Application of the ANN Algorithm to Predict Access to Drinkable Water in North Sumatra Regency (City)
DOI:
https://doi.org/10.64366/ijids.v1i1.16Keywords:
Drinking water; Artificial Neural Networks; Backpropagation; Prediction; DataAbstract
The increase in population has an impact on increasing the need for drinking water, but this is not in line with the fact that not 100% of the people in Indonesia physically receive or consume safe drinking water. This analysis is based on data from the Central Statistics Agency to look at the social, economic and demographic factors of households regarding the availability of adequate physical quality drinking water. This research aims to predict the percentage of households that have access to adequate drinking water using the Artificial Neural Network (ANN) method. The technique used is Backpropogation. Backrpopagation is a supervised neural network training method, it evaluates the error contribution of each neuron after a set of data has been processed. The goal of backpropagataion is to modify weights to train a neural network to map arbitrary inputs to outputs correctly. Therefore, looking at the above problems, this research aims to determine access to adequate drinking water sources by predicting which households have adequate drinking water so that there is no lack of adequate drinking water sources in the City Regency area. Methods and basic data are needed to make predictions. In this research, data was obtained from BPS which used data from 2014 - 2021, with training data from 2014 - 2020 and testing data from 2015 - 2021. Based on the best architecture produced in this research, namely the 6-17-1 architecture with an accretion of 90%. Thus it can be concluded that the Backpropagation Neural Network can provide good accuracy in carrying out the prediction process.
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Copyright (c) 2023 Muhammad Alfahrizi Lubis, Deza Geraldin Salsabilah Saragih, Indah Dea Anastasia, Agus Perdana Windarto, Putrama Alkhairi
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