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Submitted
August 8, 2025
Accepted
January 12, 2026
Published
April 30, 2026
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Abstract

Micro and small industry (MSME) is an industrial sector that includes small-scale businesses, both those with limited assets and turnover. MSME is an industrial business that is mostly labor-intensive and plays a role in creating jobs and driving the local economy. One of the largest industries in MSME is the textile industry. Production in the textile industry tends to fluctuate due to market demand, availability of raw materials, and economic conditions. Understanding the dynamics of market demand is very important for the government and business actors in making decisions. This study aimed to predict the growth of MSME production in the textile industry using the seasonal autoregressive integrated moving average (SARIMA) method. Several SARIMA models were used to predict the growth of MSME production in the textile industry. However, only the model with the smallest AIC value was selected to predict the growth of MSME production in the textile industry. The prediction results showed that fluctuations occurred in the growth of the textile industry in each period.

Keywords

Micro and Small Industry SARIMA Special Region of Yogyakarta Textile Industry Time Series

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How to Cite
Cahyaningrum, N. H., Yotenka, R., & Suparna. (2026). Forecasting Production Growth of Micro and Small Textile Industries Using SARIMA. Enthusiastic : International Journal of Applied Statistics and Data Science, 6(1), 46–56. https://doi.org/10.20885/enthusiastic.vol6.iss1.art5
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References

  1. M. Junaidi, “UMKM Hebat, Perekonomian Nasional Meningkat,” Accessed: June 1, 2026. [Online]. Available: https://djpb.kemenkeu.go.id/kppn/curup/id/data-publikasi/artikel/2885-umkm-hebat,-perekonomian-nasional-meningkat.html
  2. Native, “UMKM Jaga Denyut Ekonomi Saat Pandemi, Pemerintah Dorong Perbaikan Ekosistemnya,” Pikiran Rakyat. Accessed: June 1, 2026. [Online]. Available: https://www.pikiran-rakyat.com/ekonomi/pr-013087425/umkm-jaga-denyut-ekonomi-saat-pandemi-pemerintah-dorong-perbaikan-ekosistemnya
  3. A.A. Widita, A.M. Lechner, and D.T. Widyastuti, “Spatial patterns and drivers of micro, small and medium-sized enterprises (MSMEs) within and across Indonesian cities: Evidence from highly granular data,” Reg. Sci. Policy Pract., vol. 16, no. 11, Nov. 2024, Art. no 100137, doi: 10.1016/j.rspp.2024.100137.
  4. V. Sarasi, I. Primiana, B. Harsanto, and Y. Satyakti, “Sustainable supply chain of Indonesia’s textile & apparel industry: opportunities and challenges,” Res. J. Text. Appar., vol. 28, no. 4, pp. 819–838, Nov. 2024, doi: 10.1108/RJTA-08-2022-0091.
  5. [5] BPS Indonesia, “Profil Industri Mikro dan Kecil 2023,” 2004. [Online]. Available: https://www.bps.go.id/id/publication/2024/09/18/52d85cbe9de005b6f5d69f95/profil-industri-mikro-dan-kecil-2023.html
  6. S.D.A. Kusumawardani, T.B.A. Kurnani, A.J. Astari, and S. Sunardi, “Readiness in implementing green industry standard for SMEs: Case of Indonesia’s batik industry,” Heliyon, vol. 10, no. 16, Aug. 2024, Art. no e36045, doi: 10.1016/j.heliyon.2024.e36045.
  7. K. Swaminathan and R. Venkitasubramony, “Demand forecasting for fashion products: A systematic review,” Int. J. Forecast., vol. 40, no. 1, pp. 247–267, Jan.–Mar. 2024, doi: 10.1016/j.ijforecast.2023.02.005.
  8. L. Ye, N. Xie, J.E. Boylan, and Z. Shang, “Forecasting seasonal demand for retail: A Fourier time-varying grey model,” Int. J. Forecast., vol. 40, no. 4, pp. 1467–1485, Oct.–Dec. 2024, doi: 10.1016/j.ijforecast.2023.12.006.
  9. U.H. Perez-Guerra et al., “Seasonal autoregressive integrated moving average (SARIMA) time-series model for milk production forecasting in pasture-based dairy cows in the Andean highlands,” PLoS ONE, vol. 18, no. 11, Nov. 2023, Art. no e0288849, doi: 10.1371/journal.pone.0288849.
  10. N. Qisthi, S.L. Fitri, A. Imannuel, and D.D. Dewi, “Prediksi harga emas untuk investasi masa depan menggunakan model seasonal autoregressive integrated moving average (SARIMA),” J. Innov. Res. Knowl., vol. 4, no. 7, pp. 4183–4194, Dec. 2024, doi: 10.53625/jirk.v4i7.9053.
  11. M.I. Rizki and T.A. Taqiyyuddin, “Penerapan Model SARIMA untuk Memprediksi Tingkat Inflasi di Indonesia,” J. Sains Mat. Stat., vol. 7, no. 2, pp. 62–72, Aug. 2021, doi: 10.24014/jsms.v7i2.13168.
  12. L.F. Tokan and A. Hermawan, “Implementasi model SARIMA untuk memprediksi produksi minyak kelapa sawit,” J. FASILKOM, vol. 13, no. 3, pp. 456–463, Dec. 2023, doi: 10.37859/jf.v13i3.6033.
  13. W. Rahmalina and S. Puspita, “Pemodelan seasonal autoregressive integrated moving average untuk memprediksi jumlah kasus COVID-19 di Padang,” J. Mat. Integratif, vol. 17, no. 1, pp. 23–31, Aug. 2021, doi: 10.24198/jmi.v17.n1.32024.23-31.
  14. W. Alwi, Adiatma, and Hafsari, “Peramalan produksi padi menggunakan metode SARIMA di Kabupaten Bone,” J. MSA Mat. Stat. Apl., vol. 11, no. 2, pp. 16–22, Aug. 2023, doi: 10.24252/msa.v11i2.36163.
  15. L.D. Jayanti, R. Lestari, F.A.D. Suparno, and F. Fachruzzaki, “Prediksi harga emas tahun 2024-2025 dengan metode autoregressive integrated moving average (ARIMA) pada aplikasi RStudio,” MARAS J. Penelit. Multidisip., vol. 3, no. 4, pp. 1275–1289, Oct. 2025, doi: 10.60126/maras.v3i4.1253.
  16. A.F. Muzakki, D. Aditama, and I.G. Anugrah, “Penerapan metode autoregressive integrated moving average untuk memprediksi penggunaan barang medis pada logistik medis Rumah Sakit Muhammadiyah Gresik,” Indexia, vol. 4, no. 1, pp. 1–16, Jun. 2022, doi: 10.30587/indexia.v4i1.3595.
  17. U. Usmadi, “Pengujian persyaratan analisis (uji homogenitas dan uji normalitas),” Inov. Pendidik., vol. 7, no. 1, pp. 50–62, Mar. 2020, doi: 10.31869/ip.v7i1.2281.
  18. U. Yakubu and M. Saputra, “Time series model analysis using autocorrelation function (ACF) and partial autocorrelation function (PACF) for e-wallet transactions during a pandemic,” Int. J. Glob. Oper. Res., vol. 3, pp. 80–85, Aug. 2022, doi: 10.47194/ijgor.v3i3.168.
  19. A. Mall and S. Singh, “Modelling and forecasting of CERES-retrieved ultraviolet radiation and AOD using a seasonal-ARIMA model in urban regions of Indo-Gangetic Plain,” Atmos. Environ., vol. 362, Dec. 2025, Art. no 121570, doi: 10.1016/j.atmosenv.2025.121570.
  20. F.E. Mokorimban, Y. Langi, and N. Nainggolan, “Penerapan metode autoregressive integrated moving average (ARIMA) dalam model intervensi fungsi step terhadap indeks harga konsumen di Kota Manado,” d’Cartesian, vol. 10, no. 2, pp. 91–99, Sep. 2021, doi: 10.35799/dc.10.2.2021.34969.
  21. K.R.A. Muslihin and B.N. Ruchjana, “Model autoregressive moving average (ARMA) untuk peramalan tingkat inflasi di Indonesia,” Limits J. Math. Its Appl., vol. 20, no. 2, pp. 209–218, Jul. 2023, doi: 10.12962/limits.v20i2.15098.
  22. I. Fadliani, I. Purnamasari, and W. Wasono, “Peramalan dengan metode SARIMA pada data inflasi dan identifikasi tipe outlier (Studi kasus: Data inflasi Indonesia tahun 2008-2014),” J. Stat. Univ. Muhammadiyah Semarang, vol. 9, no. 2, pp. 109–116, Dec. 2021, doi: 10.26714/jsunimus.9.2.2021.109-116.
  23. F. Ayiah-Mensah, S. Bosson-Amedenu, E.M. Baah, and J.A. Addor, “Advancements in seasonal rainfall forecasting: A seasonal auto-regressive integrated moving average model with outlier adjustments for Ghana’s Western Region,” Sci. Afr., vol. 28, Jun. 2025, Art. no e02632, doi: 10.1016/j.sciaf.2025.e02632.
  24. N. Septiani, N. Salam, and K. Khairullah, “Prakiraan indeks kekeringan menggunakan metode seasonal autoregressive integrated moving average (SARIMA) berdasarkan data standardized precipitation index (SPI) Kota Banjarbaru,” RAGAM J. Stat. Its Appl., vol. 2, no. 2, pp. 77–89, Jan. 2024, doi: 10.20527/ragam.v2i2.11334.
  25. J. Dai, Y. Xiao, Q. Sheng, J. Zhou, Z. Zhang, and F. Zhu, “Epidemiology and SARIMA model of deaths in a tertiary comprehensive hospital in Hangzhou from 2015 to 2022,” BMC Public Health, vol. 24, no. 1, Sep. 2024, Art. no 2549, doi: 10.1186/s12889-024-20033-7.
  26. D. Widyanti, S. Sudarno, and T. Widiharih, “Analisis volatilitas bitcoin menggunakan model ARCH dan GARCH,” J. Gaussian, vol. 12, no. 2, pp. 254–265, Jul. 2023, doi: 10.14710/j.gauss.12.2.254-265.
  27. Q. Cao, Z. Sun, and H. Li, “Comparative analysis of SARIMA, prophet, and a diagnostic decomposition–correction hybrid for long-horizon lottery sales forecasting,” Entropy, vol. 28, no. 3, March 2026, Art. no 286, doi: 10.3390/e28030286.
  28. H.N.D. Fortuna and A. Oktaviarina, “Metode SARIMA ARCH peramalan jumlah produksi padi Kabupaten Ngawi menggunakan metode SARIMA ARCH,” MATHunesa J. Ilm. Mat., vol. 12, no. 2, pp. 418–427, Apr. 2024, doi: 10.26740/mathunesa.v12n2.p418-427.
  29. D.W. Laraswati and A. Fauzan, “Implementasi metode runtun waktu dalam pemodelan total harga alat kedokteran dan kesehatan,” Jambura J. Probab. Stat., vol. 4, no. 1, pp. 30–38, May 2023, doi: 10.34312/jjps.v4i1.17873.
  30. H. Ning et al., “Enhancing public health surveillance: SARIMAX model incorporating Baidu search index for HCV prediction in China,” BMC Med. Res. Methodol., vol. 25, no. 1, Apr. 2025, Art. no. 108, doi: 10.1186/s12874-025-02562-w.
  31. B.G. Prianda and E. Widodo, “Perbandingan metode seasonal ARIMA dan extreme learning machine pada peramalan jumlah wisatawan mancanegara ke Bali,” BAREKENG J. Ilmu Mat. Dan Terap., vol. 15, no. 4, pp. 639–650, Dec. 2021, doi: 10.30598/barekengvol15iss4pp639-650.
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