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All Journal Journal of Information Technology and Computer Engineering Information Technology Education Journal
Pulung Hendro Prastyo
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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A Cardiotocographic Classification using Feature Selection: A comparative Study Septian Eko Prasetyo; Pulung Hendro Prastyo; Shindy Arti
JITCE (Journal of Information Technology and Computer Engineering) Vol 5 No 01 (2021): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.5.01.25-32.2021

Abstract

Cardiotocography is a series of inspections to determine the health of the fetus in pregnancy. The inspection process is carried out by recording the baby's heart rate information whether in a healthy condition or contrarily. In addition, uterine contractions are also used to determine the health condition of the fetus. Fetal health is classified into 3 conditions namely normal, suspect, and pathological. This paper was performed to compare a classification algorithm for diagnosing the result of the cardiotocographic inspection. An experimental scheme is performed using feature selection and not using it. CFS Subset Evaluation, Info Gain, and Chi-Square are used to select the best feature which correlated to each other. The data set was obtained from the UCI Machine Learning repository available freely. To find out the performance of the classification algorithm, this study uses an evaluation matrix of precision, Recall, F-Measure, MCC, ROC, PRC, and Accuracy. The results showed that all algorithms can provide fairly good classification. However, the combination of the Random Forest algorithm and the Info Gain Feature Selection gives the best results with an accuracy of 93.74%.
Indonesian Automated Essay Scoring: A Comparative Study of Pretrained Transformer Models Pulung Hendro Prastyo; Eddy Tungadi; Shaifudin Zuhdi
Information Technology Education Journal Vol. 4, No. 2, May (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i2.8069

Abstract

Manual essay scoring is often characterized by inefficiency and inconsistency. This process is notably time-consuming, leading to delayed feedback and increased susceptibility to evaluator fatigue and subjective bias, thereby posing significant challenges. Automated Essay Scoring (AES) offers a scalable, robust, and consistent solution to these issues. However, the performance of AES models can vary considerably depending on the specific application. Therefore, this study evaluated ten Indonesian pretrained transformer models from Hugging Face for AES tasks, using 300 essay responses from a Research Methodology quiz at Politeknik Negeri Ujung Pandang. Performance was assessed using Root Mean Square Error (RMSE) and Quadratic Weighted Kappa (QWK). Among the evaluated models, Indobenchmark/indobert-base-p2 (BERT-02) demonstrated superior performance. It achieved the lowest RMSE of 5.664 and the highest QWK score of 0.6745. The findings suggest that BERT-02 is the most effective model for Indonesian AES tasks. Future research could explore larger datasets and different models to further enhance the performance and understanding of Indonesian AES systems.
Co-Authors Septian Eko Prasetyo Shaifudin Zuhdi Shindy Arti TUNGADI, EDDY