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All Journal Jurnal Teknik Informatika (JUTIF) IJoICT (International Journal on Information and Communication Technology)
Prati Hutari Gani
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Computer Science & IT Environmental Science

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PERSONALITY DETECTION ON TWITTER USER USING XGBOOST ALGORITHM Adinda Putri Rosyadi; Warih Maharani; Prati Hutari Gani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1166

Abstract

Personality is a person's identity that is addressed to the public. The Big Five personality is the most commonly used personality model. Detecting a person's personality is still a difficult task today. Because personality detection still often requires humans to fill out lengthy questionnaires to evaluate various personality traits. Therefore, a system that is able to identify personality easily and specifically is needed. By using social media, individuals often express their feelings. Twitter is the most popular social networking platform today. In this research, we use the XGBoost Algorithm, a powerful machine learning method, to create a personality detection system that improves upon existing approaches. Our research aims to determine how well the XGBoost algorithm can recognize Big Five personality features in Twitter users. We achieved encouraging results through in-depth investigation and experimentation. The XGBoost algorithm successfully developed a model that can recognize all Big Five personality trait labels but with different precision, recall and f1-score values. The highest value was obtained for the Extroversion label with a precision of 0.92, recall of 1.00 and f1-score of 0.96. Meanwhile, the lowest value is owned by the Agreeableness label with a precision value of 0.29, recall 0.29, and f1-score of 0.29. This research demonstrates the potential of the XGBoost Algorithm for personality discovery on social media platforms, providing a fast and accurate method to identify distinctive characteristics. Overall, the results of this study demonstrate the efficiency of the XGBoost Algorithm in the context of personality recognition, opening the door for further development in understanding and evaluating human behavior through social media platforms such as Twitter.
Text Classification Using Large Language Models to Detect AI and Human-Generated Sentences Arfan Rahman Yudiantoro; Prati Hutari Gani; Donni Richasdy
IJoICT (International Journal on Information and Communication Technology) Vol. 12 No. 1 (2026): Vol.12 No.1 Jun 2026
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v12i1.11223

Abstract

With the rapid development of the technology of Artificial Intelligence (AI), especially Large Language Models (LLMs) like ChatGPT, GPT-4 and Gemini, systems have become able to generate texts very similar to human writing. This similarity has been a boon to various sectors, but also poses fresh challenges of content authenticity and data integrity. A significant challenge is finding a way to automatically and accurately distinguish AI-generated sentences from human-written ones. This research focuses on building a text classification model that uses Large Language Models to distinguish between AI-generated and human-written sentences. This approach of research is based on recent research models that combine deep learning and classification using LLMs . The research process involves gathering human and AI-generated text data, pre-processing the text to normalize and tokenize it, feature extraction using the embeddings of large language models like IndoBERT, training the binary classification model, and assessing the model's performance using the metrics accuracy, precision, recall, and F1-score. Keywords: Text clustering, Large language models, AI text identification, Text authenticity.
Co-Authors Adinda Putri Rosyadi Arfan Rahman Yudiantoro Donni Richasdy Warih Maharani