Sigit Purnomo

Sigit Purnomo

Senior Lecturer at Informatics Department, Universitas Atma Jaya Yogyakarta

Recent posts

Factors Affecting Students Acceptance of Learning Simulation Tools in Computing Education Courses from Social, Technology, and Personal Trait Perspectives

2 minute read

Published:

This study presents a theoretical model to explore the factors influencing students’ acceptance of simulation tools in computing education. These factors include social influences, technology-related aspects, and personal characteristics. The term simulation tools refers to systems that can replicate complex processes and situations, providing students with realistic, hands-on experiences without the risks or costs associated with physical setups. To validate the proposed model, 312 responses from university students were collected. A cross-sectional online survey was conducted, and the participants were selected through purposive sampling. The findings indicated that subjective norms have the most significant direct effect on students perceptions of usefulness, influencing their views on learning outcomes from using simulation tools in computing education courses. Additionally, social support and self-efficacy were also found to have significant effects. However, the impacts of fidelity and innovativeness were not supported. This study sets itself apart from previous research by using a comprehensive approach to explore the factors influencing student acceptance of simulation tools in computing education. Specifically, this research develops a theory based on the Technology Acceptance Model (TAM) and expands it by incorporating environmental factors and personal characteristics of students.

Extraction and attribution of public figures statements for journalism in Indonesia using deep learning

2 minute read

Published:

News articles are usually written by journalists based on statements taken from interviews with public figures. Attribution from such statements provides important information and it can be extracted from news articles to build a knowledge base by developing a sequential tagging scheme such as entity recognition. This research applies two deep learning architectures: recurrent neural networks-based and transformer-based, to establish public figures statement attribution and extraction models in the Indonesian Language. The experiments are conducted using five deep-learning model architectures with two different corpus sizes to investigate the impact of corpus size on each model’s performance. The experiments show that the best model for the RNN-based architecture is PFSA-ID-BLWCA which achieves 81.34 % F1 score, and the best model for the transformer-based is PFSA-ID-TWCA which obtains 81.01 % F1 score. This research also discovers that the size of the corpus influences the model performances. Furthermore, the study lays a foundation to overcome the attribution extraction in another language, especially low-resource languages, with some necessary adjustments.

PFSA-ID: An Annotated Indonesian Corpus and Baseline Model of Public Figures Statements Attributions

2 minute read

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By far, the corpus for the quotation extraction and quotation attribution tasks in Indonesian is still limited in quantity and depth. This study aims to develop an Indonesian corpus of public figure statements attributions and a baseline model for attribution extraction, so it will contribute to fostering research in information extraction for the Indonesian language.

Understanding quotation extraction and attribution: towards automatic extraction of public figure’s statements for journalism in Indonesia

3 minute read

Published:

Extracting information from unstructured data becomes a challenging task for computational linguistics. Public figure’s statement attributed by journalists in a story is one type of information that can be processed into structured data. Therefore, having the knowledge base about this data will be very beneficial for further use, such as for opinion mining, claim detection and fact-checking. This study aims to understand statement extraction tasks and the models that have already been applied to formulate a framework for further study.

Research about Named Entity Recognition Published in ArXiv

2 minute read

Published:

Named Entity Recognition (NER) is a task in Information Extraction consisting in identifying and classifying just some types of information elements, called Named Entities (NE). I have tried to collect and curate some publications form Arxiv that related to NER, and the results were listed here. Please enjoy it!