Perbandingan Algoritma K-Nearest Neighbor dan Support Vector Machine Pada Pengenalan Pola Tulisan Tangan

 (*)Adella Widiyanti Mail (Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia)
 Dyah Ayu Megawaty (Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia)

(*) Corresponding Author

Submitted: June 1, 2024; Published: July 26, 2024

Abstract

Handwriting is a biometric characteristic because each person has a unique handwriting pattern. This uniqueness can be utilized as a biometric identity. Handwriting pattern recognition is one of the important fields in document analysis to biometric authentication. This research explores the implementation of K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) algorithms in the context of handwriting pattern recognition. In addition, this research incorporates digital image processing technology by utilizing feature extraction using Gray-Level Co-occurrence Matrix (GLCM). This process involves taking handwriting samples, digitizing them into digital images, and utilizing GLCM to extract texture features. These features play an important role in capturing the unique characteristics of each handwriting pattern. This research was conducted because handwriting has a wide implementation in various fields. In the field of data security, handwriting recognition can be used for data verification in financial transactions and official documents. A comparison of the K-NN and SVM algorithms was conducted to determine the most effective and efficient algorithm in handwriting pattern recognition. These two algorithms are very popular and often used in classification. By comparing these two algorithms, this research aims to evaluate and compare the performance of two classification algorithms in handwriting pattern recognition so as to provide recommendations for implementation in handwriting pattern recognition. The main focus of this research is to investigate the effectiveness and accuracy of the K-NN and SVM algorithms in recognizing and classifying handwriting. K-NN algorithm produces the highest accuracy value of 82.11%, while the SVM algorithm produces the highest accuracy value of 83.87%, so that the SVM algorithm becomes the best algorithm in the classification of handwriting pattern recognition.

Keywords


Handwriting; GLCM; K-NN; SVM; Pattern Recognition

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References

S. Mawaddah and N. Suciati, “Pengenalan Karakter Tulisan Tangan Menggunakan Ekstraksi Fitur Bentuk Berbasis Chain Code,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 4, pp. 683–692, 2020, doi: 10.25126/jtiik.2020742022.

U. Rosyidah and N. Rochmawati, “Analisis Kepribadian Melalui Tulisan Tangan Menggunakan Metode Support Vector Machine,” J. Informatics Comput. Sci., vol. 1, no. 02, pp. 91–96, 2020, doi: 10.26740/jinacs.v1n02.p91-96.

R. Budiawan, A. Ichwani, R. Munir, and D. Mahayana, “Pergeseran Paradigma pada Penelitian Pengenalan Tulisan Tangan Berdasarkan Teori Pemikiran Thomas Kuhn,” J. Filsafat Indones., vol. 6, no. 2, pp. 170–179, 2023.

V. Nomor, D. Nugraha, P. Novantara, A. Muhamad, and K. Kunci, “Implementasi algoritma pca pada pengenalan pola tanda tangan dengan menggunakan bahasa pemrograman phyton,” vol. 6, pp. 7–12, 2021.

A. S. Wijaya, N. Chamidah, and M. M. Santoni, “Pengenalan Karakter Tulisan Tangan Dengan K-Support Vector Nearest Neighbor,” vol. 9, no. 1, pp. 33–44, 2019, doi: 10.22146/ijeis.38729.

N. Putu, L. Santiari, and I. G. S. Rahayuda, “Perbandingan Akurasi Algoritma K-Nearest Neighbors ( KNN ) dan Naive Pattern Search ( NPS ) dalam Website Handwritten Recognition untuk Latihan Menulis Bentuk,” 2023.

K. Ayu Safitri, R. Wulanningrum, K. Kunci -Grafologi, and T. Tangan, “Aplikasi Pengenalan Pola Tulisan Tangan Menggunakan Metode Support Vector Machine,” 2020.

G. Gunawan and Y. Reswan, “Desain Aplikasi Pengenalan Pola Tanda Tangan Menggunakan Metode Support Vector Machine (Svm),” J. Media Infotama, vol. 17, no. 1, pp. 8–12, 2021, doi: 10.37676/jmi.v17i1.1311.

A. Budianto, R. Ariyuana, and D. Maryono, “Perbandingan K-Nearest Neighbor (Knn) Dan Support Vector Machine (Svm) Dalam Pengenalan Karakter Plat Kendaraan Bermotor,” J. Ilm. Pendidik. Tek. dan Kejuru., vol. 11, no. 1, p. 27, 2019, doi: 10.20961/jiptek.v11i1.18018.

V. Oktavia and N. Wijaya, “Pengenalan Tulisan Tangan Huruf Latin Bersambung Menggunakan Local Binary Pattern dan K-Nearest Neighbor,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 7, no. 3, pp. 211–225, 2022, doi: 10.14421/jiska.2022.7.3.211-225.

F. Bimantoro, A. Aranta, G. Satya Nugraha, R. Dwiyansaputra, and A. Yudo Husodo, “Pengenalan Pola Tulisan Tangan Aksara Bima menggunakan Ciri Tekstur dan KNN (Handwriting Recognition of Bima Script using Texture Features and KNN),” J. Comput. Sci. Informatics Eng., vol. 5, no. 1, pp. 60–67, 2021, [Online]. Available: http://jcosine.if.unram.ac.id/

R. Yulianti, I. G. P. S. Wijaya, and F. Bimantoro, “Pengenalan Pola Tulisan Tangan Suku Kata Aksara Sasak Menggunakan Metode Moment Invariant dan Support Vector Machine,” J. Comput. Sci. Informatics Eng., vol. 3, no. 2, pp. 91–98, 2019, doi: 10.29303/jcosine.v3i2.181.

M. A. Amrustian, V. F. Muliati, and E. E. Awal, “Studi Komparasi Metode Machine Learning untuk Klasifikasi Citra Huruf Vokal Hiragana,” J. Media Inform. Budidarma, vol. 5, no. 3, p. 905, 2021, doi: 10.30865/mib.v5i3.3083.

A. N. Wangsaputra, Alfred Louis; Adipranata, Rudy; Tjondrowiguno, “Pengenalan Aksara Jawa dengan Menggunakan Metode Area Based Feature Extraction dan Support Vector Machine,” J. Infra Petra, vol. 7, no. 1, pp. 140–146, 2019.

M. Septiani, “Pengenalan Pola Batik Lampung Menggunakan Metode Principal Component Analysis,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 2, no. 4, pp. 552–558, 2022, doi: 10.33365/jatika.v2i4.1612.

A. Qintan Maharani and T. Ardiansyah, “Kombinasi Metode Multi-Attribute Utility Theory dan Pivot Pairwise Relative Criteria Importance Assessment Dalam Penentuan Lulusan Terbaik,” J. Media Inform. Budidarma, vol. 7, no. 4, pp. 2074–2086, 2023, doi: 10.30865/mib.v7i4.6884.

G. D. Angel and R. Wulanningrum, “Machine Learning untuk Identifikasi Tanda Tangan Menggunakan GLCM dan Euclidean Distance,” Pros. SEMNAS INOTEK (Seminar Nas. Inov. Teknol., vol. 4, no. 1, pp. 297–301, 2020, [Online]. Available: https://proceeding.unpkediri.ac.id/index.php/inotek/article/view/213

J. Wahyudi and I. Maulida, “Pengenalan Pola Citra Kain Tradisional,” Jtiulm, vol. Volume 04, no. Nomor 02, p. (hlm. 43), 2019.

L. Hakim, S. P. Kristanto, D. Yusuf, and F. N. Afia, “Pengenalan Motif Batik Banyuwangi Berdasarkan Fitur Grey Level Co-Occurrence Matrix,” J. Teknoinfo, vol. 16, no. 1, p. 1, 2022, doi: 10.33365/jti.v16i1.1320.

N. Widiastuti, A. Hermawan, and D. Avianto, “Komparasi Algoritma Klasifikasi Data Mining Untuk Prediksi Minat Pencari Kerja,” J. Teknoinfo, vol. 17, no. 1, pp. 1–9, 2023.

B. Baskoro, S. Sriyanto, and L. S. Rini, “Prediksi Penerima Beasiswa dengan Menggunakan Teknik Data Mining di Universitas Muhammadiyah Pringsewu,” Pros. Semin. Nas. Darmajaya, vol. 1, no. 0, pp. 87–94, 2021, [Online]. Available: https://jurnal.darmajaya.ac.id/index.php/PSND/article/view/2918

agus heri yunial, “Analisa Perbandingan Algoritma Klasifikasi Support Vector Machine, Decession Tree Dan Naive Bayes,” Pros. Semin. Inform. Dan Sist. Inf., vol. 5, no. 2, pp. 138–156, 2020, [Online]. Available: http://openjournal.unpam.ac.id/index.php/SNISIS/article/view/9269

A. Rahmat Dian Nugraha, K. Auliasari, and Y. Agus Pranoto, “IMPLEMENTASI METODE K-NEAREST NEIGHBOR (KNN) UNTUK SELEKSI CALON KARYAWAN BARU (Studi Kasus : BFI Finance Surabaya),” JATI (Jurnal Mhs. Tek. Inform., vol. 4, no. 2, pp. 14–20, 2020, doi: 10.36040/jati.v4i2.2656.

A. Purnamawati, M. N. Winnarto, and M. Mailasari, “Analisis Cart (Classification and Regression Trees) Untuk Prediksi Pengguna Sepeda Berdasarkan Cuaca,” J. Teknoinfo, vol. 16, no. 1, p. 14, 2022, doi: 10.33365/jti.v16i1.1478.

Deny Nusyirwan, “Jurnal Teknoinfo,” J. Teknoinfo, vol. 14, pp. 48–58, 2020, [Online]. Available: https://ejurnal.teknokrat.ac.id/index.php/teknoinfo/article/view/336/329

E. Hernando and A. R. Widiarti, “Transliterasi Citra Aksara Bali Daun Lontar Dengan Algoritma Intensity of Character dan Support Vector Machine,” no. 2019, pp. 1–10, 2021.

M. R. Givari, M. R. Sulaeman, and Y. Umaidah, “Perbandingan Algoritma SVM, Random Forest Dan XGBoost Untuk Penentuan Persetujuan Pengajuan Kredit,” Nuansa Inform., vol. 16, no. 1, pp. 141–149, 2022, doi: 10.25134/nuansa.v16i1.5406.

R. Oktafiani and R. Rianto, “Perbandingan Algoritma Support Vector Machine (SVM) dan Decision Tree untuk Sistem Rekomendasi Tempat Wisata,” J. Nas. Teknol. dan Sist. Inf., vol. 9, no. 2, pp. 113–121, 2023, doi: 10.25077/teknosi.v9i2.2023.113-121.

M. R. A. Nasution and M. Hayaty, “Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter,” J. Inform., vol. 6, no. 2, pp. 226–235, 2019, doi: 10.31311/ji.v6i2.5129.

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