similar existing works in the literature. More precisely, our results achieve a maximum accuracy of 86.2% and 0.912 f1 measure score on the Facebook dataset; 88.5% accuracy and 0.882 f1 measure score on the Twitter dataset.

The rise of social media as a platform for self-expression and self-understanding has led to increased interest in using the Myers–Briggs Type Indicator (MBTI) to explore human personalities. Despite this, there needs to be more research on how other word-embedding techniques, machine learning algorithms, and imbalanced data-handling techniques can improve the results of MBTI personality-type predictions. Our research aimed to investigate the efficacy of these techniques by utilizing the Word2Vec model to obtain a vector representation of words in the corpus data. We implemented several machine learning approaches, including logistic regression, linear support vector classification, stochastic gradient descent, random forest, the extreme gradient boosting classifier, and the cat boosting classifier. In addition, we used the synthetic minority oversampling technique (SMOTE) to address the issue of imbalanced data. The results showed that our approach could achieve a relatively high F1 score (between 0.7383 and 0.8282), depending on the chosen model for predicting and classifying MBTI personality. Furthermore, we found that using SMOTE could improve the selected models’ performance (F1 score between 0.7553 and 0.8337), proving that the machine learning approach integrated with Word2Vec and SMOTE could predict and classify MBTI personality well, thus enhancing the understanding of MBTI.

Technology and information growth make all internet users can play a role in disseminating information, including hoax news. One way that can be done to avoid hoax news is to look for sources of information, but valid news is not always perceived as 'true' by individuals because human judgments can lead to bias. Several studies on automatic hoax news classification have been carried out using various deep learning approaches such as the pre-trained multilingual transformer model. This study focuses on classifying Indonesian hoax news using the pre-trained transformer multilingual model (XLM-R and mBERT) combined with a BERTopic model as a topic distribution model. The result shows that the proposed method outperforms the baseline model in classifying fake news in the low-resource language (Indonesian) with accuracy, precision, recall, and F1 results of 0.9051, 0.9515, 0.8233, and 0.8828 respectively.

Sarcasm detection in the Indonesian language poses a unique set of challenges due to the linguistic nuances and cultural specificities of the Indonesian social media landscape. Understanding the dynamics of sarcasm in this context requires a deep dive into not only language patterns but also the socio-cultural background that shapes the use of sarcasm as a form of criticism and expression. In this study, we developed the first publicly available Indonesian sarcasm detection benchmark datasets from social media texts. We extensively investigated the results of classical machine learning algorithms, pre-trained language models, and recent large language models (LLMs). Our findings show that fine-tuning pre-trained language models is still superior to other techniques, achieving F1 scores of 62.74% and 76.92% on the Reddit and Twitter subsets respectively. Further, we show that recent LLMs fail to perform zero-shot classification for sarcasm detection and that tackling data imbalance requires a more sophisticated data augmentation approach than our basic methods.

Short answer question is one of the methods used to evaluate student cognitive abilities, including memorizing, designing, and freely expressing answers based on their thoughts. Unfortunately, grading short answers is more complicated than grading multiple choices answers. For that problem, several studies have tried to build an artificial intelligence system called automatic short answer grading (ASAG). We tried to improve the accuracy of the ASAG system at scoring student answers in Indonesian by enhancing the earlier state-of-the-art models and methods. They were the bidirectional encoder representations from transformer (BERT) with fine-tuning approach and ridge regression models utilizing advanced feature extraction. We conducted this study by doing stages of literature review, data set preparation, model development, implementation, and comparison. Using two different ASAG data sets, the best result of this study was an achievement of 0.9508 in pearson’s correlation and 0.4138 in root-mean-square error (RMSE) by the BERT-based model with the fine-tuning approach. This result outperformed the results of the previous studies using the same evaluation metrics. Thus, it proved our ASAG system using the BERT model with fine-tuning approach can improve the accuracy of grading short answers.

Indonesia is home to over 700 languages and most people speak their respective regional languages aside from the lingua franca. In this paper, we focus on the task of multilingual machine translation for 45 regional Indonesian languages and introduced Indo-T5 which leveraged the mT5 sequence-to-sequence language model as a baseline. Performances of bilingual and multilingual fine-tuning methods were also compared, in which we found that our models have outperformed current state-of-the-art translation models. We also investigate the use of religious texts from the Bible as an intermediate mid-resource translation domain for low-resource translation domain specialization. Our findings suggest that this two-step fine-tuning approach is highly effective in improving the quality of translations for low-resource text domains. Our results show an increase in SacreBLEU scores when evaluated on the low-resource NusaX dataset. We release our translation models for other researchers to leverage.