Analisa Bonus Demografi Dengan Algoritma Machine Learning Di Kabupaten Gorontalo Utara

Sumarni Sumarni(1), Suhardi Rustam(2),


(1) Teknik Informatika, Fakultas Ilmu Komputer, Universitas Ichsan Gorontalo
(2) Teknik Informatika, Fakultas Ilmu Komputer, Universitas Ichsan Gorontalo
Corresponding Author

Abstract


Nationally, Indeks has entered the demographic bonus era since 2012 and is predicted to end in 2037. The demographic Bonus is the period during which 100 productive people are dependent on the unproductive population of a country below 50. The results of the 2020 population census recorded that the population in Gorontalo province reached 1,171,681 people, a data source from the Central Statistics Agency (BPS) of Gorontalo province. Not increasing opportunities in such a rapid digital era makes employment decline, especially in labor-intensive activities. On the other hand, the advancement of information technology opens up many new business opportunities. This is the challenge of this nation in welcoming the demographic bonus. Demographic Bonus in Gorontalo. The problem of the impact of the demographic bonus in addition to the ongoing covid 19 pandemic also holds many problems that will also have an impact on uneven economic recovery, unemployment,stunting, and poor nutrition. The government’s efforts to overcome and control population growth have had an impact on changes in the demographic structure of the population in Gorontalo. The purpose of this study is to produce a product description of knowledge pattern analysis of population data in the province of North Gorontalo from data extraction with machine learning algorithms. Clustering analysis, the experimental results with K-Means model k value with the best bouldin indeks feature is at K=3, for classification analysis with decision tree model of the demographic dataset of North Gorontalo region has an accuracy of 89.29% while using the random forest model reaches 100% accuracy.


Keywords


Demography, Population, Policy, Machine Learning, Algorithm

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DOI: 10.56327/jtksi.v6i1.1391

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