MODEL PERAMALAN JUMLAH TANGKAPAN IKAN KAKAP YANG DIDARATKAN DI PPI OEBA KUPANG NUSA TENGGARA TIMUR

Sri Imelda Edo, Mikson M. D. Nalle
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Abstract

Snapper is one of the economically important fish targeted by fishers in the island of Timor. Domestic and foreign markets for fillet and fresh snapper is considerably large. The export volume in 2021 was 4,172,056 kg, and the total value reached 12,452,211 USD. Along with the increasing snapper production in Kupang City and NTT, it is necessary to forecast the future snapper production. Thus, it can be a reference for policy makers in the region in designing fisheries development. This study aims to produce forecasting model for snapper catches at PPI Oeba Kupang. This research applied Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA), as a forecasting method. The data of snapper production analyzed in this study consisted of 72 months, started from January 2016 to December 2021. Data were obtained from the UPT of the Department of Marine and Fisheries of NTT Province at PPI Oeba Kupang. Data have a non-stationary pattern to the variance, hence a logarithmic transformation is needed, as well as seasonal differences analysis. However, there is no need for non-seasonal differences since the data are stationary with respect to the mean. The results of the identification of the Autocorrelation function and Partial Autucorrelation function Plots are ARIMA models with seasonal factor period 4, ARIMA (P,D,Q) = (1,0,1), while the SARIMA order (P, D, Q) = (2,1,1)4. Based on parameter testing, verification, examination, and testing of suitable models, the best model obtained was (0,0,1)(2,1,1)4. Keywords:       Autoregressive Integrated Moving Average, focecasting model, Seasonal Autoregressive Integrated Moving Average, Snapper.
东努沙登加拉省欧巴古邦笛鲷捕获量预测模型。
鲷鱼是帝汶岛渔民捕捞的重要经济鱼类之一。鲷鱼片和新鲜鲷鱼的国内外市场相当大。2021 年的出口量为 4,172,056 公斤,总价值达到 12,452,211 美元。随着古邦市和 NTT 的鲷鱼产量不断增加,有必要对未来的鲷鱼产量进行预测。因此,它可以为该地区的决策者设计渔业发展提供参考。本研究旨在建立古邦乌巴鲷鱼产量预测模型。本研究采用了自回归综合移动平均法(ARIMA)和季节自回归综合移动平均法(SARIMA)作为预测方法。本研究分析的鲷鱼产量数据包括从 2016 年 1 月至 2021 年 12 月的 72 个月。数据来自位于古邦 PPI Oeba 的南太平洋省海洋与渔业部 UPT。数据的方差具有非平稳模式,因此需要进行对数变换和季节差异分析。然而,由于数据相对于均值是静止的,因此不需要进行非季节性差异分析。自相关函数和部分自相关函数图的识别结果是季节因子周期为 4 的 ARIMA 模型,ARIMA(P,D,Q)=(1,0,1),而 SARIMA 阶数(P,D,Q)=(2,1,1)4。根据参数测试、验证、检验和测试合适的模型,得到的最佳模型是(0,0,1)(2,1,1)4。 关键词:自回归整合移动平均 自回归综合移动平均值 预测模型 季节性自回归综合移动平均值 鲷鱼
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