EKSAKTA: Journal of Sciences and Data Analysis
VOLUME 18, ISSUE 1, February 2018

Estimation of Exponential Smoothing Parameter on Pesticide Characteristic Forecast using Ant Colony Optimization (ACO)

Dinita Rahmalia (Universitas Islam Darul Ulum Lamongan)



Article Info

Publish Date
21 Mar 2018

Abstract

Pest in agriculture can raise plant disease and fail to harvest. The pest problem in agriculture can be solved by using pesticide. Pesticide usage must be done proportionally. So, the manufacturer should fix standard pesticide active ingredient in pesticide production. Forecast is a prediction of some future evens. In forecast problem, there are any parameters which should be determined. Parameters can be estimated by exact method or heuristic method. Ant Colony Optimization (ACO) is inspired from the cooperative behavior of ant colonies, which can find the shortest path from their nest to a food source. In this research, we use heuristic method like ACO to estimate exponential smoothing parameter on pesticide active ingredient forecast and pesticide sample weight forecast. From the simulation, on the first iteration, all ants choose parameter randomly. At the optimization process, we update pheromone until all ants choose the similar parameter so that process converges and variance approaches to zero. The optimal exponential smoothing parameter can be applied in forecasting with minimum sum of squared error (SSE).

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Journal Info

Abbrev

eksakta

Publisher

Subject

Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Materials Science & Nanotechnology

Description

Ekstakta is an interdisciplinary journal with the scope of mathematics and natural sciences that is published by Fakultas MIPA Universitas Islam Indonesia. All submitted papers should describe original, innovatory research, and modelling research indicating their basic idea for potential ...