Pengenalan Tulisan Tangan Huruf Latin Bersambung Menggunakan Local Binary Pattern dan K-Nearest Neighbor
DOI:
https://doi.org/10.14421/jiska.2022.7.3.211-225Keywords:
Latin Letters, Cursive, K-Nearest Neighbor, Local Binary Pattern, K-Fold Cross Validation, HandwrittenAbstract
There are 26 Latin letters in Indonesia, 5 of which are vowels and 21 consonants. This study will translate handwriting with a Latin object using the K-Nearest Neighbor method with the Local Binary Pattern extension. The research is being done with a focus on experimentation using a few methods that have already been discussed. Concatenated Latin letters have a few variations that depend on the work's author, so research will be conducted to identify cursive Latin letters based on these variations. Each of the 30 respondents wrote 26 capital letters and 26 lowercase letters on paper, which was then scanned to provide the image data. Black, blue, and red pens were used to write by every ten responders. The recognition procedure is broken into two halves, capital and non-capital letter recognition using 780 picture datasets each. In the study, k-fold cross-validation is used, with k = 6. The best value was reached at k = 7 with 29.49 percent accuracy, 33.88 percent precision, recall 33.46 percent, and F1-score 27.65 percent according to the research utilizing KNN with values k = 3, 5, and 7. and for recognizing non-capital characters, the best result was found at k=3 with accuracy, precision, recall, and F1-score of 26.28, 27.27, and 22.7%, respectively.
References
Al Rivan, M. E., Devella, S., & Saputra, J. (2020). Pengenalan Iris Dengan Normalisasi Menggunakan LBP dan RBF. Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer Dan Teknologi Informasi, 6(2), 122. https://doi.org/10.24014/coreit.v6i2.9685
Andana, A., Widyati, R., & Irzal, M. (2018). Pengenalan Citra Tulisan Tangan Dengan Metode Backpropagation. Jurnal Matematika Terapan, 2(1), 36–44.
Anggraeny, F. T., Munir, M. S., & Purbasari, I. Y. (2020). Histogram Profil Proyeksi sebagai Metode Ekstraksi Fitur pada Pengenalan Karakter Tulisan Tangan. Prosiding Seminar Nasional Informatika Bela Negara, 1, 164–168. https://doi.org/10.33005/santika.v1i0.44
Aranta, A., Bimantoro, F., & Putrawan, I. P. T. (2020). Penerapan Algoritma Rule Base dengan Pendekatan Hexadesimal pada Transliterasi Aksara Bima Menjadi Huruf Latin. Jurnal Teknologi Informasi, Komputer, Dan Aplikasinya (JTIKA ), 2(1), 130–141. https://doi.org/10.29303/jtika.v2i1.96
Bimantoro, F., Aranta, A., Nugraha, G. S., Dwiyansaputra, R., & Husodo, A. Y. (2021). Pengenalan Pola Tulisan Tangan Aksara Bima menggunakan Ciri Tekstur dan KNN. Journal of Computer Science and Informatics Engineering (J-Cosine), 5(1), 60–67. https://doi.org/10.29303/jcosine.v5i1.387
Cahyani, S., Wiryasaputra, R., & Gustriansyah, R. (2018). Identifikasi Huruf Kapital Tulisan Tangan Menggunakan Linear Discriminant Analysis dan Euclidean Distance. Jurnal Sistem Informasi Bisnis, 8(1), 57. https://doi.org/10.21456/vol8iss1pp57-67
Hidayat, R., & Minati, S. (2019). Comparative Analysis of Text Mining Classification Algorithms for English and Indonesian Qur’an Translation. IJID (International Journal on Informatics for Development), 8(1), 47. https://doi.org/10.14421/ijid.2019.08108
Ilham, F., & Rochmawati, N. (2020). Transliterasi Aksara Jawa Tulisan Tangan ke Tulisan Latin Menggunakan CNN. Journal of Informatics and Computer Science (JINACS), 1(04), 200–208. https://doi.org/10.26740/jinacs.v1n04.p200-208
Maharani, D., Efendi, R., & Johar, A. (2019). Penerapan Augmented Reality Sebagai Media Pembelajaran Pengenalan Aksara Korea (Hangul). Jurnal Rekursif, 7(1), 77–90. https://doi.org/10.33369/rekursif.v7i1.6320
Masrani, H., Ruslianto, I., & Ilhamsyah. (2018). Aplikasi Pengenalan Pola Pada Huruf Tulisan Tangan Menggunakan Jaringan Saraf Tiruan Dengan Metode Ekstraksi Fitur Geometri. Jurnal Coding, Sistem Komputer Untan, 06(02), 69–78. https://doi.org/10.26418/coding.v6i2.26674
Maulidah, M., Windu Gata, Rizki Aulianita, & Cucu Ika Agustyaningrum. (2020). ALGORITMA KLASIFIKASI DECISION TREE UNTUK REKOMENDASI BUKU BERDASARKAN KATEGORI BUKU. E-Bisnis : Jurnal Ilmiah Ekonomi Dan Bisnis, 13(2), 89–96. https://doi.org/10.51903/e-bisnis.v13i2.251
Nikmatun, I. A., & Waspada, I. (2019). Implementasi Data Mining untuk Klasifikasi Masa Studi Mahasiswa Menggunakan Algoritma K-Nearest Neighbor. Jurnal SIMETRIS, 10(2), 421–432. https://doi.org/10.24176/simet.v10i2.2882
Pangestu, R. A., Rahmat, B., & Anggraeny, F. T. (2020). Implementasi Algoritma Cnn Untuk Klasifikasi Citra Lahan Dan Perhitungan Luas. Jurnal Informatika Dan Sistem Informasi (JIFoSI), 1(1), 166–174. https://doi.org/10.33005/jifosi.v1i1.5
Prihatiningsih, S., M, N. S., Andriani, F., & Nugraha, N. (2019). Analisa Performa Pengenalan Tulisan Tangan Angka Berdasarkan Jumlah Iterasi Menggunakan Metode Convolutional Neural Network. Jurnal Ilmiah Teknologi Dan Rekayasa, 24(1), 58–66. https://doi.org/10.35760/tr.2019.v24i1.1934
Purbayanti, T. S. (2018). Pengenalan Tulisan Tangan Huruf Latin Dengan Menggunakan Metode K-Nearest Neighbour. Simki-Techsain, 02(02), 3–10.
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