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All Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Eka Ramadhani Putra
Universitas Putra Indonesia “YPTK” Padang, Indonesia
Computer Science & IT Control & Systems Engineering

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Penerapan Jaringan Syaraf Tiruan Dengan Algoritma Backpropagation Untuk Memprediksi Kunjungan Poliklinik (Studi Kasus Di Rumah Sakit Otak Dr. Drs. M. Hatta Bukittinggi) Eka Ramadhani Putra; Gunadi Widi Nurcahyo; Y Yuhandri
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.354

Abstract

Artificial Neural Networks (ANN) are computational models inspired by the structure and function of biological neural networks. ANN can model and learn complex patterns in data. The Backpropagation algorithm is a training algorithm used to optimize weights and biases in ANN.. Use of Python Applications is a popular form of computing used in the fields of science and engineering, including in the development and implementation of ANN. Python provides powerful library for building, training, and deploying ANNs. This research aims to have the ANN Backpropagation Algorithm train data using previously collected polyclinic visit data so that the ANN can learn to predict the burden of polyclinic visits in the future. The method in this research uses the Backpropagation Algorithm. This method has six stages, namely data input, normalization, training, testing, calculating test accuracy, and prediction. The dataset processed in this research comes from the annual report of Rumah Sakit Otak Dr. Drs. M. Hatta Bukittinggi from 2020 to 2022. The dataset consists of 36 months of visits to the polyclinic. The results of this research use the 3-10-1 pattern and can identify or calculate predictions for the next 5 months, 2547 people, 2506 people, 2463 people, 2482 people, and 2495 people. The percentage of predictions for polyclinic patient visits with an accuracy level of computing time requiring 0.001 seconds, an average error of 8.794%, and an average accuracy of 91.706%. Therefore, this research can be a reference in predicting polyclinic patient visits in the future so that it can be a consideration for hospital management.
Co-Authors Gunadi Widi Nurcahyo Y Yuhandri