Fuzzy Inference System Tsukamoto–Decision Tree C 4.5 in Predicting the Amount of Roof Tile Production in Kebumen

Authors

  • Tundo Tundo Departement of Informatics, Universitas 17 Agustus 1945 Jakarta
  • Fajar Mahardika Departement of Computer Technology, ITS NU Pekalongan

DOI:

https://doi.org/10.31764/jtam.v7i2.13034

Keywords:

Fuzzy Logic, Fuzzy Inference System Tsukamoto, Decision Tree C 4.5, Forecasting

Abstract

Tile is a product that is in great demand by many people. This has become a trigger for producers to improve their management. The company's tile production management is still experiencing problems, namely frequent miscalculations in determining the agreement that must be issued in making tile production from customer requests. One of the efforts made is to predict the production that can be done to get the optimal amount obtained, to get a big profit. In this study, to obtain a prediction of the amount of tile production, computerized calculations were carried out using the Tsukamoto fuzzy logic method. This method uses the concept of rules from the C 4.5 decision tree in the building to make it easier to determine the rules that are built without having to consult an expert because C 4.5 will study existing datasets to serve as a reference in forming these rules according to conditions that often occur. The modeling results produce relevant rules after being compared with the actual results. The results of the comparison of predictions with actual production have an error percentage of 29.34%, with a truth of 70.66% (based on the calculation of the Average Forecasting Error Rate (AFER)). Therefore when implemented in the Tsukamoto Fuzzy Inference System it can produce predictions of tile production that are quite optimum. It is said to be quite optimum because all customer requests are met, either generated by the production prediction itself or the prediction results are added up with inventory data, and all predictions are close to actual production.

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Published

2023-04-08

Issue

Vol. 7 No. 2 (2023): April

Section

Articles

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