Automatic Clustering and Fuzzy Logical Relationship to Predict the Volume of Indonesia Natural Rubber Export

Widya Aprilini, Dian Palupi Rini, Hadipurnawan Satria

Abstract

Natural rubber is one of the pillars of Indonesia's export commodities. However, over the last few years, the export value of natural rubber has decreased due to an oversupply of this commodity in the global market. To overcome this problem, it is possible to predict the volume of Indonesia natural rubber exports. Predicted values can also help the government to compile market intelligence for natural rubber commodities periodically. In this study, the prediction of the export volume of natural rubber was carried out using the Automatic Clustering as an interval maker in the Fuzzy Time Series or usually called Automatic Clustering and Fuzzy Logical Relationship (ACFLR). The data used is 51 data per year from 1970 to 2020. The purpose of this study is to predict the volume of Indonesia natural rubber exports and compare the prediction results between the Automatic Clustering and Fuzzy Logical Relationship (ACFLR) and Chen's Fuzzy Time Series. The results showed that there was a significant difference between the two methods, ACFLR got 0.5316% MAPE with  and Chen's Fuzzy Time Series model got 8.009%. Show that the ACFLR method performs better than the pure Fuzzy Time Series in predicting volume of Indonesia natural rubber exports.

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DOI: https://doi.org/10.36706/sjia.v4i1.51

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