Sigit Purnomo

Sigit Purnomo

Senior Lecturer at Informatics Department, Universitas Atma Jaya Yogyakarta

Recent posts

Automated Rubric-Based Classification of Student Peer Code Review Feedback

2 minute read

Published:

This study examines automated approaches for classifying student peer code review feedback in Bahasa Indonesia according to rubric-based code quality categories. The study used 2,281 student feedback items collected from peer code review activities in an introductory programming course. The dataset was annotated into seven labels and validated with strong inter-rater agreement. Three approaches were compared: classical machine learning, deep learning, and few-shot prompting using large language models. Random Forest with count vectorization produced the best performance with an F1-score of 0.9430, outperforming recurrent convolutional neural networks with FastText embeddings and few-shot prompting. The findings indicate that classical machine learning with token-based features can be an effective and interpretable baseline for supporting rubric alignment in peer code review tools, especially in computing education contexts using Bahasa Indonesia.

Leveraging Machine Learning in Student Peer Review: A Systematic Literature Review

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Published:

Our study examines how machine learning techniques are integrated into student peer review processes, focusing on the challenges that motivate their adoption and the methods used to address them. Using Kitchenham’s systematic literature review framework, 328 articles were screened, and 25 empirical studies on machine learning applications in student peer review were selected. The findings show that machine learning is mainly used to manage large volumes of reviews, support automated grading, and improve feedback quality. Common techniques include classification, prediction, ranking, and clustering, which help improve the fairness, efficiency, and objectivity of peer review. This study provides a rigorous synthesis of machine learning adoption in student peer review and highlights its potential to enhance assessment accuracy, support learning outcomes, and guide future research and broader implementation in educational contexts.

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.

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.