Abstract

Sea cucumbers are marine resources that have a significant ecological role and important economic value. UD. Matahari Jalan Sukolilo Baru II, Bulak District, Surabaya is an MSME that processes sea cucumber catches and sells them to various distributors, including trading abroad. The catch of sea cucumbers obtained by fishermen in uncertain quantities, every subsequent period. Therefore, the results of sea cucumber fishing are known to be influenced by several factors, one of which is climatic factors such as temperature, humidity and tides. The research was conducted to predict the uncertain catch of sea cucumbers with the ARIMA method to obtain effective modeling and equations. Forecasting can help determine the right period by using one of the methods that correspond to the sequence of time. The ARIMA method is an approach used in time series analysis to model and forecast data arranged in a specific order. Predict the catch of sea cucumbers by looking at the smallest error, and the catch of sea cucumbers after forecasting in the next period. The result of the selection of the best ARIMA model from the humidity variable is (1,1,1) more significant and effective for sea cucumber fishing in the short term (1.2 days) in the rainy season, with the smallest error of 271.11. The forecast results in April 2024 for the 107 period are 268.42 kg, the forecast data is close to the actual data in the previous period.

Keywords

  • University
  • Intellectuals
  • University Intellectuals
  • Higher Education
  • Industrial Revolution 4.0

References

  1. Jernihati, Y., Kurniawan, G., & Zai, S. Analysis of Sales Forecasting in Raw Material Inventory Management At Sun Cafe. J. Gea. 483 Jurnal EMBA, 11(4), 483–490, 2023.
  2. Oey, E., & Ayrine, G. K. Penerapan Proses Dan Teknik Peramalan – Studi Kasus Di Manufaktur Transformer. Jurnal Manajemen Industri Dan Logistik, 2(2), 106–115, 2018.
  3. Buchori, M., & Sukmono, T. Peramalan Produksi Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA) di PT. XYZ. PROZIMA (Productivity, Optimization and Manufacturing System Engineering), 2(1), 27–33, 2018.
  4. Pramujo, B., Juwono, P. T., & Soetopo, W. (2015). Pemodelan Debit Menggunakan Metode Arima Guna Menentukan Pola Operasi Waduk Selorejo. Jurnal Teknik Pengairan, 5(2), 141–148, 2015.
  5. Hutasuhut, A. H., Anggraeni, W., & Tyasnurita, R. (2014). Bahan Baku Plastik. Jurnal Teknik POMITS, 3(2), 169–174, 2014.
  6. Nur, M. I., & Puspita, A. R. Peramalan Produksi Banggai Cardinalfish Menggunakan Autoregressive Integrated Moving Average (ARIMA). Jurnal Karya Pendidikan Matematika, 8(1), 23–29, 2021.
  7. Nugroho, K. Model Analisis Prediksi Menggunakan Metode Fuzzy Time Series. Infokam, 12(1), 46–50, 2016.
  8. Wei, W.W.S. Time Series Analysis:Univariate and Multivariate Methods. New York: Pearson, 2006.
  9. Salwa, N., Tatsara, N., Amalia, R., & Zohra, A. F. Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average). Journal of Data Analysis, 1(1), 21–31, 2018.
  10. Razak, F. A., Shitan, M., Hashim, A. H., & Abidin, I. Z. Load Forecasting Using Time Series Models. Jurnal Kejuruteraan, 21(1), 53–62, 2009.
  11. Amjady, N. Short-Term Hourly Load Forecasting Using Time-Series Modeling With Peak Load Estimation Capability. IEEE Transactions on Power Systems, 16(4), 798–805, 2001.
  12. Djoni, H. . Penerapan Model ARIMA untuk Memprediksi Harga Saham PT. Telkom Tbk. Jurnal Ilmiah Sains, 11(1), 116–123, 2011.
  13. Pal, N., Barton, K. N., Petersen, M. R., Brus, S. R., Engwirda, D., Arbic, B. K., Roberts, A. F., Westerink, J. J., & Wirasaet, D. (2023). Barotropic Tides in MPAS-Ocean: impact of ice shelf cavities. Geoscientific Model Development, 16(4), 1297–1314. https://doi.org/10.5194/gmd-16-1297-2023.