Sales Prediction on the Diamond Cell Counter Using Autoregresive Integrated Moving Average (ARIMA) Method

  • Kris Rahayu Universitas Mercu Buana Yogyakarta, Indonesia
  • Putri Taqwa Prasetyaningrum Universitas Mercu Buana Yogyakarta, Indonesia
Keywords: ARIMA, forecasting, sales, internet vouchers.

Abstract

Diamond Cell is a specialized retailer that offers a diverse range of smartphone accessories, electronic credits, and internet vouchers from different providers, each with varying active periods. However, the uncertainty surrounding internet voucher sales transactions often leaves counter owners hesitant to increase their stock due to the short active period of the vouchers. This leads to frequent customer dissatisfaction as the internet vouchers run out, resulting in lost sales opportunities. To address this issue, this study aimed to predict voucher sales for the upcoming month to serve as a reference for calculating the stock of voucher supply. The Auto-regressive Integrated Moving Average (ARIMA) method was used based on voucher sales data from November 2022 to January 2023. Out of the three tentative models obtained, only one proved suitable for use. The best ARIMA model was the (2,1,0) model, with a MAD value of 29.65, an MSE value of 2409.95, and a MAPE value of 23.3%. Based on the February voucher sales, the stock level can remain the same as the previous period since the sales were stable. The findings of this study can help Diamond Cell counter owners make more informed decisions about stocking internet vouchers, resulting in better customer satisfaction and reduced likelihood of losses.

Downloads

Download data is not yet available.

References

S. Wardah, “Kemasan Bungkus ( Studi Kasus : Home Industry Arwana Food Tembilahan ),” 2016.

S. E. Rumagit and A. SN, “Prediksi Pemakaian Listrik Kelompok Tarif Menggunakan Jaringan Syaraf Tiruan dan ARIMA,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 7, no. 2, p. 189, 2013, doi: 10.22146/ijccs.3359.

M. H. Hamirsa and R. Rumita, “Usulan Perencanaan Peramalan ( Forecasting ) Dan Safety Stock Persediaan Spare Part Busi Champion Type Ra7yc - 2 ( Ev - 01 / Ew - 01 / 2 ) Menggunakan Metode Time Series Pada Pt Triangle Motorindo Semarang,” vol. 2, 1927.

W. S. Rahayu, P. T. Juwono, and W. Soetopo, “Analisis Prediksi Debit Sungai Amprong Dengan Model Arima (Autoregressive Integrated Moving Average) Sebagai Dasar Penyusunan Pola Tata Tanam,” J. Tek. Pengair., vol. 10, no. 2, pp. 110–119, 2019, doi: 10.21776/ub.pengairan.2019.010.02.04.

S. P. Elvani, A. R. Utary, and R. Yudaruddin, “Peramalan jumlah produksi tanaman kelapa sawit dengan menggunakan metode arima (autoregressive integrated moving average)," Jurnal Manajemen, vol. 8, no. 1, pp. 95–112, 2016.

M. Personal and R. Archive, “Munich Personal RePEc Archive Forecasting irish inflation using ARIMA models,” no. 11359, 2008.

A. Lusiani, “Pemodelan Autoregressive Integrated Moving Average ( Arima ) Curah Hujan Di Kota Bandung Modelling Of Autoregressive Integrated Moving Average ( Arima ) Rainfall In Bandung,” pp. 9–25, 2010.

A. F. Kurniawan, S. F. Pane, and R. M. Awangga, “Prediksi Jumlah Penjualan Rumah di Bojongsoang ditengah Pandemi Covid-19 dengan Metode ARIMA,” J. Media Inform. Budidarma, vol. 5, no. 4, p. 1479, 2021, doi: 10.30865/mib.v5i4.3121.

E. Munarsih et al., “Peramalan Jumlah Pengangguran di Provinsi Sumatera Selatan dengan Metode Autoregressive Integreted Moving Average (ARIMA),” J. Penelit. Sains, vol. 19, pp. 1–5, 2017.

M. S. Pradana, D. Rahmalia, and E. D. A. Prahastini, “Peramalan Nilai Tukar Petani Kabupaten Lamongan dengan Arima,” J. Mat., vol. 10, no. 2, p. 91, 2020, doi: 10.24843/jmat.2020.v10.i02.p126.

L. Farosanti, H. Mubarok, and Indrianto, “Analisa Peramalan Penjualan Alat Kesehatan dan Laboratorium di PT. Tristania Global Indonesia Menggunakan Metode ARIMA,” JIMP J. Inform. Merdeka Pasuruan, vol. 7, no. 2, pp. 14–18, 2022, [Online]. Available: http://dx.doi.org/10.37438/jimp.v7i1.428.

I. B. B. Mahayana, I. Mulyadi, and S. Soraya, “Peramalan Penjualan Helm dengan Metode ARIMA (Studi Kasus Bagus Store),” Inferensi, vol. 5, no. 1, p. 45, 2022, doi: 10.12962/j27213862.v5i1.12469.

Published
2023-03-07
Abstract views: 1605 times
Download PDF: 944 times
How to Cite
Rahayu, K., & Prasetyaningrum, P. (2023). Sales Prediction on the Diamond Cell Counter Using Autoregresive Integrated Moving Average (ARIMA) Method. Journal of Information Systems and Informatics, 5(1), 271-284. https://doi.org/10.51519/journalisi.v5i1.450