IMPLEMENTASI GREY MODEL (1,N) UNTUK SISTEM PERAMALAN JUMLAH TANGKAPAN IKAN
Abstract
The increasing need for fish causes problems related to number of fish catches in the fisheries sector. In fish catches amount, all information related to fishing ground is well known, but on the other hand it is not easy to predict the number of fish catches due to unclear information. This is also related to the number of ships that make trips, the length (time) of the trip, the type of fishing gear, weather conditions, the quality of human resources, natural environmental factors, and others. The purpose of this study is to apply grey forecasting model GM (1.N) to forecast the number of fish catches. Grey forecasting models are used to build forecast models with limited amounts of data with short-term forecasts that will produce accurate forecasts. This study employs the data on monthly number of fish catches and wave height in the year of 2016 to 2018 to analyze calculations using the GM (1.N) models. The study was conducted with 36 time series data. The result showed that the MAPE on the GM (1.N) model of 57% in the experiment with 36 data.
References
BOESONO, H., SETIAWAN, D.R., PRIHANTOKO, K.E., JAYANTO, B.B. AND MALALA, A.R., 2016. Productivity Analysis of Mini Purse Seine in PPI Pulolampes Brebes, Central Java, Indonesia. Aquatic Procedia, [online] 7, pp.112–117.
CHEN, Z. AND WANG, X., 2012. Applying the Grey Forecasting Model to the Energy Supply Management Engineering. Systems Engineering Procedia, 5, pp.179–184.
CUI, J., LIU, S. FENG, ZENG, B. AND XIE, N. MING, 2013. A novel grey forecasting model and its optimization. Applied Mathematical Modelling, [online] 37(6), pp.4399–4406.
DIEBOLD, F., 2016. Forecasting in Economics , Business , Finance and Beyond. p.206.
DING, S., HIPEL, K.W. AND DANG, Y. GUO, 2018. Forecasting China’s electricity consumption using a new grey prediction model. Energy, [online] 149, pp.314–328..
FELTHOVEN, R.G. AND MORRISON PAUL, C.J., 2004. Directions for productivity measurement in fisheries. Marine Policy, 28(2), pp.161–169.
GRECH, V., 2018. WASP (Write a Scientific Paper) using Excel – 13: Correlation and Regression. Early Human Development, [online] 122(xxxx), pp.60–63.
HYNDMAN, L.R.J., 2014. Forecasting : Principles & Practice. (September).
KIM, J.Y., JEONG, H.C., KIM, H. AND KANG, S., 2015. Forecasting the monthly abundance of anchovies in the South Sea of Korea using a univariate approach. Fisheries Research, [online] 161, pp.293–302.
LI, D., CHANG, C., CHEN, C. AND CHEN, W., 2012. Forecasting short-term electricity consumption using the adaptive grey-based approach — An Asian case. Omega, [online] 40(6), pp.767–773.
OU, S., 2012. Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithm. COMPUTERS AND ELECTRONICS IN AGRICULTURE, [online] 85, pp.33–39.
SHODIQ, M. AND SAPUTRA, B.D., 2022. Grey Forecasting Model Untuk Peramalan Harga Ikan Budidaya. 9(6), pp.1770–1778.
SUGIYONO, 2011. Statistika untuk penelitian / Sugiyono. Statistika untuk penelitian
TIEN, T.L., 2012. A research on the grey prediction model GM(1,n). Applied Mathematics and Computation, [online] 218(9), pp.4903–4916.
WANG, S.J., WANG, W.L., HUANG, C.T. AND CHEN, S.C., 2011. Improving inventory effectiveness in RFID-enabled global supply chain with Grey forecasting model. Journal of Strategic Information Systems, [online] 20(3), pp.307–322.
YUAN, C., LIU, S. AND FANG, Z., 2016. Comparison of China’s primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model. Energy, [online] 100, pp.384–390.
ZENG, B., LUO, C., LIU, S., BAI, Y. AND LI, C., 2016. Development of an optimization method for the GM(1,N) model. Engineering Applications of Artificial Intelligence, [online] 55, pp.353–362.
| Keywords | : |
Keywords:
Kata kunci: GM(1,N), Peramalan, Jumlah Tangkapan Ikan.
|
| Galleys | : | |
| Published | : |
2023-05-10
|
| How to Cite | : |
Shodiq, M., Ayuningsih, E., & Ramanda, F. (2023). IMPLEMENTASI GREY MODEL (1,N) UNTUK SISTEM PERAMALAN JUMLAH TANGKAPAN IKAN. Jurnal Informatika Medis (J-INFORMED), 1(1), 24–30. https://doi.org/10.52060/im.v1i1.1194
|
| Issue | : |
Section
Articles
|
.png)
