Implementasi Algoritma Freeman Chain Code dan Algoritma K-Nearest Neighbor Dalam Pengenalan Huruf Mandarin
DOI:
https://doi.org/10.30865/jurikom.v9i4.4532Keywords:
Freeman Chain Code, K-Nearest Neighbor, Feature Extraction, Chinese Characters, Classification, Pre-processingAbstract
Mandarin is the second international language used by the world's population and is the language most studied by students in Indonesia. This research was conducted to determine the success rate of the Freeman Chain Code algorithm and the K-Nearest Neighbor algorithm in mandarin letter recognition. In recognizing Chinese characters, there are several stages that must be passed, namely pre-processing, feature extraction, and letter recognition. The pre-processing stage uses grayscale, Gaussian Blur, binaryization, and thinning. The feature extraction stage uses the Freeman Chain Code Algorithm and the Depth First Search (DFS) Algorithm. The classification stage uses the K-Nearest Neighbor Algorithm and the L1-Metric Algorithm (Manhattan Distance). In this study, there were 10 letter classes with each letter having 100 sample images. The distribution ratio of this research is 70% training data and 30% testing data. This research produces an application that is able to recognize Chinese characters. The success rate resulting from this study was 72% with 216 of the 300 images successfully recognized
References
M. Andriani, “Kajian Peningkatan Minat Belajar Bahasa Mandarin Dengan Media Lagu,†VOX EDUKASI J. Ilm. Ilmu Pendidik., vol. 10, no. 2, pp. 78–83, 2019, doi: 10.31932/ve.v10i2.466.
Ina, “STRATEGI PEMBELAJARAN AKSARA HAN PADA PEMBELAJARAN MANDARIN TINGKAT DASAR,†VOX EDUKASIJurnal Ilm. Ilmu Pendidik., vol. 12, no. 1, pp. 81–93, 2021, doi: https://doi.org/10.31932/ve.v12i1.920.
J. F. Ajao, D. O. Olawuyi, and O. O. Odejobi, “Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier,†J. Teknol. dan Sist. Komput., vol. 6, no. 4, pp. 129–134, 2018, doi: 10.14710/jtsiskom.6.4.2018.129-134.
I. Riadi, A. Fadlil, and P. Annisa, “Identifikasi Tulisan Tangan Huruf Katakana Jepang Dengan Metode Euclidean,†J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 4, no. 1, p. 29, 2020, doi: 10.30645/j-sakti.v4i1.184.
T. M. S. Mulyana and A. Harjoko, “A chinese character recognition method based on population matrix and relational database,†Proc. 2nd Int. Conf. Inf. Commun. Technol. Semin. ICTS 2006, no. 1, pp. 518–523, 2006.
T. M. S. Mulyana, D. Widyaningrum, and H. Herlina, “OCR HURUF JAWA DENGAN FITUR KODE RANTAI DAN LEVENSHTEIN DISTANCE,†Netw. Eng. Res. Oper., vol. 6, no. 1, p. 67, Apr. 2021, doi: 10.21107/nero.v6i1.217.
T. F. Abidin, A. A. AzZuhri, and F. Arnia, “Pengenalan Karakter Plat Nomor Kendaraan Bermotor Menggunakan Zoning dan Fitur Freeman Chain Code,†J. Rekayasa Elektr., vol. 14, no. 1, pp. 19–25, Apr. 2018, doi: 10.17529/jre.v14i1.8932.
T. M. S. Mulyana and Herlina, “Evenly brightening using kurtosis Gaussian pattern to simplify image binarization,†J. Phys. Conf. Ser., vol. 1397, no. 1, p. 012076, Dec. 2019, doi: 10.1088/1742-6596/1397/1/012076.
H. Sunandar, “Perbaikan kualitas Citra Menggunakan Metode Gaussian Filter,†MEANS (Media Inf. Anal. dan Sist., vol. 2, no. 1, pp. 19–22, 2017, doi: 10.54367/means.v2i1.18.
S. S. Sumijan, A. W. Purnama, and S. Arlis, “Peningkatan Kualitas Citra CT-Scan dengan Penggabungan Metode Filter Gaussian dan Filter Median,†J. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 6, pp. 591–600, 2019, doi: 10.25126/jtiik.201966870.
A. Septiarini, K. Kunci, and P. Proyeksi, “Segmentasi Karakter Menggunakan Profil Proyeksi,†J. Inform. Mulawarman Ed. Juli, vol. 7, no. 2, pp. 66–69, 2012, doi: http://dx.doi.org/10.30872/jim.v7i2.88.
Z. Zurnawita and Z. Suar, “Algoritma Image Thinning,†Elektron J. Ilm., vol. 1, no. 1, pp. 29–37, 2009, doi: 10.30630/eji.1.1.7.
A. Herdiansah, R. I. Borman, D. Nurnaningsih, A. A. J. Sinlae, and R. R. Al Hakim, “Klasifikasi Citra Daun Herbal Dengan Menggunakan Backpropagation Neural Networks Berdasarkan Ekstraksi Ciri Bentuk,†JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 2, pp. 388–395, 2022, doi: 10.30865/jurikom.v9i1.3846.
. S., F. Arnia, and R. Muharar, “Pengenalan Aksara Jawi Tulisan Tangan Menggunakan Freemen Chain Code (FCC), Support Vector Machine (SVM) dan Aturan Pengambilan Keputusan,†J. Nas. Tek. Elektro, vol. 5, no. 1, p. 45, 2016, doi: 10.25077/jnte.v5n1.185.2016.
K. L. H., K. Surya, and H. A. Agung, “Optical Character Recognition Citra Kata Kanji Menggunakan Ekstraksi Fitur Algoritma Chain Code Dan Algoritma L1-Metric,†J. Ilm. Tek. Inform., vol. 9, no. 2, p. 112, 2020, doi: http://dx.doi.org/10.22441/format.2020.v9.i2.002.
K. R. R. Wardani, A. Setiawan, and Y. Setiawan, “Penerapan Sistem Pakar Berbasis Aturan dan Metode Freeman Chain Code pada Aplikasi Pengukuran Isi Botol,†J. Telemat., vol. 13, no. 2, pp. 67–72, 2018.
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, p. 683, Aug. 2020, doi: 10.25126/jtiik.2020742022.
S. Rahman and M. Ulfayani, “Perancangan Aplikasi Identifikasi Biometrika Telapak Tangan Menggunakan Metode Freeman Chain Code,†CESS (Journal Comput. Eng. Syst. Sci., vol. 2, no. 2, pp. 64–73, 2017, doi: https://doi.org/10.24114/cess.v2i2.6195.
B. Prasetiyo and M. R. Hidayah, “Penggunaan Metode Depth First Search (DFS) dan Breadth First Search (BFS) pada Strategi Game Kamen Rider Decade Versi 0.3,†Sci. J. Informatics, vol. 1, no. 2, pp. 161–167, 2014, doi: 10.15294/sji.v1i2.4022.
M. Rahmasuci, H. H. S, M. Azizah, P. Wulandari, D. A. A, and S. Bukhori, “Strategi Menemukan Jalan Keluar Labirin dengan Waktu Tercepat Menggunakan Metode DFS,†INFORMAL Informatics J., vol. 3, no. 1, pp. 12–16, 2018, doi: 10.19184/isj.v3i1.9852.
Rismayani and Ardimansyah, “Mobile-Based Public Transportation Search Application for Makassar City Using Depth First Search Algorithm,†J. Pekommas, vol. 18, no. 3, pp. 171–180, 2015, doi: http://dx.doi.org/10.30818/jpkm.2015.1180303.
M. Laia, R. K. Hondro, and T. Zebua, “Implementasi Pengolahan Citra dengan Menggunakan Metode K-Nearest Neighbor Untuk Mengetahui Daging Ayam Busuk dan Daging Ayam Segar,†J. Ris. Komputer), vol. 8, no. 2, pp. 2407–389, 2021, doi: 10.30865/jurikom.v8i2.2818.
M. Nishom, “Perbandingan Akurasi Euclidean Distance, Minkowski Distance, dan Manhattan Distance pada Algoritma K-Means Clustering berbasis Chi-Square,†J. Inform. J. Pengemb. IT, vol. 4, no. 1, pp. 20–24, 2019, doi: 10.30591/jpit.v4i1.1253.
M. Hasibuan, A. S. Sembiring, and R. D. Sianturi, “Penerapan Algoritma K-NN Pada Rekrutment Program Magang Keluar Negeri,†JURIKOM (Jurnal Ris. Komputer), vol. 8, no. 4, pp. 120–127, 2021, doi: 10.30865/jurikom.v8i4.3589.
J. W. Yodha and A. W. Kurniawan, “Pengenalan Motif Batik Menggunakan Deteksi Tepi Canny Dan K-Nearest Neighbor,†Techno.COM, vol. 13, no. 4, November, pp. 251–262, 2014, doi: https://doi.org/10.33633/tc.v13i4.607.
F. S. Pattiiha, “Perbandingan Metode K-NN , Naïve Bayes , Decision Tree untuk Analisis Sentimen Tweet Twitter Terkait Opini Terhadap PT PAL Indonesia,†JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 2, pp. 506–514, 2022, doi: 10.30865/jurikom.v9i2.4016.
R. K. Dinata, H. Akbar, and N. Hasdyna, “Algoritma K-Nearest Neighbor dengan Euclidean Distance dan Manhattan Distance untuk Klasifikasi Transportasi Bus,†Ilk. J. Ilm., vol. 12, no. 2, pp. 104–111, 2020, doi: 10.33096/ilkom.v12i2.539.104-111.
