Time Series Forecasting of Ship Departure Health Inspections for Strengthening Quarantine Surveillance Using the ARIMA Model

Yopi Riski Mei Sandra; Mahmudah Mahmudah; Acub Zaenal Amoe; Jumali Jumali; M. Abriyanto

doi:10.47650/pjphsr.v5i3.2114

Yopi Riski Mei Sandra Departemen Kesehatan lingkungan, Fakultas Kesehatan Masyarakat, Universitas Airlangga, Surabaya, Indonesia
Mahmudah Mahmudah Departemen Epidemiologi, Biostatistika, Kependudukan dan Promosi Kesehatan Fakultas Kesehatan Masyarakat, Universitas Airlangga, Surabaya, Indonesia
Acub Zaenal Amoe Balai Kekarantinaan Kesehatan Kelas I Probolinggo, Probolinggo, Indonesia
Jumali Jumali Balai Kekarantinaan Kesehatan Kelas I Probolinggo, Probolinggo, Indonesia
M. Abriyanto Balai Kekarantinaan Kesehatan Kelas I Probolinggo, Probolinggo, Indonesia

DOI: https://doi.org/10.47650/pjphsr.v5i3.2114

Keywords: ARIMA, Ship Departure, Inspection, Time Series, Health Quarantine

Abstract

ARIMA (Autoregressive Integrated Moving Average) is a time series analysis method used to evaluate data based on temporal patterns. The number of ship departure inspections conducted by the Probolinggo Class I Health Quarantine Center has shown fluctuations over time. These inspections are part of disease prevention efforts as regulated in the Indonesian Minister of Health Regulation No. 10 of 2023 concerning the Organization and Work Procedures of the Quarantine Technical Implementation Unit. This study aims to forecast the number of ship departure inspections at the Probolinggo Class I Health Quarantine Center. This research employed a non-reactive design using secondary data from 2020 to 2023, sourced from the Health Quarantine Information System (SINKARKES). The ARIMA (2,0,2) model provided the best fit, with good accuracy (MSE 685,277; MAPE 7.311). Forecasting results show an upward trend in ship departure inspections throughout 2024. This increase is highly relevant for public health, as stronger inspection activity supports quarantine surveillance, helps detect potential disease risks early, and improves preparedness against cross-border health threats.

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