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All Journal CogITo Smart Journal SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal ICT : Information Communication & Technology Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Abdimas Sangkabira Abditeknika - Jurnal Pengabdian Kepada Masyarakat Kresna: Jurnal Riset dan Pengabdian Masyarakat Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Academic Journal of Computer Science Research (AJCSR) Jurnal Ticom: Technology of Information and Communication JAMAIKA: Jurnal Abdi Masyarakat Telematika MKOM Artinara Jurnal Ticom: Technology of Information and Communication
Pudoli, Ahmad
Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Budi Luhur
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : CogITo Smart Journal

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Predictive Linear Regression Model for Premature Birth Risk Assessment System Kusumaningsih, Dewi; Kadir, Abdul; Pudoli, Ahmad
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.924.402-413

Abstract

Preterm birth is a major cause of neonatal mortality in Indonesia and is influenced by multiple maternal factors. Early prediction models are crucial for supporting timely clinical decision-making and reducing adverse maternal–infant outcomes. The method of this study developed a linear regression–based predictive model using 915 pregnancy medical records from Dr. H. M. Ansari Saleh Regional Hospital, Banjarmasin (2020–2022). The workflow included data preprocessing, feature selection, Min-Max normalization, and experimentation with various train–test split ratios (90:10 to 50:50). Model performance was evaluated using R², Adjusted R², MAE, MSE, RMSE, and MAPE metrics. As the results, the 70:30 split ratio achieved the best accuracy of 89.05% and AUC of 98.10%, with low prediction errors. Optimizations with Adamax and Nadam enhanced stability and reduced MAPE to 1.95%. The optimized linear regression model reliably predicts preterm birth risk and is suitable for clinical decision support, particularly in resource-limited settings.
Co-Authors Abdul Kadir Amri Wicahyo Arinda Hana Syafira Aryabima, Muhammad Iqbal Aryasanti, Agnes Baehaqi Batu Bara, Muhamad Aldi Mudin Brury Triya Sartana Devit Setiono Dewi Kusumaningsih Elizabeth Yohanes Estu, Maulana Alzura Hardjianto, Mardi Iis Torisa Utami Iqbal Suwandi Khalid, Zulvia Kirana, Anindya Sasi Kusumaningsih, Dewi M. Bucci Ryando Mohammad Syafrullah Noripansyah . Noripansyah, Noripansyah Novia Rana Fadhila Nugroho, Radityo Nurmaesah, Nunung Pradana, Rizky Purwanto Purwanto Raharja, Rian Umbara Maulana Ririt Roeswidiah Roeswidiah, Ririt Rusdah Rusdah Ruwirohi, Jan Everhard Septian Apriyanto Triana Anggraini Wicahyo, Amri Yulianawati Yulianawati Yulianawati Yulianawati Yulianawati Yulianawati