Backpropagation Neural Network Untuk Prediksi Kebutuhan Pemakaian Obat (Kasus Di RSUD dr. Adnaan WD)

H Hazlita(1*), Sarjon Defit(2), Gunadi Widi Nurcahyo(3),

(1) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(2) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(3) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(*) Corresponding Author

Abstract


Artificial Intelligence which is developing increasingly rapidly makes it possible to make predictions. Predictions are made using one of the Artificial Intelligence systems, namely Artificial Neural Networks. Predicting the need for drug use is a problem currently being faced by RSUD dr. Adnaan WD Payakumbuh so that the service is not optimal. This research aims to design an Artificial Neural Network architecture and determine the resulting level of accuracy in predicting the need for drug use. The method used in this research is the Backpropagation method. The stages in the Backpropagation algorithm include the initial weight initialization process, activation stage, weight change and iteration stage. The data processed in this research is drug use data obtained from the Pharmacy Installation at dr. Adnaan WD Payakumbuh Hospital. The results of this research show that the best network architecture is 12-12-1 with a relatively small Mean Squared Error (MSE) value of 0.00685, a Mean Absolute Percentage Error (MAPE) value of 0.1696% and a high level of accuracy reaching 99 .83% for the prediction of Paracetamol 150 mg. The results of this research can help health service centers optimize their services

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References


S. Supriyanto, S. Sunardi, and I. Riadi, “Pengaruh Nilai Hidden layer dan Learning rate Terhadap Kecepatan Pelatihan Jaringan Saraf Tiruan Backpropagation,” JIKO (Jurnal Inform. dan Komputer), vol. 6, no. 1, p. 27, 2022, doi: 10.26798/jiko.v6i1.508.

J. Parab, M. Sequeira, M. Lanjewar, C. Pinto, and G. Naik, “Backpropagation Neural Network-Based Machine Learning Model for Prediction of Blood Urea and Glucose in CKD Patients,” IEEE J. Transl. Eng. Heal. Med., vol. 9, no. May, pp. 1–8, 2021, doi: 10.1109/JTEHM.2021.3079714.

J. E. Riwurohi and Studi, “Prediction of Number of Elderly Hajj Registrant Using Backpropagation Artificial Neural Network,” vol. 4, no. 2, pp. 112–121, 2021, doi: 10.33387/jiko.

D. Hutabarat, Solikhun, M. Fauzan, A. P. Windarto, and F. Rizki, “Penerapan Algoritma Backpropagation dalam Memprediksi Hasil Panen Tanaman Sayuran,” BIOS J. Teknol. Inf. dan Rekayasa Komput., vol. 2, no. 1, pp. 21–29, 2021, doi: 10.37148/bios.v2i1.18.

Z. Amarta and J. D. Ma’rifah, “Peramalan Penjualan Produk Furniture Dengan Metode Backpropagation Neural Network,” J. Ilm. Tek. Ind., vol. 9, no. 1, p. 29, 2021, doi: 10.24912/jitiuntar.v9i1.9510.

V. V. Utari, A. Wanto, I. Gunawan, and Z. M. Nasution, “Prediksi Hasil Produksi Kelapa Sawit PTPN IV Bahjambi Menggunakan Algoritma Backpropagation,” J. Comput. Syst. Informatics (JoSYC, vol. 2, no. 3, pp. 271–279, 2021.

B. Yanto, Hendri, Almadison, R. Hutagaol, and R. Rahman, “Analisis Optimasi Algoritma Backpropagation Momentum Dalam Memprediksi Jenis Tingkat Kejahatan Di Kecamatan Tambusai Utara,” J. Ict Apl. Syst., vol. 1, no. 1, pp. 47–60, 2022, doi: 10.56313/jictas.v1i1.165.

W. Santoso and P. Sukmasetya, “JURNAL MEDIA INFORMATIKA BUDIDARMA Prediksi Volume Sampah di TPSA Banyuurip Menggunakan Metode Backpropagation Neural Network,” J. Media Inform. Budidarma, vol. 7, no. 1, pp. 464–472, 2023, doi: 10.30865/mib.v7i1.5499.

A. F. Suahati, A. A. Nurrahman, and O. Rukmana, “Penggunaan Jaringan Syaraf Tiruan – Backpropagation dalam Memprediksi Jumlah Mahasiswa Baru,” J. Media Tek. dan Sist. Ind., vol. 6, no. 1, p. 21, 2022, doi: 10.35194/jmtsi.v6i1.1589.

I. I. Ridho, C. F. Ramadhani, and A. P. Windarto, “Penerapan Artificial

Neural Network dengan Metode Backpropagation Dalam Memprediksi Harga Saham (Kasus: PT. Bank BCA, Tbk),” J. Ris. Sist. Inf. Dan Tek. Inform., vol. 8, pp. 295–303, 2023,

[Online]. Available: https://tunasbangsa.ac.id/ejurnal/index.php/jurasik

E. H. Damanik, E. Irawan, and F. Rizki, “Sma Menggunakan Backpropagation,” vol. 4, no. 2, pp. 1–7, 2021.

E. Elisawati, A. Linarta, A. M. I. Putra, and H. Elvaningsih, “Analysis of Backpropagation Method in Predicting Drug Stock,” SinkrOn, vol. 7, no. 2, pp. 297–307, 2022, doi: 10.33395/sinkron.v7i2.11269.




DOI: http://dx.doi.org/10.30645/jurasik.v9i1.736

DOI (PDF): http://dx.doi.org/10.30645/jurasik.v9i1.736.g711

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