Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Journal of Information Technology and Computer Engineering IJID (International Journal on Informatics for Development)
Pulung Hendro Prastyo
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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%.
Comparative Analysis of the Combination of Metaheuristic and Machine Learning Algorithms Sirmayanti; Farhan Rahman; Pulung Hendro Prastyo; Mahyati
IJID (International Journal on Informatics for Development) Vol. 15 No. 1 (2026): IJID JUNE
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2026.4888

Abstract

Diabetes affects about 1.9% of the global population, mainly through Type 2 diabetes. Machine learning (ML) serves a pivotal role in enhancing diabetes prediction by analyzing complex datasets. Feature selection, a crucial ML pre-processing step, improved prediction accuracy by identifying relevant data and discarding irrelevant features. This study investigates the combination of metaheuristic algorithms and ML techniques to enhance diabetes prediction accuracy and computational efficiency. Utilizing the PIMA, Early Stage, and Vanderbilt datasets, experiments evaluated ten algorithm-model combinations based on metrics like accuracy, precision, the Wilcoxon test, and convergence curves. Key findings included that Firefly Algorithm-Logistic Regression, Bat Algorithm-Logistic Regression, and Cuckoo Search-Logistic Regression achieved 74.72% accuracy on PIMA; Firefly Algorithm-Support Vector Machine and Cuckoo Search-Naïve Bayes achieved 83.39% accuracy and 96.15% precision on Early Stage; and Firefly Algorithm-Naïve Bayes achieved 92.88% accuracy and precision on Vanderbilt. These results highlighted the potential of integrating metaheuristics with ML methods to improve clinical diagnostics. Future research is recommended to validate algorithm robustness across diverse datasets to further optimize diabetes prediction strategies.
Co-Authors Farhan Rahman Mahyati Septian Eko Prasetyo Shindy Arti Sirmayanti, Sirmayanti