Sistem Peramalan Permintaan Darah dengan Metode Simple Moving Average

Authors

  • Wan Mhd Iqbal Muttaqin Program Studi Sistem Informasi, STMIK Royal Kisaran
  • William Ramdhan Program Studi Sistem Informasi, STMIK Royal Kisaran
  • Wan Mariatul Kifti Program Studi Sistem Informasi, STMIK Royal Kisaran

DOI:

https://doi.org/10.29408/edumatic.v6i2.6326

Keywords:

simple moving average, forecasting, blood demand

Abstract

Simple Moving Average (SMA) is a method used to forecast a state of affairs in the next period. This method is applied to determine the number of blood requests in PMI in the Asahan Regency area. Blood stock is an important factor to support activities in this PMI organization by paying attention to the condition of this month /period and predicting the next period. The purpose of this study is to build a blood demand forecasting system with the SMA method. The model used to build this system is a waterfall with stages of analysis, design, implementation and testing. The data we use for this forecasting is the demand for blood from July 2021 to June 2022. Data analysis used the SMA method to determine the rate of prediction errors in this system, while testing this system used a black box. The results of our products are in the form of a web consisting of the login menu, the main menu, the calculation results. The calculation results using the SMA method in our system are appropriate, and can display the number of blood demand stocks in each period. The results of testing the system using black box show that this system is running properly without any errors, and it is working properly. Therefore, the existence of this system can help pmi to determine the number of requests based on blood type.

References

Afif, A. N., Noviyanto, F., Sunardi, S., Akbar, S. A., & Aribowo, E. (2020). Integrated application for automatic schedule-based distribution and monitoring of irrigation by applying the waterfall model process. Bulletin of Electrical Engineering and Informatics, 9(1), 420–426. https://doi.org/10.11591/eei.v9i1.1368

Afuan, L., Nofiyati, N., & Umayah, N. (2021). Rancang Bangun Sistem Informasi Bank Sampah di Desa Paguyangan. Edumatic: Jurnal Pendidikan Informatika, 5(1), 21–30. https://doi.org/10.29408/edumatic.v5i1.3171

Aini, N., Sinurat, S., & Hutabarat, S. A. (2018). Penerapan Metode Simple Moving Average Untuk Memprediksi Hasil Laba Laundry Karpet Pada CV. Homecare. JURIKOM (Jurnal Riset Komputer), 5(2), 167–175.

Almeida, F. A., Gomes, G. F., Paula, V. R., Correa, J. E., Paiva, A. P., Gomes, J. H. de F., & Turrioni, J. B. (2018). A weighted mean square error approach to the robust optimization of the surface roughness in an AISI 12L14 free-machining steel-turning process. Journal of Mechanical Engineering, 64(3), 147–156. https://doi.org/10.5545/sv-jme.2017.4901

Anggraeni, D. T. (2019). Forecasting Harga Saham Menggunakan Metode Simple Moving Average Dan Web Scrapping. Jurnal Ilmiah Matrik, 21(3), 234–241. https://doi.org/10.33557/jurnalmatrik.v21i3.726

Ardiansah, I., Adiarsa, I. F., Putri, S. H., & Pujianto, T. (2021). Penerapan Analisis Runtun Waktu pada Peramalan Penjualan Produk Organik menggunakan Metode Moving Average dan Exponential Smoothing. Jurnal Teknik Pertanian Lampung, 10(4), 548–559. https://doi.org/10.23960/jtep-l.v10i4.548-559

Chaiyakan, S., & Thipwiwatpotjana, P. (2019). Mean Absolute deviation portfolio frontiers with interval-valued returns. International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, 222–234. https://doi.org/10.1007/978-3-030-14815-7_19

Chen, Z., Song, S., Wei, Z., Fang, J., & Long, J. (2021). Approximating median absolute deviation with bounded error. Proceedings of the VLDB Endowment, 14(11), 2114–2126. https://doi.org/10.14778/3476249.3476266

Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. Geoscientific Model Development, 15(14), 5481–5487. https://doi.org/10.5194/gmd-15-5481-2022

Hudaningsih, N., Utami, S. F., & Jabbar, W. A. A. (2020). Perbandingan Peramalan Penjualan Produk Aknil Pt. Sunthi Sepurimengguanakan Metode Single Moving Average Dan Single Exponential Smooting. Jurnal Informatika Teknologi Dan Sains, 2(1), 15–22. https://doi.org/10.51401/jinteks.v2i1.554

Islam, M. A., Che, H. S., Hasanuzzaman, M., & Rahim, N. A. (2020). Energy demand forecasting. In Energy for sustainable development (pp. 105–123). Elsevier. https://doi.org/10.1016/B978-0-12-814645-3.00005-5

Jardim, R. R. J., Santos, M., Neto, E., da Silva, E., & De Barros, F. (2020). Integration of the waterfall model with ISO/IEC/IEEE 29148: 2018 for the development of military defense system. IEEE Latin America Transactions, 18(12), 2096–2103. https://doi.org/10.1109/TLA.2020.9400437

Karasu, S., Altan, A., Bekiros, S., & Ahmad, W. (2020). A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series. Energy, 212, 118750. https://doi.org/10.1016/j.energy.2020.118750

Kramer, M. (2018). Best practices in systems development lifecycle: An analyses based on the waterfall model. Review of Business & Finance Studies, 9(1), 77–84.

Kumila, A., Sholihah, B., Evizia, E., Safitri, N., & Fitri, S. (2019). Perbandingan metode moving average dan metode naïve dalam peramalan data kemiskinan. JTAM (Jurnal Teori Dan Aplikasi Matematika), 3(1), 65–73. https://doi.org/10.31764/jtam.v3i1.764

Mahmud, M., Gata, W., Putra, J. L., Novitasari, H. B., & Saputra, S. A. (2022). Desain Informasi Cara Bayar Penerimaan Negara menggunakan Pemodelan Finite State Automata. Edumatic: Jurnal Pendidikan Informatika, 6(1), 21–30. https://doi.org/10.29408/edumatic.v6i1.5053

Marita, L. S., & Darwati, I. (2022). Prediksi Persediaan Barang Menggunakan Metode Weighted Moving Average, Exponential Smoothing dan Simple Moving Average. Jurnal Tekno Kompak, 16(1), 56–68. https://doi.org/10.33365/jtk.v16i1.1484

Merkuryeva, G., Valberga, A., & Smirnov, A. (2019). Demand forecasting in pharmaceutical supply chains: A case study. Procedia Computer Science, 149, 3–10. https://doi.org/10.1016/j.procs.2019.01.100

Nicolson, A., & Paliwal, K. K. (2019). Deep learning for minimum mean-square error approaches to speech enhancement. Speech Communication, 111, 44–55. https://doi.org/10.1016/j.specom.2019.06.002

Prajam, S., Wechtaisong, C., & Khan, A. A. (2022). Applying machine learning approaches for network traffic forecasting. Indian Journal of Computer Science and Engineering, 13(2), 324–335. https://doi.org/10.21817/indjcse/2022/v13i2/221302188

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. https://doi.org/10.30591/jpit.v4i2.1373

Reswari, P. A. D., Cahyadi, R., & Wijaya, T. (2021). Sosialisasi dan Pendampingan Penanganan Hematoma Pada Pedonor Darah Di UTD PMI Kota Surabaya Tahun 2019. Journal of Community Engagement in Health, 4(2), 518–525.

Rohan, H. H., Amalia, Y., & Reswari, P. A. D. (2021). Kegiatan Donor Darah Di Fakultas Ilmu Kesehatan Universitas Dr. Soetomo Surabaya Tahun 2018. Journal of Community Engagement in Health, 4(2), 475–480.

Samsulhadi, W., Reswari, P. A. D., & Aziz, S. A. (2021). Sosialisasi Donor Darah di Bank Panin KCP Tunjungan Surabaya Tahun 2018. Journal of Community Engagement in Health, 4(2), 533–538.

Savard, A., & Cyr, S. (2018). A Waterfall Model for Providing Professional Development for Elementary School Teachers: A Pilot Project to Implement a Competency-Based Approach. Global Education Review, 5(3), 165–182.

Shin, J., Yoo, J., & Park, P. (2018). Adaptive regularisation for normalised subband adaptive filter: mean‐square performance analysis approach. IET Signal Processing, 12(9), 1146–1153. https://doi.org/10.1049/iet-spr.2018.5165

Susilawati, S., & Muhathir, M. (2019). Analisis Pengaruh Fungsi Aktivasi, Learning Rate Dan Momentum Dalam Menentukan Mean Square Error (MSE) Pada Jaringan Saraf Restricted Boltzmann Machines (RBM). Journal of Informatics and Telecommunication Engineering, 2(2), 77–91. https://doi.org/10.31289/jite.v2i2.2162

Downloads

Published

2022-12-20