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All Journal BERKALA SAINSTEK Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Teknik Informatika (JUTIF) BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Kreativitas dan Inovasi (Jurnal Kreanova) Jurnal Pengabdian Masyarakat Journal of Digital Literacy and Volunteering Journal of Informatics Development RECORD Journal of Loyality and Community Development Journal of Digital Literacy and Volunteering
Fauziah, Difari Afreyna
Institut Teknologi Dan Sains Mandala
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Journal : Journal of Informatics Development

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Detection of Diabetes in Pregnant Women Using Machine Learning as an Effort Towards Golden Indonesia 2045 Muliawan, Agung; Rohim, Muhamat Abdul; Fauziah, Difari Afreyna; Yusuf, Hamzah Fansuri
Journal of Informatics Development Vol. 3 No. 1 (2024): Oktober 2024
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i1.1418

Abstract

One of the goals of the Golden Indonesia 2045 program is to utilize health technology to enhance public health, with diabetes being a prominent concern. This research aims to employ ensemble classifier optimization techniques in machine learning for the early detection of diabetes among pregnant women. The study uses physiological data, including variables such as glucose levels, number of pregnancies, skin thickness, blood pressure, insulin levels, body weight, family history, and age. By combining multiple models, ensemble classifiers can enhance prediction accuracy, stability, and overall model performance. This research utilizes an open Kaggle dataset on pregnant women to train and test machine learning models, specifically Support Vector Machine (SVM) and Deep Learning, incorporating ensemble techniques such as bagging and boosting. Experimental results indicate that the ensemble classifier approach significantly enhances diabetes classification, with SVM using bagging achieving the highest accuracy at 76.95%. These findings suggest that ensemble classifier methods could be a valuable tool for early diabetes detection, providing timely intervention and improved risk management during pregnancy, which supports the objectives of improving public health under the Golden Indonesia 2045 initiative.
Co-Authors Abdul Wahid Achmad Maududie Agung Muliawan Ahmad Nurdianyah Aini, Arifatul Andita Prasetyo , Nur Andita Prasetyo, Nur Angga Pranadaning, Fadila Arini Shufia Dwi Sukmawati Daffa Duta Perdana, Mohammad Dwi Natasya, Amelia Edwar Yudhistira, Gedion Hamzah Fansuri Hary Sulaksono Hermansyah, Masud Hermawan, Muhammad Ida Ayu Putu Sri Widnyani Ifrina Nuritha Iqbal Sabilirrasyad laili, Nufilatul Muhaimin Dimyati Muhamat Abdul Rohim Narisa, Defani Nurfaizeh, Nurfaizeh Nurhayati Nurhayati Oktavinda, Diana Purnamie Titisari Shofyan, Firdaus Syaifuddin, Ageng Tamriatin Hidayah Wiranto, Ferry Zahro Alifia Rohma, Sofiatus