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Prediksi Pertumbuhan Jumlah Penduduk di Provinsi Riau Menggunakan Metode Double Exponential Smoothing Brown

Sitorus, Rio Fit Andika (2023) Prediksi Pertumbuhan Jumlah Penduduk di Provinsi Riau Menggunakan Metode Double Exponential Smoothing Brown. Other thesis, Universitas Islam Riau.

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Abstract

Overpopulation has a negative impact on the environment and social community. Uniyal, et al. (2017), stated that impact overpopulation broadly causing: deforestation, climate change, affecting marine ecosystems, increasing energy demand, expanding areas for residential or urban areas, weakening food security, decreasing biocapacity, welfare problems. Riau Province is one of the developing provinces that has a large population with an average percentage of population growth from 2010-2019 of 2.52% per year, which exceeds the ideal threshold for population growth in developing regions, which is 1.2% per year, and there is no application to predict population growth in Riau Province. Therefore, it is necessary to predict the population growth of Riau Province in the future and implement it into applications to provide easy accessibility and open information. The purpose of this research is to provide an information system as an instrument for predicting population growth so that it becomes useful information for stakeholders to make decisions and determine what actions should be taken as the most appropriate steps to deal with problems in the future. The prediction of population growth in Riau Province uses the method Double Exponential Smoothing Brown with dataset from 1995-2022 at percentage rates error (MAPE) of 2.56% with parameters = 0.54 and an accuracy of 97.44%.

Item Type: Thesis (Other)
Contributors:
Contribution
Contributors
NIDN/NIDK
Thesis advisor
Efendi, Akmar
1024077901
Uncontrolled Keywords: Population growth, Predictions, Double Exponential Smoothing Brown
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Divisions: > Teknik Informatika
Depositing User: Uthi kurnia S.IP
Date Deposited: 01 Sep 2026 02:00
Last Modified: 01 Sep 2026 02:00
URI: https://repository.uir.ac.id/id/eprint/32856

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