Analisis Model Backpropagation Dalam Meramalkan Tingkat Penjualan Saldo “Link Aja”
-
Dwi Findi Auliasari
*
STIKOM Tunas Bangsa, Pematangsiantar,
Indonesia
- Gita Febrianti STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
- Agus Perdana Windarto STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
- Dedy Hartama STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Abstract
Analysis of a prediction (forecasting) is very important in a study, so that research becomes more precise and directed (Wanto and Windarto, 2017). As is the case in predicting the level of Link Aja's balance sales. This research is expected to be useful for an agency as one of the study materials in business development. A system to predict the level of sales of Link Aja balance at PT. Wahana Putra Yudha. Artificial Neural Network is a method that is able to perform a mathematical process to predict the level of sales of Link Aja Balance at PT. Wahana Putra Yudha. By using the backpropagation method, the previous data processing process is carried out which will be used as input to predict the sales level of Link Aja Balance. The data were taken from January 2021 to April 2022. January 2021 to August 2021 were used as training data, while September 2021 to April 2022 were used as test data. The training architecture model used to predict the sales level of Link Aja's Balance is: 4-2-1; 4-25-1; 4-50.1; 4-75-1; and 4-100-1. The best architecture is 4-50-1, the percentage result is 75% in each test
References
A. Novita, “Prediksi Pergerakan Harga Saham Pada Bank Terbesar Di Indonesia Dengan Metode Backpropagation Neural Network,” Jutisi, vol. 05, no. 01, pp. 965–972, 2009.
Y. Andrian and P. H. Putra, “Analisis Penambahan Momentum Pada Proses Prediksi Curah Hujan Kota Medan Menggunakan Metode,” Semin. Nas. Inform., pp. 165–172, 2017.
S. Andriyani and N. Sihombing, “Implementasi Metode Backpropagation Untuk Prediksi Harga Jual Kelapa Sawit Berdasarkan Kualitas Buah,” Jurteksi, vol. 4, no. 2, pp. 155–164, 2018.
N. P. Sakinah, I. Cholissodin, and A. W. Widodo, “Prediksi Jumlah Permintaan Koran Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 7, pp. 2612–2618, 2018.
S. Hansun, “Peramalan Data IHSG Menggunakan Metode Backpropagation,” J. Ultim., vol. 5, no. 1, pp. 26–30, 2013.
N. Khesya, “Mengenal Flowchart Dan Pseudocode Dalam Algoritma Dan Pemrograman,” Preprints, vol. 1, pp. 1–15, 2021.
A. S. Rachman, I. Cholissodin, and M. A. Fauzi, “Peramalan Produksi Gula Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation Pada PG Candi Baru Sidoarjo Adi,” Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 4, pp. 1683–1689, 2018.
F. S. D. Arianto and N. P, “Prediksi Kasus Covid-19 Di Indonesia Menggunakan Metode Backpropagation Dan Fuzzy Tsukamoto,” J. Teknol. Inf., vol. 4, no. 1, pp. 120–127, 2020.
D. H. Tanjung, “Jaringan Saraf Tiruan dengan Backpropagation untuk Memprediksi Penyakit Asma,” Creat. Inf. Technol. J., vol. 2, no. 1, p. 28, 2015.
D. Rahayu, R. C. Wihandika, and R. S. Perdana, “Implementasi Metode Backpropagation Untuk Klasifikasi Kenaikan Harga Minyak Kelapa Sawit,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 4 e-ISSN: 2548-964X, pp. 1547–1552, 2018.
S. Kosasi, “Penerapan metode jaringan saraf tiruan backpropagation untuk memprediksi nilai ujian sekolah,” J. Teknol., no. June 2014, pp. 20–28, 2014.
M. Thoriq, “Peramalan Jumlah Permintaan Produksi Menggunakan Jaringan Saraf Tiruan Algoritma Backpropagation,” J. Inf. dan Teknol., vol. 1, no. 2, pp. 27–32, 2022.
I. K. Hadihardaja and S. Sutikno, “Pemodelan Curah Hujan-Limpasan Menggunakan Artificial Neural Network (ANN) dengan Metode Backpropagation,” J. Tek. Sipil, vol. 12, no. 4, p. 250, 2010.
M. A. Raharja and I. M. T. G. Astra, “Prediksi Ketinggian Gelombang Laut Menggunakan Metode Backpropagation Pada Pantai Lebih Gianyar,” J. Ilmu Komput., vol. 11, no. 1, p. 19, 2018.
Copyright (c) 2022 Dwi Findi Auliasari, Gita Febrianti, Agus Perdana Windarto, Dedy Hartama
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
