Penerapan Pemilihan Model Arsitektur Terbaik pada Neural Network pada Prediksi Jumlah Siswa SD di Kecamatan Siantar Barat
- Cerah Fitri Ramadhani STIKOM Tunas Bangsa, Pematang Siantar, Indonesia
- Muhammad Noor Hasan Siregar Universitas Graha Nusantara, Padangsidimpuan, Indonesia
- Indra Riyana Rahadjeng Universitas Bina Sarana Informatika, DKI Jakarta, Indonesia
-
Agus Perdana Windarto
*
STIKOM Tunas Bangsa, Pematangsiantar,
Indonesia
(*) Corresponding Author
Abstract
The use of the artificial neural network (Backpropagation) method can be used in determining the best architectural model for predicting the number of elementary school students in the Siantar Barat District. The dataset used is a dataset on the number of Elementary School (SD) students in West Siantar District, Pematang Siantar City in 2017-2021 obtained from the Website of the Ministry of Education, Culture, Research and Technology of the Republic of Indonesia (https://dapo.kemdikbud.go.id /pd/3/076303). The dataset is then divided into 2 parts, namely the training and testing dataset. In the training datasets, attribute X1 is a dataset for 2017, X2 is the dataset for 2018, X3 is a dataset for 2019, and attribute Y (target) is the dataset for 2020. For the test datasets, attribute X1 is the dataset for 2018, attribute X2 is a dataset for 2019, attribute X3 is a dataset for 2020 and attribute Y (target) is a dataset for 2021. The results obtained from the analysis of the Backpropagation and virtualization methods using the MatLab application can be generated with a valid dataset and produce an accuracy rate of 87.5% in architectural models 3-9-1. So that the Backpropagation method can be used as a prediction method that makes it very easy to find predictions.
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