PREDIKSI JUMLAH ANAK BERISIKO STUNTING MENGGUNAKAN ALGORITMA NAÏVE BAYES

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dc.contributor.authorYufita, Nurussakinah
dc.contributor.supervisorFatayat, Fatayat
dc.date.accessioned2023-01-31T02:28:14Z
dc.date.available2023-01-31T02:28:14Z
dc.date.issued2022-10
dc.description.abstractThe Government of the Republic of Indonesia in 2017 established the National Strategy (STRANAS) as a form of government effort in accelerating the reduction of stunting in Indonesia. Through the STRANAS, the government succeeded in reducing the stunting rate in Indonesia with the prevalence of stunting from 32.7% in 2013 to 27.67% in 2019. The government has targeted that in 2024 the stunting prevalence rate in Indonesia can decrease to 14 percent. It means that the government should be able to reduce the prevalence rate every year by 2.7%. Based on data from the Pekanbaru City Health Office, in 2021 the area with the highest stunting locus is in Lima Puluh Subdistrict with a stunting prevalence of 7.29%. In this case, the Lima Puluh Health Center, which is responsible for the main health services in handling stunting problems in Lima Puluh District, has made efforts to monitor the nutritional health conditions of toddlers in accordance with the program provided by the government by opening posyandu services every month. However, problems were found, namely delays in data delivery by posyandu cadres and data input by puskesmas health workers making monitoring of the nutritional health of children under five too late. Therefore, this study was conducted to predict children at risk of stunting using the nave Bayes algorithm and the accuracy of the algorithm was measured using a confusion matrix. Training data and testing data are divided using K-fold cross validation. Based on the results of research using 830 data, the best accuracy is found in fold 5 with an accuracy presentation of 73% and is implemented in a web-based programming system.en_US
dc.description.sponsorshipFakultas Matematika dan Ilmu Pengetahuan Alamen_US
dc.identifier.citationPerpustakaanen_US
dc.identifier.otherElfitra
dc.identifier.urihttps://repository.unri.ac.id/handle/123456789/10833
dc.language.isoenen_US
dc.publisherElfitraen_US
dc.subjectMalnutritionen_US
dc.subjectNaïve Bayesen_US
dc.subjectPredictionen_US
dc.titlePREDIKSI JUMLAH ANAK BERISIKO STUNTING MENGGUNAKAN ALGORITMA NAÏVE BAYESen_US
dc.typeArticleen_US

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