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All Journal Jurnal Teknik Informatika (JUTIF) Decode: Jurnal Pendidikan Teknologi Informasi
Marentek, Billy
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Computer Science & IT Control & Systems Engineering Education Languange, Linguistic, Communication & Media Other

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Development and Comparative Evaluation of Machine Learning Models using Clinically Relevant Features for Predicting Newborn Patients’ Length of Stay Triyono, Gandung; Marentek, Billy; Syafrullah, Mohammad
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5410

Abstract

The Length of Stay (LOS) of newborns is a crucial indicator for healthcare management and hospital resource allocation. However, prior research has yet to systematically compare machine learning models for newborn LOS prediction using clinically pertinent features in developing-country hospital contexts, creating an important methodological and contextual gap. Accurate prediction of LOS is urgently needed to support timely clinical decision-making and prevent overcrowding, inefficiencies, and unnecessary healthcare costs. This study aims to identify factors influencing LOS and develop a predictive model for newborn LOS using several machine learning algorithms. A comparison was conducted among Linear Regression, Random Forest Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN). The dataset consisted of medical records of newborn patients from three private hospitals in Indonesia. The research included data collection and understanding, data preprocessing, modeling, and evaluation. Experimental results show that Random Forest Regression achieved the best predictive performance, with MAE = 0.019, MSE = 0.011, RMSE = 0.086, and R² = 0.987. Feature importance analysis revealed that gender, referral source, insurance type, and diagnosis were the most influential predictors of LOS. This study contributes to the advancement of machine learning applications in healthcare data analytics and provides evidence-based insights to support neonatal care planning and hospital resource optimization.
Sistem Pendukung Keputusan Pemeringkatan Kinerja Satuan Pengamanan menggunakan AHP dan TOPSIS Marentek, Billy; Triyono, Gandung
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 5 No. 1: MARET 2025
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v5i1.891

Abstract

Penilaian kinerja satuan pengamanan (Satpam) secara objektif dan transparan merupakan tantangan penting di PT Jakarta International Security Service (JISS) Indonesia. Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan (SPK) berbasis metode Analytic Hierarchy Process (AHP) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) guna mendukung evaluasi kinerja Satpam. Metode AHP digunakan untuk menentukan bobot kriteria penilaian berdasarkan tingkat kepentingannya, sementara TOPSIS digunakan untuk merangking alternatif berdasarkan kriteria yang telah dibobot. Kriteria yang digunakan meliputi kepuasan user, absensi, penguasaan SOP, laporan bulanan, kemampuan penanganan kasus, perencanaan kerja, tugas dan tanggung jawab, serta pengontrolan administrasi. Hasil penelitian menunjukkan bahwa kriteria “kepuasan user” dan “absensi” memiliki bobot tertinggi masing-masing sebesar 25,57%. Perankingan dengan TOPSIS menghasilkan Satpam nomor 17 sebagai peringkat terbaik dengan nilai closeness 0,810731, diikuti oleh Satpam nomor 4 dan 11. Sistem ini memberikan evaluasi yang lebih transparan, akurat, dan berbasis data, mendukung pengambilan keputusan yang lebih baik di PT JISS. Penelitian ini menyimpulkan bahwa kombinasi metode AHP dan TOPSIS efektif untuk mengatasi subjektivitas dalam evaluasi kinerja dan dapat dikembangkan lebih lanjut untuk aplikasi yang lebih luas.
Co-Authors Mohammad Syafrullah Triyono, Gandung