Improving the Accuracy of C4.5 Algorithm with Chi-Square Method on Pure Tea Classification Using Electronic Nose

  • Mula Agung Barata Universitas Dian Nuswantoro
  • Edi Noersasongko Universitas Dian Nuswantoro
  • Purwanto Universitas Dian Nuswantoro
  • Moch Arief Soeleman Universitas Dian Nuswantoro
DOI: https://doi.org/10.29207/resti.v7i2.4687
Keywords: Electronic Nose, E-nose, C4.5 Algorithm, Chi-Square, Tea Plantation Commodities, Pure Indonesian Tea

Abstract

Tea is one of the plantation products within the Ministry of Agriculture of the Republic of Indonesia, which plays an essential role as a mainstay commodity that boosts the Indonesian economy. Each type of tea has different properties, and the aroma of each type of tea can measure the quality of the tea. The human sense of smell is still very limited in classifying pure types of tea. Therefore, a device is needed to help measure the aroma of tea from an electronic nose. The devices attached to several gas sensors help humans take data from the smell of pure tea and calculate the value of each type of tea to test datasets with data mining algorithms. This study uses the C4.5 algorithm as a classification method with advantages over noise data, missing values, and handling variables with discrete and continuous types. Meanwhile, Chi-square is used to perform attribute severing in the data preprocessing process to increase the accuracy of dataset testing. Testing a pure tea dataset with four whole attributes, namely CO2, CO, H2, and CH4, using the C4.5 algorithm resulted in an accuracy of 93.65% and an increase in the accuracy performance of the C4.5 algorithm by 94.27% with dataset testing using Chi-Square feature selection with the two highest value attributes.

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Published
2023-03-03
How to Cite
Barata, M. A., Edi Noersasongko, Purwanto, & Moch Arief Soeleman. (2023). Improving the Accuracy of C4.5 Algorithm with Chi-Square Method on Pure Tea Classification Using Electronic Nose. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(2), 226 - 235. https://doi.org/10.29207/resti.v7i2.4687
Issue
Vol 7 No 2 (2023): April 2023
Section
Information Systems Engineering Articles

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