FORECASTING THE NUMBER OF BREAST CANCER AMONG WOMEN IN INDONESIA BASED ON TIME SERIES MODELS

Wulanova Romadhona(1*), Syasya Qonita Azizah(2), Vivin Vivin(3)


(1) Department of Mathematics, The Republic of Indonesia Defense University
(2) Department of Mathematics, The Republic of Indonesia Defense University
(3) Department of Mathematics, The Republic of Indonesia Defense University
(*) Corresponding Author

Abstract


Breast cancer is one of the leading causes of death among women in Indonesia, requiring a mathematical prediction model to support health policy and planning. This study uses two time series forecasting methods with an autocorrelation approach, namely Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing State Space (ETS), to predict the number of new breast cancer cases among women in Indonesia. The data used is secondary data from Gapminder for the period 1990-2021 and analyzed using accuracy metrics such as AIC, BIC, RMSE, MAE, and MAPE. The best ARIMA model obtained was ARIMA (0,2,2), with AIC (358.16) and BIC (362.37) values, as well as smaller RMSE and MAE values compared to the ETS (M,A,N) model. Diagnostic results showed good model fit with ARIMA model residuals being white noise. The forecast results for 2022-2031 show a consistent upward trend in the number of cases, from around 26,218 cases in 2022 to 20,616 cases in 2031. These findings confirm that the ARIMA model is effective in capturing long-term linear patterns and can be used as a basis for formulating strategies for the prevention and early detection of breast cancer in Indonesia.

Keywords


Breast cancer; Forecast; ARIMA; ETS

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DOI: https://doi.org/10.26714/jsunimus.14.1.2026.61-70

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