Study on the Use of Multidimensional Database Systems for Demography Data Analysis

Abstract

The demography database system should be very useful for various purposes in overcoming the socio-economic problems of the population, such as the distribution of charity, especially for people affected by the Covid-19 pandemic. For example, those who have lost their jobs, those who have difficulty in accessing medical services, etc., or for general election data, for example, presidential election, legislator election, or governor election. But unfortunately, the demography database system in our country, Indonesia still has many problems. Things to consider are that there is no integration between databases at the central and local governments, besides that there is no synchronization of data between institutions. As a result, there are many mistakes in the distribution of charity for people affected by the Covid-19 pandemic in 2020, such as those who should receive do not receive it, and those who should not receive it receive it. This problem can be caused by the absence of a definite reference in the population data. As an example, On the time dimension, one year's data can be multi-interpreted both between the central and the regions government as well as between agencies. This study aims to build an integrated demography database system through a multidimensional database system model. In the multidimensional database system, the data is a cell that can show information from multi-dimensions (such as spatial, temporal, and variable). Each dimension in a multidimensional database system is interconnected in a system so that it can eliminate multiple interpretations of data in one dimension or between dimensions. Each dimension in a multidimensional database system can be arranged in a hierarchy. In the spatial dimension, it can be arranged based on a hierarchy of administrative units, such as from state down to province, then district/city, sub-district, village, and so on. So that it can eliminate the non-integration between data on the central and local governments. In the dimension of variable, it can be arranged based on the level of detail of demography data, and the total population as the top level of detail. While the next level of detail can be arranged based on criteria from demographic data such as sex, age, occupation, education, etc. So that it can eliminate the non-synchronization of data between institutions.