Forecasting Analysis of Fishermen’s Productivity Data Using Single Exponential Smoothing

Taufiq Dwi Cahyono; Heri  Purwanto; Iwan  Adhicandra; Kraugusteeliana  Kraugusteeliana; Edy  Winarno

doi:10.35877/454RI.jinav1487

Taufiq Dwi Cahyono Universitas Semarang, Semarang City, Central Java 50160, Indonesia (ID)
Heri Purwanto Universitas Sangga Buana, Bandung City, West Java 40124, Indonesia (ID) https://orcid.org/0000-0001-5358-1513
Iwan Adhicandra Universitas Bakrie, Special Capital Region of Jakarta 12940, Indonesia (ID) https://orcid.org/0000-0003-2652-9167
Kraugusteeliana Universitas Pembangunan Nasional Veteran Jakarta, Special Capital Region of Jakarta 12450, Indonesia (ID)
Edy Winarno Universitas Stikubank, Semarang City, Central Java 50241, Indonesia (ID)

https://doi.org/10.35877/454RI.jinav1487

Keywords: Forecasting, Fishermen’s Productivity Data, Single Exponential Smoothing, Controlling Production and Fulfill Market Demand

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Abstract

One of the reasons why it is vital to forecast fisher production data in coastal regions is to increase fish resource management efficiency. By calculating the number of fishing boats, the amount of fish that must be caught, and the amount of raw materials required for fish processing based on the anticipated amount of fishermen's production in the following period, decision-makers can determine the amount of fish that must be caught and the amount of raw materials required for fish processing. So that the objective of the research is to forecast fishermen's production data using the Single Exponential Smoothing method, this method is effectively used to perform forecasting of time series data with short period data intervals to produce forecasts for the next period, and it can measure the rate of change of fishermen's production data each period. The results of forecasting data on fishermen's production utilizing time series data intervals from October 2022 to January 2023 to make forecasts for February 2023, namely a MAPE error rate of 2.85%, indicate that the forecasting results are within the "good" category.

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