Article Per Year (5 Year)
p-Index From 2020 - 2025
1.311
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Jurnal Dedikasi Jurnal Teknik Industri TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JURNAL NASIONAL TEKNIK ELEKTRO Cyberspace: Jurnal Pendidikan Teknologi Informasi Jurnal Tekno Kompak Jurnal Manajemen Informatika dan Sistem Informasi Innovative: Journal Of Social Science Research
Irfan, Muhammad
Universitas Muhammadiyah Malang
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Bulletin of Electrical Engineering and Informatics

 1 
Giving more insight for automatic risk prediction during pregnancy with interpretable machine learning Muhammad Irfan; Setio Basuki; Yufis Azhar
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.2344

Abstract

Maternal mortality rate (MMR) in Indonesia intercensal population survey (SUPAS) was considered high. For pregnancy risk detection, the public health center (puskesmas) applies a Poedji Rochjati screening card (KSPR) demonstrating 20 features. In addition to KSPR, pregnancy risk monitoring has been assisted with a pregnancy control card. Because of the differences in the number of features between the two control cards, it is necessary to make agreements between them. Our objectives are determining the most influential features, exploring the links among features on the KSPR and pregnancy control cards, and building a machine learning model for predicting pregnancy risk. For the first objective, we use correlation-based feature selection (CFS) and C5.0 algorithm. The next objective was answered by the union operation in the features produced by the two techniques. By performing the machine learning experiment on these features, the accuracy of the XGBoost algorithm demonstrated the hightest results of 94% followed by random forest, Naïve Bayes, and k-Nearest neighbor algorithms, 87%, 66%, and 60% respectively. Interpretability aspects are implemented with SHAP and LIME to provide more insight for classification model. In conclusion, the similarity feature generated in the two interpretation approaches confirmed that Cesar was dominant in determining pregnancy risk.
Co-Authors Afirda Desember Riawati Agus Eko Minarno Amir Syarifuddin Amrul Faruq Aura Lintang Alif Salsabila Azhar Adi Darmawan Bambang Yudi Ariadi Basuki, Setio Dandy Ahmad Yani Evi Dwi Wahyuni Ilham Pakaya Khaeruddin Khaeruddin Lailis Syafa’ah Machmud Effendy Merinda Lestandy Mochammad Arif Hidayatullah Nur Alif Sumadi, Fauzi Dwi Setiawan Suwignyo . Syafa'ah, Lailis Syafaah, Lailis Yufis Azhar