Application Of K-Means Clustering Algorithm On Population Growth In Simalungun Regency - Murniati Rambe; M. Safii; Irawan Irawan (International Journal of Basic and Applied Science, 2021)
Population growth is a condition when the population increases from previous years. Population growth has several variables, namely birth, death and migration rates. Positive population growth indicates an increase in population and vice versa. Population growth is caused by a high birth rate with a decrease in the death rate. The high rate of population growth and occurs in a fast period of time is what triggers a population explosion which is closely related to an increase in poverty, unemployment, crime, slum settlements, hunger and other social problems. An increase in the poverty rate occurs when high population growth is not matched by good economic growth accompanied by equitable distribution of income. An increase in unemployment occurs if the increase in population with reduced availability of adequate employment can lead to an increase in criminal cases. By knowing these problems, Data Mining is needed to classify aid receipts, build jobs. by using the K-Means method in clustering the population growth rate. The K-Means method can assist the Government in making decisions and the information needed to solve the problem of population growth and record all densely populated areas in an appropriate way.