Forecasting the Number of Visitors to the UIN Walisongo Semarang Library with the Decomposition Method

Eva Khoirun Nisa*  -  Universitas Islam Negeri Walisongo Semarang, Indonesia
Nawwarotul Jannah  -  Universitas Islam Negeri Walisongo Semarang, Indonesia
Muhammad Amjed Iqbal  -  University of Agriculture Faisalabad, Pakistan

(*) Corresponding Author
DOI: 10.21580/at.v14i1.7858

The library is an important part of a university. Especially for UIN Walisongo Semarang which states to be a research Islamic university based on the vision that has been announced. The library is a vital unit contributing to the realization of the vision of the institution because it is a storage place for various research results both from inside and outside UIN Walisongo Semarang. In addition, several problems occur in the library, so it is necessary to forecast the number of visitors to ascertain the number of librarians who serve it. Decomposition becomes a statistical method in predicting the number of visitors because this method breaks the components of a periodic series separately which can improve the accuracy of forecasting. This prediction has resulted in an increase in the number of visitors to the library of UIN Walisongo Semarang in 2020 compared to the previous year. Based on the results of forecasting the number of visitors, the number of librarians that must be provided is 42 librarians.

Keywords: Forecasting, Decomposition Method, Library

  1. Asadi, R., & Regan, A. (2020). A spatio-temporal decomposition based deep neural network for time series forecasting. Applied Soft Computing, 87.
  2. Azhari, A., & Ramadan, Z. H. (2022). The Intensity of Visiting the School Library as an Indicator of Students’ Reading Interest in Elementary Schools. International Journal of Elementary Education, 6(2).
  3. Caudillo, D., Encinas, S., Martínez-Rocha, R., & Marcos, J. (2022). Experiential learning: visiting libraries in Southern California. Qualitative & Quantitative Methods in Libraries, 11(1).
  4. Chang, P. C., Wang, Y. W., & Liu, C. H. (2007). The development of a weighted evolving fuzzy neural network for PCB sales forecasting. Expert Systems with Applications, 32(1), 86–96. https://doi.org/10.1016/j.eswa.2005.11.021
  5. Chen, R. J. C., & Bloomfield, P. (2003). An Evaluation of Alternative Forecasting Methods to Recreation Visitation. Journal of Leisure Research, 35(4), 441–454.
  6. Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003
  7. LPM. (2015). Selayang Pandang Universitas Islam Negeri Walisongo. LPM UIN Walisongo Semarang.
  8. Moghadam, H. S., R., V., Shahabi, H., Chapi, K., & Shirzadi, A. (2018). Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. Journal of Environmental Management, 217, 1–11.
  9. Navratil, M., & Kolkova, A. (2019). Decomposition and Forecasting Time Series in Business Economy Using Prophet Forecasting Model BUSINESS ECONOMY USING PROPHET FORECASTING. Central European Business Review, 8(December). https://doi.org/10.18267/j.cebr.221
  10. Nisa, E. K. (2022). Penentuan Saham Syariah Terbaik melalui Pendekatan Peramalan. Square: Journal of Mathematics and Mathematics …, 4(1), 27–42. https://doi.org/10.21580/square.2022.4.1.11229
  11. Partap, B. (2019). A review of service quality assessment of library and information centres. Library Philosophy and Practice, 2019(June).
  12. Prema, V., & Rao, K. U. (2015). Time series decomposition model for accurate wind speed forecast. Renewables: Wind, Water, and Solar, 2(1). https://doi.org/10.1186/s40807-015-0018-9
  13. Rafikasari, E. F., & Rohman, K. (2018). Analisis Deskriptif Pengunjung Perpustakaan IAIN Tulungagung. Dinamika Penelitian: Media Komunikasi Sosial Keagamaan, 18(1), 105–122.
  14. Rodrigues, P. C., Pimentel, J., Messala, P., & Kazemi, M. (2020). The decomposition and forecasting of mutual investment funds using singular spectrum analysis. Entropy, 22(1), 83. https://doi.org/10.3390/e22010083
  15. Subagyo, P. (1986). Forecasting Konsep dan Aplikasi. BPFE.
  16. Sudjana. (1986). Metode Statistika. Tarsito.
  17. Thoplan, R. (2014). Qualitative v/s Quantitative Forecasting of Yearly Tourist Arrival in Mauritius. International Journal of Statistics and Applications, 4, 198–203.
  18. Tim Penyusun Standar Nasional Perpustakaan RI Bidang Perpustakaan Sekolah dan Perpustakaan Perguruan Tinggi. (2011). Standar Nasional Perpustakaan ( SNP ). Perpustakaan Nasional RI.
  19. Ulya, D. I., Airlangga, U., & Java, E. (2020). DECOMPOSITION METHOD FOR FORECASTING THE NUMBER OF. Jurnal Biometrika Dan Kependudukan, 9(July), 36–43. https://doi.org/10.20473/jbk.v9i1.2020.36
  20. Undang Undang Republik Indonesia No 43 tahun 2007 tentang Perpustakaan. (2007).
  21. Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. Advances in Neural Information Processing Systems, 27(NeurIPS), 22419–22430.
  22. Yudaruddin, R. (2019). Forecasting untuk Kegiatan Ekonomi dan Bisnis. Pustaka Horizon.

Open Access Copyright (c) 2022 At-Taqaddum
Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
At-Taqaddum
Published by Lembaga Penjaminan Mutu
Universitas Islam Negeri (UIN) Walisongo Semarang
Jl. Walisongo No.3-5 Semarang 50185, Indonesia
Phone: +62 857-1999-1679
Website: https://lpm.walisongo.ac.id/
Email: attaqaddum@walisongo.ac.id 
 
ISSN: 1979-4703 (p)
ISSN: 2527-9726 (e)
DOI : 10.21580/at

Lisensi Creative Commons
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 
apps