Perbandingan Algoritma Machine Learning menggunakan Orange Data Mining untuk Klasifikasi Jenis Kendaraan pada Sistem Tilang Digital

Authors

  • Egipta Pranadjaya Program Studi Teknik Mesin, Fakultas Teknik, Universitas Katolik Indonesia Atma Jaya, Jakarta 12930, Indonesia
  • Evan Sudira Pangestu Program Studi Teknik Mesin, Fakultas Teknik, Universitas Katolik Indonesia Atma Jaya, Jakarta 12930, Indonesia
  • Catherine Olivia Sereati Program Studi Teknik Elektro, Fakultas Teknik, Universitas Katolik Indonesia Atma Jaya, Jakarta 12930, Indonesia
  • Sandra Octaviani Program Studi Teknik Elektro, Fakultas Teknik, Universitas Katolik Indonesia Atma Jaya, Jakarta 12930, Indonesia
  • Marten Darmawan Program Studi Teknik Mesin, Fakultas Teknik, Universitas Katolik Indonesia Atma Jaya, Jakarta 12930, Indonesia

DOI:

https://doi.org/10.25170/jurnalelektro.v17i1.5429

Keywords:

Classification of Vehicle Types, Logistic Regression, Neural Network, Orange Data Mining, Support Vector Machine

Abstract

This paper discusses the application of the Orange Data Mining application to compare several machine learning algorithms for classifying vehicle types in digital ticket systems. This research compares and analyzes the logistic regression algorithm, Support Vector Machine (SVM) and Neural Network (NN) to solve vehicle classification problems in digital traffic tickets. The research results show that in the training process and based on the dataset used, the algorithms that have the highest level of accuracy are Logistic Regression, Neural Network and Support Vector Machine. Meanwhile, during the testing process, all algorithms in the model were able to carry out classification with 100% accuracy

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Published

2024-04-29
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