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Penerapan Metode Autoregressive (AR) dalam Memprediksi Tren Pertumbuhan Start Up di Kota Medan

Author


Mario Syahputra(1Mail), Rahmat Hutapea(2), Agung Hidayat Nuryadi(3), Rio Anggara Panjaitan(4), Rina Widya Sari(5),
(1) Universitas Islam Negeri Sumatera Utara, Indonesia
(2) Universitas Islam Negeri Sumatera Utara, Indonesia
(3) Universitas Islam Negeri Sumatera Utara, Indonesia
(4) Universitas Islam Negeri Sumatera Utara, Indonesia
(5) Universitas Islam Negeri Sumatera Utara, Indonesia

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Available online: 2026-03-24  |  Published : 2026-03-24
Copyright (c) 2026 Mario Syahputra
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Abstract


The rapid growth of startups in Medan City highlights the need for data-driven approaches to understanding digital entrepreneurship dynamics. This study aims to apply the Autoregressive (AR) model to predict startup growth trends in Medan using historical data from 2016 to 2024. The method employed is time series analysis with a first-order AR(1) model, utilizing secondary data from the Investment and Licensing Agency of North Sumatra Province. The findings indicate a significant increase in startup numbers since 2022, with projections suggesting continued growth to 127 startups by 2030. The AR model proves to offer a simple yet accurate forecasting framework, making it a valuable tool for strategic planning. These results have important implications for policymakers, investors, and entrepreneurs in designing sustainable startup development strategies in urban areas.


Keywords


Autoregressive; Deret Waktu; Prediksi; Startup

References


Deviana, S., Nusyirwan, Azis, D., & Ferdias, P. (2021). Analisis Model Autoregressive Integrated Moving Average Data Deret Waktu Dengan Metode Momen Sebagai Estimasi Parameter. Jurnal Siger Matematika, 02(02), 57–67.

Ferdiansyah, O., & Permana, E. (2022). Peran start up untuk pengembangan kewirausahaan mahasiswa pasca pandemi covid 19 di Indonesia. Jurnal Riset Pendidikan Ekonomi, 7(2), 151–159. https://doi.org/10.21067/jrpe.v7i2.6828.

Kawegian, M. G. (2024). Analisa Tren Tipe Bisnis Startup Digital 2024. Jurnal EMBA, 12(2), 69–74.

Lestari, V. A., Ananta, A. Y., & Basudewa, P. (2023). Sistem Informasi Prediksi Persediaan Obat Di Apotek Naylun Farma Menggunakan Holt-Winters. Jurnal Informatika Polinema, 9(2), 229–236. https://doi.org/10.33795/jip.v9i2.1289

Lestari, W. S. (2024). Optimasi Model Prediksi Kesuksesan Startup Menggunakan StandartScaler Tranform. Seminar Nasional Teknologi & Sains, 3(1), 76–81. https://doi.org/10.29407/stains.v3i1.4340

Putri, R. C., & Junaedi, L. (2022). Penerapan Metode Peramalan Autoregressive Integrated Moving Average Pada Sistem Informasi Pengendalian Persedian Bahan Baku ( Studi Kasus : Toko Kue Onde-Onde Surabaya ). Jurnal Ilmu Komputer Dan Bisnis (JIKB), XIII(1), 164–173.

Sarwo, & Hermawan. (2019). Prediksi Penerimaan Siswa Baru Pada Madrasah Aliyah Assayafi’Iyah 02 Menggunakan Metode Time Series. Petir, 9(2), 151–156. https://doi.org/10.33322/petir.v9i2.182

Savada, A. G. A., Nama, G. F., Yulianti, T., & Mardiana, M. (2025). Peramalan Data Ekonomi Menggunakan Model Hybrid Vector Autoregressive-Long Short Term Memory. Jurnal Teknik Informatika Dan Sistem Informasi, 11(1), 91–104. https://doi.org/10.28932/jutisi.v11i1.10066

Yuliawanti, F. D., Novitasari, D. C. R., Widodo, N., Hamid, A., & Utami, W. D. (2021). Penerapan Metode Autoregressive Integrated Moving Average (Arima) Untuk Prediksi Bilangan Sunspot. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 15(3), 555–564. https://doi.org/10.30598/barekengvol15iss3pp555-564

Yusian, D. R., & Aulia, N. (2021). Start Up Digital Business: Mengenal Peluang dan Tips Bisnis Bagi Para Pemula. Jurnal Pengabdian Masyarakat INOTEC UUI, 3(2), 34–39.


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