Multivariat Predict Sales Data Using the Recurrent Neural Network (RNN) Method

https://doi.org/10.22146/ijccs.90165

Ni Nengah Dita Ardriani(1), Jamiin Al Yastawil Yastawil(2*), Kadek Nonik Erawati(3), I Gede Made Yudi Antara(4), Gede Agus Santiago(5)

(1) Faculty of Technology and Informatics, Institut Bisnis dan Teknologi Indonesia, Bali
(2) Faculty of Technology and Informatics, Institut Bisnis dan Teknologi Indonesia, Bali
(3) Faculty of Technology and Informatics, Institut Bisnis dan Teknologi Indonesia, Bali
(4) Faculty of Technology and Informatics, Institut Bisnis dan Teknologi Indonesia, Bali
(5) Faculty of Technology and Informatics, Institut Bisnis dan Teknologi Indonesia, Bali
(*) Corresponding Author

Abstract


Sales is an activity or business selling a product or service. In this study, I took a case study on Kaggle. Sales problems at the company cause inventory to be very high or vice versa, causing a loss of sales because there are no items to sell. Inventory that is too high results in increased costs due to existing resources being inefficient. In the opposite condition, it will cause a product vacancy in the market. Using the Recurrent Neural Network (RNN) Algorithm, this study predicts sales. The data used is sales data in 2020 with the parameter Number of sales per day in the last four months. The results obtained through testing several training scenarios and testing the implementation of the algorithm, in this case, is the highest accuracy value of 96.92% in the network architecture of three input neuron layers, three hidden layer neurons, one output, division of training, and test data 70: 30, learning value rate of 0.9 and a maximum of 9000000 epochs

Keywords


Forecasting; Recurrent Neural Network; multivariate prediction

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References

Agus Ambarwari, Adrian, Q. J., dkk. 2020. "Analisis Pengaruh Data Scaling Terhadap Performa Algoritme Machine Learninguntuk Identifikasi Tanaman". JURNAL RESTI, 4.

Alfarisi, S. 2017. "Sistem Prediksi Penjualan Gamis Toko QITAZ Menggunakan Metode Single Exponential Smoothing". JABE (Journal of Applied Business and Economic), 4(1), 80. https://doi.org/10.30998/jabe.v4i1.1908.

Arinal, V., dan Sentosa, E. 2022. "Klasifikasi Tingkat Kesejahteraan RW 006 Kelurahan Kalideres Jakarta Barat dengan Metode K- Nearest Neighbor". Jurnal Pendidikan dan Konseling, 4, 5621– 5638.

Anggraini, F., Suryanto, A., dkk. 2013. "SISTEM TANAM DAN UMUR BIBIT PADA TANAMAN PADI SAWAH (Oryza sativa L.) VARIETAS INPARI 13". Jurnal Produksi Tanaman, 1.

Ardian, H. 2019. "PREDIKSI PRODUKSI KELAPA SAWIT MENGGUNAKAN ELMAN RECURRENT NEURAL NETWORK".

Dwiyanto, M. A., Djamal, C. E., dkk. 2019. "Prediksi Harga Saham menggunakan Metode Recurrent Neural Network". Seminar Nasional Aplikasi Teknologi Informasi (SNATI), 33–38.

Ghozi, A. A., Aprianti, A., dkk. 2022. "Analisis Prediksi Data Kasus Covid-19 di Provinsi Lampung Menggunakan Recurrent Neural Network ( RNN )". Indonesian Journal, 2(1), 25–32.

Hakim, Y. A., Randy Erfa Saputra, S.T., M. T., dkk. 2020. "Sistem Pendukung Keputusan Penyiraman Tanaman Cabai Dengan Memanfaatkan Kecerdasan Buatan Menggunakan Algoritma Lstm Decision Support System of Chili Planting Using Artificial Intelligence Using Lstm Algorithm", 7(2), 4959–4967.

Juanda, R. A., Jondri, dkk. 2018. "Prediksi Harga Bitcoin Dengan Menggunakan Recurrent Neural Network". E-Proceeding of Engineering, 5(2), 3682–3690.

Lubis, N. H., dan Lubis, Y. F. A. 2021. "Implementasi Model Recurrent Neural Network Dalam Melakukan Prediksi Harga Kartu Perdana Internet Dengan Menggunakan Algoritma Long Short Term Memory". Seminar Nasional Teknologi … diambil dari http://prosiding.snastikom.com/index.php/SNASTIKOM2020/arti cle/view/147%0Ahttp://prosiding.snastikom.com/index.php/SNAS TIKOM2020/article/download/147/140.

Mikami, A. 2016. Long Short-Term Memory Recurrent Neural Network Architectures for Generating Music and Japanese LyricsNo Title.

Nugraha, T., Furqon, M. T., dkk. 2017. "Peramalan Permintaan Daging Sapi Nasional Menggunakan Metode Multifactors High Order Fuzzy Time Series Model". Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 1(12), 1764–1770.

Siringoringo, R. 2018. "KLASIFIKASI DATA TIDAK SEIMBANG MENGGUNAKAN ALGORITMA SMOTE DAN k-NEAREST NEIGHBOR". Jurnal ISD, 3(1), 44–49.

Nurhalimah 2017. "Implementasi Metode Arima Untuk Prediksi Penjualan Mobil pada PT.Arista Auto Lestari". Majalah Ilmiah INTI, Volume 12,(Mei 2017), 215–218.

Rizal, A. A., dan Hartati, S. 2017. "Prediksi Kunjungan Wisatawan di Pulau Lombok dengan Menerapkan Recurrent Neural Network dengan Algoritma Training Extended Kalman Filter.". Jurnal Ilmiah ILMU KOMPUTER, X(1), 7–18.

Sanny, L., Sarjono, H., dkk. 2013. "Peramalan Jumlah Siswa / I Sekolah Menengah Atas Swasta Menggunakan Enam Metode", 10, 198– 208.

Silvin 2019. Analisis Sentimen Media Twitter Menggunakan Long Short-Term Memory Recurrent Neural Network. Universitas Multimedia Nusantara.

Suhartanto, E., Cahya, E. N., dkk. 2019. "ANALISA LIMPASAN BERDASARKAN CURAH HUJAN MENGGUNAKAN MODEL ARTIFICAL NEURAL NETWORK (ANN) DI SUB DAS BRANTAS HULU". Jurnal Teknik Pengairan, 10(2), 134–144.

Wanto, A., dan Windarto, A. P. 2017. "Analisis Prediksi Indeks Harga Konsumen Berdasarkan Kelompok Kesehatan Dengan Menggunakan Metode Backpropagation". Jurnal & Penelitian Teknik Informatika.



DOI: https://doi.org/10.22146/ijccs.90165

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