Abstract—In Indonesia, paddy production depends heavily on the amount of rainfall. Thus, there needs to be a risk analysis for paddy production by utilizing rainfall data patterns. However, since much rainfall data is missing then we use the ENSO indicator which is anomaly SST 3.4. In the previous research, the results of software design include analysis of the relationship between paddy harvest area and anomaly of SST 3.4 by using Copula and estimation model design of paddy harvest area using Robust regression. This research implements the prediction model of harvested area based on ENSO indicators into a web-based software. The results of this harvested area model will be used to predict paddy production. Furthermore, the prediction of rice production is compared with the amount of rice consumption of the population to obtain the level of risk of paddy production. Thematic maps are used to present the risk level of paddy production.
Index Terms—Decision Support System (DSS), ENSO, copula, robust regression.
Nisa Miftachurohmah is with the Information System Department, Universitas Sembilanbelas November Kolaka, Indonesia (e-mail: email@example.com).
Imam Mukhlash is with the Mathematics Department, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia (e-mail: firstname.lastname@example.org).
Sutikno is with the Statistics Department, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia (e-mail: email@example.com).
Cite: Nisa Miftachurohmah, Imam Mukhlash, and Sutikno, "Web-Based Implementation of Risk Analysis of Paddy Production with ENSO Indicators," International Journal of Machine Learning and Computing vol. 9, no. 3, pp. 304-309, 2019.