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All Journal Technomedia Journal Journal of Applied Engineering and Technological Science (JAETS) Jurnal Teknik Informatika (JUTIF) 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
Agung Muliawan
Institut Teknologi Dan Sains Mandala
Religion Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media 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.
Implementation Of Arima Model In The Analysis Of City Temperature Averag Rohim, Muhamat Abdul; Muliawan, Agung; Wiranto, Ferry
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.v3i1.1419

Abstract

This study analyzes the daily average temperature data of Delhi city from 2013 to 2017 using the Autoregressive Integrated Moving Average (ARIMA) model to model and predict temperature trends. The temperature data processed in this study is non-stationary, so differentiation is applied to achieve stationarity. Two ARIMA models were evaluated: ARIMA (1,1,1) and ARIMA (1,1,1)(1,0,1). The ARIMA (1,1,1) model is effective in capturing short-term patterns, while the ARIMA (1,1,1)(1,0,1) model performs better in handling seasonal components. The findings show that the ARIMA (1,1,1)(1,0,1) model provides more accurate prediction results by accounting for seasonal fluctuations in temperature data. This research is expected to serve as a reference for preventive measures related to temperature changes, as temperature variations can affect public health, infrastructure, and quality of life in rapidly growing cities like Delhi. Understanding temperature trends and making accurate predictions helps in city planning, resource management, and developing adaptation strategies for climate change, which is crucial for mitigating negative impacts and planning for a more sustainable future.
Co-Authors Abdul Wahid Achmad Rizal Ahmad Nurdianyah Andita Prasetyo , Nur Arini Shufia Dwi Sukmawati Fauziah, Difari Afreyna Ferry Wiranto Hamzah Fansuri Hermansyah, Masud Iqbal Sabilirrasyad Iqbal Sabilirrasyad iwan Syarif M Faiz Firdausi Muhaimin Dimyati Muhamat Abdul Rohim Nur Andita Prasetyo Sugondo Hadiyoso Tessy Badriyah, Tessy Wiranto, Ferry