Multivariate Time Series Forecasting pada Penjualan Barang Retail dengan Recurrent Neural Network

Robertus Bagaskara Radite Putra; Hendry Hendry

doi:10.35314/isi.v7i1.2398

Multivariate Time Series Forecasting pada Penjualan Barang Retail dengan Recurrent Neural Network

Robertus Bagaskara Radite Putra^(1*), Hendry Hendry⁽²⁾

(1)
(2) Satya Wacana Christian University
(*) Corresponding Author

Abstract

Intisari–Pasar ritel di Indonesia semakin berkembang seiring bertambahnya penduduk dan daya beli. Peluang ini harus dimanfaatkan, namun dalam bisnis ritel, kadangkala terjadi keadaan Out of Stock maupun over stock di dalam toko. Untuk mengatasi hal tersebut, kita bisa mengatasinya dengan melakukan peramalan atau prediksi penjualan yang akan terjadi di masa mendatang. Ada beberapa macam metode untuk melakukan peramalan, namun secara umum terbagi menjadi 2 jenis yaitu metode statistika dan juga computational intelligence. Penelitian ini mencoba untuk melakukan prediksi penjualan barang retail perhari menggunakan metode Recurrent Neural Network (RNN) sebagai bagian dari metode computational intelligence. Dari penelitian ini kita bisa dapatkan hasil bahwa dalam kasus prediksi penjualan ritel, performa akurasi RNN lebih baik dari metode statistika

Article metrics

Abstract views : 1330 |

Full Text:

PDF (Bahasa Indonesia)

References

M. Yanto, E. Praja Wiyata Mandala, and D. Eka Putri, “Peramalan Penjualan Pada Toko Retail Menggunakan Algoritma Backpropagation Neural Network,” MEDIA INFORMATIKA BUDIDARMA, vol. 2, no. 3, pp. 110–117, 2018.

“Indonesia Retail Market to grow at a CAGR of 4% | CT Corp. and PT Erajaya Swasembada Tbk emerge as Some Key Contributors to growth | 17000 + Technavio Reports.” https://www.prnewswire.com/news-releases/indonesia-retail-market-to-grow-at-a-cagr-of-4--ct-corp-and-pt-erajaya-swasembada-tbk-emerge-as-some-key-contributors-to-growth--17000--technavio-reports-301399662.html (accessed Nov. 10, 2021).

S. Ren, H. L. Chan, and T. Siqin, “Demand forecasting in retail operations for fashionable products: methods, practices, and real case study,” Annals of Operations Research, vol. 291, no. 1–2, pp. 761–777, Aug. 2020, doi: 0.1007/s10479-019-03148-8.

T. Ma, C. Antoniou, and T. Toledo, “Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast,” Transportation Research Part C: Emerging Technologies, vol. 111, pp. 352–372, Feb. 2020, doi: 0.1016/j.trc.2019.12.022.

H. Abbasimehr, M. Shabani, and M. Yousefi, “An optimized model using LSTM network for demand forecasting,” Computers and Industrial Engineering, vol. 143, May 2020, doi: 10.1016/j.cie.2020.106435.

A. Cecaj, M. Lippi, M. Mamei, and F. Zambonelli, “Comparing Deep Learning and Statistical Methods in Forecasting Crowd Distribution from Aggregated Mobile Phone Data,” Applied Sciences, vol. 10, no. 18, p. 6580, Sep. 2020, doi: 10.3390/app10186580.

“CS 230 - Recurrent Neural Networks Cheatsheet.” https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks (accessed Nov. 16, 2021).

L. Wiranda and M. Sadikin, “PENERAPAN LONG SHORT TERM MEMORY PADA DATA TIME SERIES UNTUK MEMPREDIKSI PENJUALAN PRODUK PT. METISKA FARMA,” Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha, vol. 8, no. 3, pp. 184–196, 2019, [Online]. Available: https://ejournal.undiksha.ac.id/index.php/janapati/article/view/19139

J. Huber and H. Stuckenschmidt, “Daily retail demand forecasting using machine learning with emphasis on calendric special days,” International Journal of Forecasting, vol. 36, no. 4, pp. 1420–1438, Oct. 2020, doi: 10.1016/j.ijforecast.2020.02.005.

U. Ugurlu, I. Oksuz, and O. Tas, “Electricity Price Forecasting Using Recurrent Neural Networks,” Energies (Basel), vol. 11, no. 5, p. 1255, May 2018, doi: 10.3390/en11051255.

S. Zahara and Sugianto, “Peramalan Data Indeks Harga Konsumen Berbasis Time Series Multivariate Menggunakan Deep Learning,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 1, pp. 24–30, Feb. 2021, doi: 10.29207/resti.v5i1.2562.

D. A. Lusia and A. Ambarwati, “PERBANDINGAN PERAMALAN UNIVARIAT DAN MULTIVARIAT ARIMA PADA INDEKS HARGA SAHAM GABUNGAN,” Jurnal Statistika Universitas Muhammadiyah Semarang, vol. 6, no. 2, pp. 78–84, 2018, [Online]. Available: http://finance.yahoo.com/q/hp?s=%5JKS

A. Nurlifa and S. Kusumadewi, “Sistem Peramalan Jumlah Penjualan Menggunakan Metode Moving Average Pada Rumah Jilbab Zaky,” INOVTEK Polbeng - Seri Informatika, vol. 2, no. 1, p. 18, Jun. 2017, doi: 10.35314/isi.v2i1.112.

M. Abdul Dwiyanto Suyudi, E. C. Djamal, A. Maspupah Jurusan Informatika, and F. Sains dan Informatika Universitas Jenderal Achmad Yani Cimahi, “Prediksi Harga Saham menggunakan Metode Recurrent Neural Network,” Seminar Nasional Aplikasi Teknologi Informasi (SNATi), pp. 1907–5022, 2019.

A. Chawla, A. Singh, A. Lamba, N. Gangwani, and U. Soni, “Demand forecasting using artificial neural networks—A case study of american retail corporation,” in Advances in Intelligent Systems and Computing, vol. 697, Springer Verlag, 2019, pp. 79–89. doi: 10.1007/978-981-13-1822-1_8.

R. Khaldi, A. el Afia, R. Chiheb, and R. Faizi, “Artificial Neural Network Based Approach for Blood Demand Forecasting: Fez Transfusion Blood Center Case Study,” in BDCA’17: Proceedings of the 2nd international Conference on Big Data, Cloud and Applications, Mar. 2017, vol. Part F129474. doi: 10.1145/3090354.3090415.

D. Tarkus, S. R. U. A. Sompie, and A. Jacobus, “Implementasi Metode Recurrent Neural Network pada Pengklasifikasian Kualitas Telur Puyuh,” Jurnal Teknik Informatika, vol. 15, no. 2, pp. 137–144, 2020.

S. Alhirmizy and B. Qader, “Multivariate Time Series Forecasting with LSTM for Madrid, Spain pollution,” in 2019 International Conference on Computing and Information Science and Technology and Their Applications (ICCISTA), 2019, pp. 1–5. doi: 10.1109/ICCISTA.2019.8830667.

A. Saxena and T. R. Sukumar, “Predicting bitcoin price using lstm And Compare its predictability with arima model,” International Journal of Pure and Applied Mathematics, vol. 119, no. 17, Feb. 2018, doi: 10.13140/RG.2.2.15847.57766.

A. Hanifa, S. A. Fauzan, M. Hikal, and M. B. Ashfiya, “PERBANDINGAN METODE LSTM DAN GRU (RNN) UNTUK KLASIFIKASI BERITA PALSU BERBAHASA INDONESIA,” Dinamika Rekayasa, vol. 17, no. 1, pp. 33–39, 2021, doi: 10.20884/1.dr.2021.17.1.436.

G. Ayuni and D. Fitrianah, “Penerapan Metode Regresi Linear Untuk Prediksi Penjualan Properti pada PT XYZ,” Jurnal Telematika, vol. 14, no. 2, pp. 79–86, 2020, [Online]. Available: https://journal.ithb.ac.id/telematika/article/view/321

S. J. Taylor and B. Letham, “Forecasting at Scale,” The American Statistician, vol. 72, no. 1, pp. 37–45, Jan. 2018, doi: 10.1080/00031305.2017.1380080.

DOI: https://doi.org/10.35314/isi.v7i1.2398

Refbacks

There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

This Journal has been listed and indexed in :

ISI: Inovtek Polbeng Seri Informatikan is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Editorial Office :
Pusat Penelitian dan Pengabdian kepada Masyarakat
Politeknik Negeri Bengkalis
Jl. Bathin alam, Sungai Alam Bengkalis-Riau 28711
E-mail: jurnalinformatika@polbeng.ac.id
www.polbeng.ac.id

View My Stats

Username
Password
Remember me