PREDIKSI PERTUMBUHAN UMKM DI KOTA BANDUNG MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE

Authors

  • Nurul Sri Hanifah Universitas Sangga Buana
  • Gunawansyah Universitas Sangga Buana

DOI:

https://doi.org/10.32897/infotronik.2024.9.2.3839

Keywords:

MSMEs, Bandung City, ARIMA, Time Series Prediction

Abstract

This study aims to predict the number of micro, small, and medium enterprises (MSMEs) in Bandung City using the ARIMA (AutoRegressive Integrated Moving Average) method. MSMEs play a crucial role in the local economy, particularly in creating jobs and driving economic growth. Predicting the number of MSMEs is important for understanding future trends to support better planning and policy-making. The data used in this study includes the number of MSMEs in Bandung City from 1990 to 2023. The ARIMA method was chosen for its ability to handle time series data with seasonal patterns and trends and for providing accurate predictions based on historical data. The ARIMA (2,1,0) prediction model was selected as the best model, with model evaluation results showing an MAE value of 666.9431 and a MAPE value of 7.55%. The accuracy of this study using the ARIMA (2,1,0) model is 92.45%. Based on the research findings, the AutoRegressive Integrated Moving Average (ARIMA) method can be used to predict the number of Micro, Small, and Medium Enterprises (MSMEs) in Bandung City.

References

“(Umkm) Analisis Peramalan Box Jenkins”.

M. Regresi Linier Berganda And A. Yulianto, “Prediksi Pertumbuhan Jumlah Unit Usaha Mikro Kecil Dan Menengah (Umkm) Menggunakan,” Riset Dan E-Jurnal Manajemen Informatika Komputer, Vol. 6, No. 2, 2022, Doi: 10.33395/Remik.V6i2.11374.

S. Vinatra, A. Bisnis, U. Veteran, And J. Timur, “Peran Usaha Mikro, Kecil, Dan Menengah (Umkm) Dalam Kesejahteraan Perekonomian Negara Dan Masyarakat,” Jurnal Akuntan Publik, Vol. 1, No. 3, Pp. 1–08, 2023, Doi: 10.59581/Jap-Widyakarya.V1i1.832.

T. N. Suharsono, G. Gunawan, R. N. Sukmana, F. A. Rahman, And S. M. Ammarulloh, “Peningkatan Desain Foto Produk Dan Pemasaran Berbasis Digital Kelompok Umkm Makanan Di Kecamatan Cinambo,” Jurnal Abdimas Sang Buana, Vol. 4, No. 2, P. 62, Nov. 2023, Doi: 10.32897/Abdimasusb.V4i2.2673.

Istu And Gunawansyah, “Sistem Pendukung Keputusan Kelayakan Penerima Bantuan Umkm Dengan Metode Simple Multi Attribute Rating Teachnique (Smart),” Jurnal Ilmiah Teknik, Vol. 1, 2022.

H. Afridar And W. Andriani, “Penerapan Metode Arima Untuk Prediksi Harga Komoditi Bawang Merah Di Kota Tegal,” 2022. [Online]. Available: Https://Hargapangan.Id/Tabel-Harga/Pedagang-Besar/Daerahdengan

C. B. Aditya Satrio, W. Darmawan, B. U. Nadia, And N. Hanafiah, “Time Series Analysis And Forecasting Of Coronavirus Disease In Indonesia Using Arima Model And Prophet,” In Procedia Computer Science, Elsevier B.V., 2021, Pp. 524–532. Doi: 10.1016/J.Procs.2021.01.036.

N. Salwa Et Al., “Peramalan Harga Bitcoin Menggunakan Metode Arima (Autoregressive Integrated Moving Average),” 2018.

M. Murat, I. Malinowska, M. Gos, And J. Krzyszczak, “Forecasting Daily Meteorological Time Series Using Arima And Regression Models,” Int Agrophys, Vol. 32, No. 2, Pp. 253–264, Apr. 2018, Doi: 10.1515/Intag-2017-0007.

D. Puspita Anggraeni, D. Rosadi, A. Ashril Rizal, M. Yogyakarta, U. Riau Kepulauan, And S. N. Syaikh Zainuddin Anjani Lombok Timur, “Prediksi Harga Emas Dunia Di Masa Pandemi Covid-19 Menggunakan Model Arima.”

A. A. Suryanto, A. Muqtadir, And S. Artikel, “Penerapan Metode Mean Absolute Error (Mea) Dalam Algoritma Regresi Linear Untuk Prediksi Produksi Padi Info Artikel : Abstrak,” No. 1, P. 11, 2019.

M. A. Rasyidi, “Prediksi Harga Bahan Pokok Nasional Jangka Pendek Menggunakan Arima,” Journal Of Information Systems Engineering And Business Intelligence, Vol. 3, No. 2, P. 107, Oct. 2017, Doi: 10.20473/Jisebi.3.2.107-112.

Published

2024-12-31

How to Cite

Sri Hanifah, N., & Gunawansyah. (2024). PREDIKSI PERTUMBUHAN UMKM DI KOTA BANDUNG MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE. Infotronik : Jurnal Teknologi Informasi Dan Elektronika, 9(2), 59–72. https://doi.org/10.32897/infotronik.2024.9.2.3839