Implementation of Artificial Neural Network on Sales Forecasting Application
Article Sidebar
Published:
Dec 31, 2022
Keywords:
Forecasting, Artificial Neural Networks, Backpropagation
Section
Articles
Statistics Article
Article View : 281 Times
Main Article Content
Abstract
Sales forecasting is an effort to fulfill customer demands. The existence of a sales forecast, can help trade business owners in carrying out stock management to deal with customer demands in the future. Data owned in the past is used in predicting and estimating a condition in the future. Quantitative data used as a reference in the forecasting process can be time series data based on a certain period containing the number of sales. Artificial Neural Networks (ANN) are one of the human efforts to model the way the human nervous system functions in carrying out certain tasks. This modeling is based on the ability of the human brain to organize brain cells called neurons. Neurons are information processing units that are the basis of artificial neural network operations. ANN can be used to solve forecasting problems based on continuous data such as time series data from a sale based on a certain period. The research stages that will be carried out consist of analyzing needs, training the model, testing the model, forecasting sales.
Downloads
Download data is not yet available.
Article Details
How to Cite
Atmaja, K. J., Pascima, I. B. N. ., Putra Asana, I. M. D., & Sudipa, I. G. I. (2022). Implementation of Artificial Neural Network on Sales Forecasting Application. Journal of Intelligent Decision Support System (IDSS), 5(4), 124-131. Retrieved from https://idss.iocspublisher.org/index.php/jidss/article/view/111
References
Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938.
Anitescu, C., Atroshchenko, E., Alajlan, N., & Rabczuk, T. (2019). Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials and Continua, 59(1), 345–359.
Aprilianto, H., Kumalaningsih, S., & Santoso, I. (2018). Penerapan Jaringan Syaraf Tiruan Untuk Peramalan Penjualan Dalam Mendukung Pengembangan Agroindustri Coklat di Kabupaten Blitar. Habitat, 29(3), 129–137. https://doi.org/10.21776/ub.habitat.2018.029.3.16
Asana, I. M. D. P., Kurniadi, I. M. D., Dwipayani, S. A., & Atmaja, K. J. (2022). Sales Forecasting Applications For Retail Companies Using Double Exponential Smoothing And Golden Section Methods. Jurnal Mantik, 6(2), 1603–1611.
Asana, I. M. D. P., Sudipa, I. G. I., Mayun, A. A. T. W., Meinarni, N. P. S., & Waas, D. V. (2022). Aplikasi Data Mining Asosiasi Barang Menggunakan Algoritma Apriori-TID. INFORMAL: Informatics Journal, 7(1), 38–45.
Astuti, E. S., Arhandi, P. P., & Lestari, P. (2017). PENGEMBANGAN SISTEM INFORMASI PERAMALAN PENJUALAN GUNA MENENTUKAN KEBUTUHAN BAHAN BAKU PUPUK MENGGUNAKAN METODE TRIPLE EXPONENTIAL SMOOTHING. 35–42.
Atika, P. D., Informatika, T., Bhayangkara, U., & Raya, J. (2019). Implementasi Jaringan Syaraf Tiruan Metode Backpropagation untuk Prediksi Penjualan Mobil Bekas. 18(2), 107–112. https://doi.org/10.36054/jict-ikmi.v18i2.70
Atmaja, K. J., & Anandita, I. B. G. (2021). Sales forecasting system using single exponential smoothing. Jurnal Mantik, 4(4), 2552–2557.
Baktiar, C., Wibowo, A., & Adipranata, R. (2015). Pembuatan Sistem Peramalan Penjualan Dengan Metode Weighted Moving Average dan Double Exponential Smoothing Pada UD Y. 1–5.
Chamidah, N., Wiharto, & Salamah, U. (2012). Pengaruh Normalisasi Data pada Jaringan Syaraf Tiruan Backpropagasi Gradient Descent Adaptive Gain ( BPGDAG ) untuk Klasifikasi. 1(1), 28–33.
Cynthia, E. P., & Ismanto, E. (2017). Memprediksi Ketersediaan Komoditi Pangan Provinsi Riau. Jurnal Teknologi Dan Sistem Informasi Univrab, 2(2), 196–209.
Fachrurrazi, S. (2015). Peramalan Penjualan Obat Menggunakan Metode Single Exponential Smoothing pada Toko Obat Bintang Geurugok. Techsi, 7(1), 19–30. https://doi.org/10.29103/techsi.v7i1.178
Hasan, N. F., Kusrini, K., & Fatta, H. Al. (2019). Analisis Arsitektur Jaringan Syaraf Tiruan Untuk Peramalan Penjualan Air Minum Dalam Kemasan. Jurnal Rekayasa Teknologi Informasi (JURTI), 3(1), 1–10.
Lillicrap, T. P., & Santoro, A. (2019). Backpropagation through time and the brain. Current Opinion in Neurobiology, 55, 82–89.
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020). Backpropagation and the brain. Nature Reviews Neuroscience, 21(6), 335–346.
Ogasawara, E., Martinez, L. C., De Oliveira, D., Zimbrão, G., Pappa, G. L., & Mattoso, M. (2010). Adaptive Normalization: A novel data normalization approach for non-stationary time series. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN.2010.5596746
Riyadi, S. (2015). APLIKASI PERAMALAN PENJUALAN OBAT MENGGUNAKAN METODE PEMULUSAN ( STUDI KASUS : INSTALASI FARMASI RSUD DR MURJANI ). 1, 6–8.
Satria, W. (2020). Jaringan Syaraf Tiruan Backpropagation Untuk Peramalan Penjualan Produk (Studi Kasus Di Metro Electronic Dan Furniture). Djtechno: Jurnal Teknologi Informasi, 1(1), 14–19.
Suyanto. (2014). Artificial Intelligence: Searching, Reasoning, Planning, dan Learning. In Informatika, Bandung, Indonesia.
Trimulya, A., Sfaifurrahman, & Setyaningsih, F. A. (2015). Implementasi jaringan syaraf tiruan metode backpropagation untuk memprediksi harga saham 1,3. Coding, 03(2), 66–75.
Walczak, S. (2019). Artificial neural networks. In Advanced methodologies and technologies in artificial intelligence, computer simulation, and human-computer interaction (pp. 40–53). IGI global.
Zai, D. A. B., Marsono, M., & Halim, J. (2021). Implementasi Jaringan Syaraf Tiruan Untuk Memprediksi Jumlah Penjualan Rumah Dengan Menggunakan Metode Backpropagation (Studi Kasus PT. Putra Pratama Properti). Jurnal Cyber Tech, 4(2).
Anitescu, C., Atroshchenko, E., Alajlan, N., & Rabczuk, T. (2019). Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials and Continua, 59(1), 345–359.
Aprilianto, H., Kumalaningsih, S., & Santoso, I. (2018). Penerapan Jaringan Syaraf Tiruan Untuk Peramalan Penjualan Dalam Mendukung Pengembangan Agroindustri Coklat di Kabupaten Blitar. Habitat, 29(3), 129–137. https://doi.org/10.21776/ub.habitat.2018.029.3.16
Asana, I. M. D. P., Kurniadi, I. M. D., Dwipayani, S. A., & Atmaja, K. J. (2022). Sales Forecasting Applications For Retail Companies Using Double Exponential Smoothing And Golden Section Methods. Jurnal Mantik, 6(2), 1603–1611.
Asana, I. M. D. P., Sudipa, I. G. I., Mayun, A. A. T. W., Meinarni, N. P. S., & Waas, D. V. (2022). Aplikasi Data Mining Asosiasi Barang Menggunakan Algoritma Apriori-TID. INFORMAL: Informatics Journal, 7(1), 38–45.
Astuti, E. S., Arhandi, P. P., & Lestari, P. (2017). PENGEMBANGAN SISTEM INFORMASI PERAMALAN PENJUALAN GUNA MENENTUKAN KEBUTUHAN BAHAN BAKU PUPUK MENGGUNAKAN METODE TRIPLE EXPONENTIAL SMOOTHING. 35–42.
Atika, P. D., Informatika, T., Bhayangkara, U., & Raya, J. (2019). Implementasi Jaringan Syaraf Tiruan Metode Backpropagation untuk Prediksi Penjualan Mobil Bekas. 18(2), 107–112. https://doi.org/10.36054/jict-ikmi.v18i2.70
Atmaja, K. J., & Anandita, I. B. G. (2021). Sales forecasting system using single exponential smoothing. Jurnal Mantik, 4(4), 2552–2557.
Baktiar, C., Wibowo, A., & Adipranata, R. (2015). Pembuatan Sistem Peramalan Penjualan Dengan Metode Weighted Moving Average dan Double Exponential Smoothing Pada UD Y. 1–5.
Chamidah, N., Wiharto, & Salamah, U. (2012). Pengaruh Normalisasi Data pada Jaringan Syaraf Tiruan Backpropagasi Gradient Descent Adaptive Gain ( BPGDAG ) untuk Klasifikasi. 1(1), 28–33.
Cynthia, E. P., & Ismanto, E. (2017). Memprediksi Ketersediaan Komoditi Pangan Provinsi Riau. Jurnal Teknologi Dan Sistem Informasi Univrab, 2(2), 196–209.
Fachrurrazi, S. (2015). Peramalan Penjualan Obat Menggunakan Metode Single Exponential Smoothing pada Toko Obat Bintang Geurugok. Techsi, 7(1), 19–30. https://doi.org/10.29103/techsi.v7i1.178
Hasan, N. F., Kusrini, K., & Fatta, H. Al. (2019). Analisis Arsitektur Jaringan Syaraf Tiruan Untuk Peramalan Penjualan Air Minum Dalam Kemasan. Jurnal Rekayasa Teknologi Informasi (JURTI), 3(1), 1–10.
Lillicrap, T. P., & Santoro, A. (2019). Backpropagation through time and the brain. Current Opinion in Neurobiology, 55, 82–89.
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020). Backpropagation and the brain. Nature Reviews Neuroscience, 21(6), 335–346.
Ogasawara, E., Martinez, L. C., De Oliveira, D., Zimbrão, G., Pappa, G. L., & Mattoso, M. (2010). Adaptive Normalization: A novel data normalization approach for non-stationary time series. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN.2010.5596746
Riyadi, S. (2015). APLIKASI PERAMALAN PENJUALAN OBAT MENGGUNAKAN METODE PEMULUSAN ( STUDI KASUS : INSTALASI FARMASI RSUD DR MURJANI ). 1, 6–8.
Satria, W. (2020). Jaringan Syaraf Tiruan Backpropagation Untuk Peramalan Penjualan Produk (Studi Kasus Di Metro Electronic Dan Furniture). Djtechno: Jurnal Teknologi Informasi, 1(1), 14–19.
Suyanto. (2014). Artificial Intelligence: Searching, Reasoning, Planning, dan Learning. In Informatika, Bandung, Indonesia.
Trimulya, A., Sfaifurrahman, & Setyaningsih, F. A. (2015). Implementasi jaringan syaraf tiruan metode backpropagation untuk memprediksi harga saham 1,3. Coding, 03(2), 66–75.
Walczak, S. (2019). Artificial neural networks. In Advanced methodologies and technologies in artificial intelligence, computer simulation, and human-computer interaction (pp. 40–53). IGI global.
Zai, D. A. B., Marsono, M., & Halim, J. (2021). Implementasi Jaringan Syaraf Tiruan Untuk Memprediksi Jumlah Penjualan Rumah Dengan Menggunakan Metode Backpropagation (Studi Kasus PT. Putra Pratama Properti). Jurnal Cyber Tech, 4(2).