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All Journal Research in the Mathematical and Natural Sciences Jurnal Penyakit Dalam Indonesia
Djafar, Fitria
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Computer Science & IT Education Mathematics Medicine & Pharmacology

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Model Prediksi Mortalitas Pembedahan Pasien Usia Lanjut yang Menjalani Pembedahan Elektif di Rumah Sakit Cipto Mangunkusumo Djafar, Fitria; Dwimartutie, Noto; Chandra, Susilo; Harimurti, Kuntjoro
Jurnal Penyakit Dalam Indonesia Vol. 11, No. 2
Publisher : UI Scholars Hub

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Abstract

Introduction. Elderly patients are at high risk of mortality after elective surgery. The aim of this study was to obtain and evaluate the performance of a mortality prediction model for elderly patients undergoing elective surgery. Methods. The design of this study was a retrospective cohort using medical record data from 747 elderly subjects who underwent elective surgery in the period January 2015–December 2017 at Cipto Mangunkusumo Hospital (RSCM), Jakarta. This study used multivariate analysis with logistic regression to determine significant predictors that were included in the prediction model. The performance of the prediction model was assessed using the Hosmer-Lameshow test, and its discrimination ability was determined by calculating the Area Under the Curve (AUC). Results. Of the total 747 elderly subjects, the elective surgery mortality rate was 14.5%. The predictor variables were functional status [ADL 9-11, OR 1.808 (95% CI 0.848-3.854); ADL 0-8, OR 3.382 (95% CI 1.724-6.634)], comorbidities [CCI 3-4, OR 12.206 (95% CI 5.317-28.018); CCI >5, OR 15.820 (95% CI 6.701-37.347)], albumin level <3 g/dL [OR 3.777 (95% CI 2.105-6.779)], type of surgery [grade II, OR 3.827 (95% CI 1.849- 7.923); grade III, OR 6.560 (95% CI 3.378-12.739)], and ASA status with an ASA score > 3 [OR 5.106 (95% CI 1.841-14.159)] were further included in the components of the surgical mortality predictor scoring system. The mortality prediction model was categorized into low risk (score < 7; probability of mortality 2.33%), medium risk (score 7-10; probability of mortality 25.22%), and high risk (score > 10; probability of mortality 74.67%). The prediction model showed good discrimination [AUC score 0.900 (95% CI: 0.873-0.927)] and good calibration (p=0.718 on Hosmer-Lameshow test). Conclusion. The prediction model of mortality among elderly patients undergoing elective surgery, incorporating factors like functional capacity, comorbidities, preoperative serum albumin concentrations, surgical procedure type, and ASA classification, showed good performance.
Penjadwalan Mata Pelajaran Menggunakan Metode Integer Linear Programming di SMA Negeri 1 Tilango Djafar, Fitria; Katili, Muhammad Rifai; Nasib, Salmun K; Nurwan, Nurwan; Wungguli, Djihad; Arsal, Armayani
Research in the Mathematical and Natural Sciences Vol. 4 No. 1 (2025): November 2024-April 2025
Publisher : Scimadly Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55657/rmns.v4i1.200

Abstract

Penjadwalan mata pelajaran secara optimal sangat penting untuk memastikan kelancaran kegiatan belajar dan mengajar. Di SMA Negeri 1 Tilango, penjadwalan yang dilakukan secara manual oleh pihak kurikulum cenderung memakan waktu yang cukup lama, sehingga sering terjadi bentrok antar mata pelajaran pada waktu yang bersamaan. Proses penjadwalan manual ini cukup sulit karena harus memenuhi semua aturan dan kebijakan sekolah yang berlaku. Untuk mengatasi tantangan tersebut, digunakan metode integer linear programming (ILP) yang dapat membantu menyusun jadwal mata pelajaran secara lebih efisien dan terstruktur. Penelitian ini bertujuan untuk menghasilkan jadwal mata pelajaran yang ideal dengan meminimalkan total bobot pelajaran, hari, dan waktu menggunakan metode ILP. Penyusunan jadwal diselesaikan dengan bantuan software Lingo 18.0. Hasil penelitian menunjukkan bahwa jadwal yang dihasilkan dengan metode ILP lebih optimal dibandingkan dengan penjadwalan manual, karena mampu memenuhi semua batasan dan kendala yang telah ditentukan oleh sekolah..
Co-Authors Armayani Arsal Djihad Wungguli Kuntjoro Harimurti Noto Dwimartutie Nurwan, Nurwan Salmun K. Nasib Susilo Chandra