The population numbers of DKI Jakarta, West Java, Central Java, and East Java on an annual basis are crucial data for the strategic planning and assessment of Indonesia’s medium- and long-term national development. These data cannot be provided through the population registration system. In addition, Badan Pusat Statistik (BPS-Statistics Indonesia) only provides the population data on a regular basis for each five-year period. Therefore, the population projection on a yearly basis is required. The common method used in population projection is the cohort component method (CCM). BPS uses CCM for the estimation of Indonesian population. Unfortunately, this method has several drawbacks with less accuracy. One of the promising methods is nowcasting, which predicts the current value based on a variety of socioeconomic and macroeconomic variables with high-frequency data over a yearly period. In this work, machine learning with two different nowcasting methods are evaluated. Support Vector Regression (SVR) and Multi-Output Support Vector Regression (M-SVR) are applied and compared to predict yearly population nowcasts in four provinces. Furthermore, SVR and M-SVR are compared with CCM. The performance comparison is evaluated based on the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The data used are obtained from BPS, with the output variable is the population of DKI Jakarta, West Java, Central Java, and East Java provinces. The results show that the SVR model performs better than the M-SVR model, as shown by the smaller RMSE and MAPE value of the SVR model. In addition, nowcasting method outperforms CCM method, which shows that nowcasting method is a promising tool to substitute CCM in population projection.
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