PREDIKSI FITUR KEMASAN PRODUK MINYAK KAYU PUTIH DENGAN SUPPORT VECTOR MACHINE (SVM)

  • Wilma Latuny Universitas Pattimura
  • Victor O. Lawalata Universitas Pattimura
  • Daniel B. Paillin Universitas Pattimura
  • Rahman Ohoirenan Universitas Pattimura
DOI: https://doi.org/10.30598/ale.4.2021.76-82
Keywords: Prediction Accuracy, Packaging Features, Eucalyptus Oil, Support Vector Machine

Abstract

UD Sinar Baru has eucalyptus oil products with various sizes from 30 ml to 550 ml, and the size of 550 ml is the most consumed eucalyptus oil product. However, this product has been criticized by consumers for its packaging which has not met their expectations. This study aims to obtain an accurate method of classifying consumer sentiment and obtain features that affect the redesign of the 550 ml eucalyptus oil product packaging. Collecting data using an online survey method from social media Facebook to get consumer comments using power queries. Data analysis uses the concept of the Support Vector Machine (SVM) method with the support of the WEKA application to provide sentiment analysis and accuracy of consumer comments. The results of the study present the tendency of comments on each attribute with an assessment of 83% accuracy for the entire class, 3% for positive class comments, and 57% comments for negative class. The sentiment that shows the packaging tends to be normal at 20% which is interpreted as neutral. The conclusion from the results of this study is that SMO has a very accurate prediction rate to analyze consumer sentiment about the features of the 550 ml eucalyptus oil packaging, and it is necessary to redesign the current packaging by considering the features of shape, color, size, and efficiency.

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Published
2021-08-17
How to Cite
Latuny, W., Lawalata, V., Paillin, D., & Ohoirenan, R. (2021). PREDIKSI FITUR KEMASAN PRODUK MINYAK KAYU PUTIH DENGAN SUPPORT VECTOR MACHINE (SVM). ALE Proceeding, 4, 76-82. https://doi.org/10.30598/ale.4.2021.76-82
Issue
Vol 4 (2021): Archipelago Engineering (ALE)
Section
Articles

Copyright (c) 2021 Wilma Latuny, Victor O. Lawalata, Daniel B. Paillin, Rahman Ohoirenan

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