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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References


N. Kasim and G. S. Nugraha, “Pengenalan pola tulisan tangan aksara Arab menggunakan metode Convolution Neural Network,” Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ), vol. 3, no. 1, pp. 85–95, 2021, doi: 10.29303/jtika.v3i1.136.

P. Tesa Ananda, “Pengenalan Karakter Aksara Incung (Kerinci) ke Karakter Latin menggunakan metode Convolutional Neural Network,” Universitas Jambi, Jambi, 2021. [Online]. Available: https://repository.unja.ac.id/28261/

A. Eko Cahyo and A. Nilogiri, “Klasifikasi gangguan Autisme pada anak menggunakan Algoritma C4.5 Denganteknik Random Forest,” Jember, 2021.

S. Kumari, D. Kumar, and M. Mittal, “An Ensemble Approach for Classification and Prediction of Diabetes Mellitus using Soft Voting Classifier,” International Journal of Cognitive Computing in Engineering, vol. 2, no. 2, pp. 40–46, 2021, doi: 10.1016/j.ijcce.2021.01.001.

E. Nuranti Kusuma, “Identifikasi citra huruf Arab menggunakan metode Jaringan Syaraf Tiruan Kohonen,” Universitas Islam Negeri Maulana Malik Ibrahim, Malang, 2015.

D. A. Nasution, H. H. Khotimah, and N. Chamidah, “Perbandingan normalisasi data untuk klasifikasi wine menggunakan algoritma K-NN,” CESS (Journal of Computer Engineering System and Science), vol. 4, no. 1, p. 78, 2019, doi: 10.24114/cess.v4i1.11458.

O. V. Putra, T. Harmini, and A. Saroji, “Outlier detection on graduation data of Darussalam Gontor University using One-Class Support Vector Machine,” in ENASAINS 2nd, 2021, vol. 1, no. 2, pp. 1–4.

C. Cortes and V. Vapnik, “Support-Vector Networks,” IEEE Expert-Intelligent Systems and their Applications, vol. 7, no. 5, pp. 63–72, 1992, doi: 10.1109/64.163674.

I. Wijayanto, A. Humairani, A. Rizal, and S. Hadiyoso, “Klasifikasi sinyal EKG menggunakan Ciri Statistik dan Parameter Hjorth dengan SVM dan k-NN,” ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika , vol. 10, no. 1, pp. 132–145, 2022.

T. M. Oshiro, P. S. Perez, and J. A. Baranauskas, “How Many Trees in A Random Forest?,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7376 LNAI, pp. 154–168, 2012, doi: 10.1007/978-3-642-31537-4_13.

A. H. Nasrullah, “Implementasi algoritma Decision Tree untuk klasifikasi produk laris,” Jurnal Ilmiah Ilmu Komputer, vol. 7, no. 2, pp. 45–51, 2021, doi: 10.33480/pilar.v14i2.926.

I. Indriati and A. Kusyanti, “Metode Ensemble Classifier untuk mendeteksi jenis Attention Deficit Hyperactivity Disorder (ADHD) Pada Anak Usia Dini,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 6, no. 3, pp. 301–308, 2019, doi: 10.25126/jtiik.201961313.

M. Jumarlis, M. Mirfan, and A. R. Manga, “Classification of Coffee Bean Defects using Gray-Level Co-Occurrence Matrix and K-Nearest Neighbor,” ILKOM Jurnal Ilmiah, vol. 14, no. 1, pp. 1–9, Apr. 2022, doi: 10.33096/ilkom.v14i1.910.1-9.

S. Budiman, A. Sunyoto, and A. Nasiri, “Analisa Performa penggunaan feature selection untuk mendeteksi Intrusion Detection Systems dengan Algoritma Random Forest Classifier,” SISTEMASI:Jurnal Sistem Informasi, vol. 10, no. 3, pp. 753–760, 2021, doi: 10.32520/stmsi.v10i3.1550.

J. Eska, “Penerapan data mining untuk prediksi penjualan wallpaper menggunakan algoritma C4.5,” JURTEKSI(Jurnal Teknologi dan Sistem Informasi), vol. 2, no. 2, pp. 9–13, 2018, doi: 10.31227/osf.io/x6svc.


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