Analisis Perbandingan Metode Single Exponential Smoothing dan Single Moving Average dalam Peramalan Pemesanan

Authors

  • Ni Putu Linda Santiari Institut Teknologi dan Bisnis STIKOM Bali
  • I Gede Surya Rahayuda Institut Teknologi dan Bisnis STIKOM Bali

DOI:

https://doi.org/10.32493/informatika.v6i2.10135

Keywords:

Single Moving Average, Forcecast, Single Exponential Smoothing

Abstract

ACK Fried Chicken (Ayam Crispy Kriuk) is a local fast food business that provides food and beverages. The competition for chicken franchises in Bali and the number of orders made by customers through online applications, namely the Gofood and Grabfood applications on ACK Fried Chicken, has resulted in the importance of implementing the SCM strategy in ACK Fried Chicken, to maintain customer trust and satisfaction. By providing an overview to make estimates in the present for future production, it is necessary to forecast demand which is carried out using the forecasting method. The forecasting method used is the time series method, namely the Single Exponential Smoothing method and the Single Moving Average method. Forecasting using the time series method, namely SES and SMA, the forecasting model is obtained from the actual data used in this case study, the forecasting model using the SMA method is declared very good because it has a MAPE value <10% while the SES method is declared good because it has a MAPE value. = 11% (10-20%). The SMA method has an error rate that is more sophisticated than the SES method. For forecasting order requests in this study, the SMA method is better used because it has the smallest error rate in forecasting.

References

Aditya, F., Devianto, D., & Maiyastri, M. (2019). Peramalan Harga Emas Indonesia Menggunakan Metode Fuzzy Time Series Klasik. Jurnal Matematika UNAND, 8(2), 45–52.

Aflah, M. N., & Rahmani, E. F. (2018). Analisa Kebutuhan (Need Analysis) Mata Kuliah Bahasa Inggris Untuk Mahasiswa Kejuruan. Jurnal Pendidikan Bahasa, 7(1), 77–89.

Al Ihsan, N. H. A. S., Dzakiyah, H. H., & Liantoni, F. (2020). Perbandingan Metode Single Exponential Smoothing dan Metode Holt untuk Prediksi Kasus COVID-19 di Indonesia. Ultimatics: Jurnal Teknik Informatika, 12(2), 89–94.

Alfatiyah, R. (2017). Perencanaan Produksi Minyak Telon Ukuran 100 Ml Dengan Metode Time Series Di PT. Merpati Mahardika. Teknik Industri, 9(25).

Anisya, A., & Wandyra, Y. (2016). Rekayasa Perangkat Lunak Pengendalian Inventori Menggunakan Metode SMA (Single Moving Average) Berbasis AJAX (Asynchronous Javascript and XML)(Studi Kasus: PTP Nusantara VI (Persero) Unit Usaha Kayu Aro). Jurnal TeknoIf, 4(2).

Bastuti, S., & Teddy, T. (2017). Analisis Persediaan Barang Dengan Metode Time Series Dan Sistem Distribution Requirement Planning Untuk Mengoptimalkan Permintaan Barang Di Pt. Asri Mandiri Gemilang. PROCEEDINGS UNIVERSITAS PAMULANG, 2(1).

Elfajar, A. B., Setiawan, B. D., & Dewi, C. (2017). Peramalan Jumlah Kunjungan Wisatawan Kota Batu Menggunakan Metode Time Invariant Fuzzy Time Series. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer E-ISSN, 2548, 964X.

Hadna, N. M. S., Santosa, P. I., & Winarno, W. W. (2016). Studi literatur tentang perbandingan metode untuk proses analisis sentimen di Twitter. Semin. Nas. Teknol. Inf. Dan Komun, 2016, 57–64.

Junianto, M. B. S. (2017). Fuzzy Inference System Mamdani dan the Mean Absolute Percentage Error (MAPE) untuk Prediksi Permintaan Dompet Pulsa pada XL Axiata Depok. Jurnal Informatika Universitas Pamulang, 2(2), 97–102.

Khair, U., Fahmi, H., Al Hakim, S., & Rahim, R. (2017). Forecasting error calculation with mean absolute deviation and mean absolute percentage error. Journal of Physics: Conference Series, 930(1), 12002. IOP Publishing.

Kuswara, J., Suryadi, D., & Tjahjamooniarsih, N. (2020). Peramalan Penyediaan Jaringan Telelkomunikasi Bts Bersama Menggunakan Pola Time Series Pada Daerah Kabupaten Kubu Raya. Jurnal Teknik Elektro Universitas Tanjungpura, 1(1).

Landia, B. (2020). Peramalan Jumlah Mahasiswa Baru Dengan Exponential Smoothing dan Moving Average. Jurnal Ilmiah Intech: Information Technology Journal of UMUS, 2(01), 71–78.

Maricar, M. A. (2019). Analisa Perbandingan Nilai Akurasi Moving Average dan Exponential Smoothing untuk Sistem Peramalan Pendapatan pada Perusahaan XYZ. Jurnal Sistem Dan Informatika (JSI), 13(2), 36–45.

naufal Hay’s, R., & Adrean, R. (2017). Sistem Informasi Inventory Berdasarkan Prediksi Data Penjualan Barang Menggunakan Metode Single Moving Average Pada CV. Agung Youanda. ProTekInfo (Pengembangan Riset Dan Observasi Teknik Informatika), 4, 29–33.

Nguyen, V. A., Shafieezadeh-Abadeh, S., Kuhn, D., & Esfahani, P. M. (2019). Bridging Bayesian and minimax mean square error estimation via Wasserstein distributionally robust optimization. ArXiv Preprint ArXiv:1911.03539.

Putra, M. S., & Solikin, I. (2019). Aplikasi Peramalan Stok Alat Tulis Kantor (Atk) Menggunakan Metode Single Moving Average (Sma) Pada Pt. Sinar Kencana Multi Lestari. CESS (Journal of Computer Engineering, System and Science), 4(2), 236–241.

Rachmat, R., & Suhartono, S. (2020). Comparative Analysis of Single Exponential Smoothing and Holt’s Method for Quality of Hospital Services Forecasting in General Hospital. Bulletin of Computer Science and Electrical Engineering, 1(2), 80–86.

Santiari, N. P. L., & Rahayuda, I. G. S. (2020). Penerapan Metode Exponential Smoothing Untuk Peramalan Penjualan Pada Toko Gitar. JOINTECS (Journal of Information Technology and Computer Science), 5(3), 203–210.

Sinaga, B., Sagala, J. R., & Sijabat, S. (2016). Perancangan Aplikasi Peramalan Penjualan Handphone Dengan Metode Triple Exponential Smoothing. Jurnal Mantik Penusa, 20(1).

Solikin, I. (2016). Sistem Informasi Peramalan Pembelian Stok Barang menggunakan Metode Single Moving Average (SMA). Jurnal Cendikia, 14(1 April), 18–22.

Suhardi, S., Widyastuti, T., Bisri, B., & Prabowo, W. (2019). Forecasting Analysis Of New Students Acceptance Using Time Series Forecasting Method. Jurnal Akrab Juara, 4(5), 10–23.

Wibawa, S., & Sokibi, P. (n.d.). Sistem Forecasting Keuangan Inventaris Sarana dan Prasarana dengan Metode Naive Approach pada Universitas CIC. Jurnal Informatika Universitas Pamulang, 5(4), 572–577.

Yang, E., Park, H. W., Choi, Y. H., Kim, J., Munkhdalai, L., Musa, I., & Ryu, K. H. (2018). A simulation-based study on the comparison of statistical and time series forecasting methods for early detection of infectious disease outbreaks. International Journal of Environmental Research and Public Health, 15(5), 966.

Zhang, N., Shen, S.-L., Zhou, A., & Xu, Y.-S. (2019). Investigation on performance of neural networks using quadratic relative error cost function. IEEE Access, 7, 106642–106652.

Zhang, Y., Han, J., Pan, G., Xu, Y., & Wang, F. (2021). A multi-stage predicting methodology based on data decomposition and error correction for ultra-short-term wind energy prediction. Journal of Cleaner Production, 292, 125981

Downloads

Published

2021-06-30