Analysis of the Effect of Data Scaling on the Performance of the Machine Learning Algorithm for Plant Identification

Analisis Pengaruh Data Scaling Terhadap Performa Algoritma Machine Learning untuk Identifikasi Tanaman

  • Agus Ambarwari Universitas Teknokrat Indonesia
  • Qadhli Jafar Adrian Universitas Teknokrat Indonesia
  • Yeni Herdiyeni Institut Pertanian Bogor
Keywords: min-max normalization, standardization, zero-mean normalization, machine learning algorithms

Abstract

Data scaling has an important role in preprocessing data that has an impact on the performance of machine learning algorithms. This study aims to analyze the effect of min-max normalization techniques and standardization (zero-mean normalization) on the performance of machine learning algorithms. The stages carried out in this study included data normalization on the data of leaf venation features. The results of the normalized dataset, then tested to four machine learning algorithms include KNN, Naïve Bayesian, ANN, SVM with RBF kernels and linear kernels. The analysis was carried out on the results of model evaluations using 10-fold cross-validation, and validation using test data. The results obtained show that Naïve Bayesian has the most stable performance against the use of min-max normalization techniques as well as standardization. The KNN algorithm is quite stable compared to SVM and ANN. However, the combination of the min-max normalization technique with SVM that uses the RBF kernel can provide the best performance results. On the other hand, SVM with a linear kernel, the best performance is obtained when applying standardization techniques (zero-mean normalization). While the ANN algorithm, it is necessary to do a number of trials to find out the best data normalization techniques that match the algorithm.

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Published
2020-02-09
How to Cite
Ambarwari, A., Jafar Adrian, Q., & Herdiyeni, Y. (2020). Analysis of the Effect of Data Scaling on the Performance of the Machine Learning Algorithm for Plant Identification. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(1), 117 - 122. https://doi.org/10.29207/resti.v4i1.1517
Section
Artikel Teknologi Informasi