Implementasi Metode Monte Carlo untuk Prediksi Data Time Series Pelayanan Kesehatan Ibu Nifas (KF Lengkap) di Kota Padang

Authors

  • Ahmad Afif Universitas Bung Hatta
  • Arnita Universitas Bung Hatta
  • Rahmayani Afrah Universitas Andalas

DOI:

https://doi.org/10.32493/jiup.v11i2.58274

Keywords:

monte carlo simulation, predection, time series, KF Lengkap, maternal health services

Abstract

Postpartum maternal health services are a key indicator in evaluating public health system performance, particularly through the coverage of Complete Postpartum Visits (KF Lengkap). The KF dataset exhibits fluctuating time series characteristics and is influenced by various external factors, making deterministic forecasting approaches less effective in capturing data uncertainty. This study aims to implement the Monte Carlo method as a probabilistic approach to predict KF coverage for 2025 in Padang City using historical data from 2020–2024. The method constructs an empirical probability distribution based on annual data changes and performs 1,000 simulation iterations for each district. The results indicate varying prediction values across regions; for example, Koto Tangah District is projected to increase from 2,998 to approximately 3,040. Model validation using simple backtesting shows a Mean Absolute Percentage Error (MAPE) below 10%, indicating acceptable predictive performance. This study contributes by demonstrating the feasibility of Monte Carlo simulation for district-level health indicator forecasting, emphasizing uncertainty modeling for risk-informed decision-making. However, the results remain dependent on limited historical data and simplified probability assumptions

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Published

2026-06-30

Issue

Vol. 11 No. 2 (2026): June (in progress)

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