Analysis of the Ensemble Method Classifier's Performance on Handwritten Arabic Characters Dataset


Abdul Rachman Manga'(1); Anik Nur Handayani(2); Heru Wahyu Herwanto(3); Rosa Andrie Asmara(4); Yudha Islami Sulistya(5*); Kasmira Kasmira(6);

(1) Universitas Negei Malang, universitas Muslim Indonesia
(2) Universitas Negeri Malang
(3) Universitas Negeri Malang
(4) State Polytechnic of Malang
(5) Universitas Muslim Indonesia
(6) Universitas Muslim Indonesia
(*) Corresponding Author

  

Abstract


Arabic character handwriting is one of the patterns and characteristics of each person's writing. This characteristic makes Arabic writing more challenging if the letter recognition process is based on a dataset of Arabic scripts. This Arabic script has been presented in a dataset totaling 16800, each representing a class of hijaiyah letters starting from alif to yes, consisting of 600 data for each class. The accuracy of the data used can be increased using the ensemble method. By using multiple algorithms at simultaneously, the ensemble technique can raise the level or result of a score in machine learning. This study's primary goal is to evaluate the ensemble method classifier's performance on datasets of handwritten Arabic characters. The classifier uses the ensemble method by applying the proposed soft voting to provide a multiclass classification of three machine learning algorithms, namely, SVM, Random Forest, and Decision Tree for classification. This research process produces an accuracy value for the voting classifier of 0.988 and several other SVM algorithms with an accuracy of 0.103, a random forest with an accuracy of 1.0, and a decision tree with an accuracy of 0.134. The test results used the confusion matrix evaluation model, including accuracy, precision, recall, and f1-score of 0.99.


Keywords


Ensemble Method; Voting Classifiers; Arabic Character Handwriting; Evaluation Model.

  
  

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doi  https://doi.org/10.33096/ilkom.v15i1.1357.186-192 		  

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