Main Article Content

Fathur Rohman
Adiwijaya Adiwijaya
Dody Qori Utama


Cases of poisonous snake bites around the world are estimated to occur around 421,000 cases and 20,000 of them die every year. Identifying snake bite marks on victims will greatly help the medical team in handling victims of snake bites and will avoid fatal errors such as the death of the victim. This research will try to create a system that can classify snake bites images. The system has been built using the extraction method Local Binary Pattern (LBP) and Naive Bayes. Parameter r is a radius, while paramter P is the number of neighbor . The best result of this system has accuracy 83.33%, precision 1.00, recall 0.75, and F1 Score 0.86,parameter that used are r = 1 with P = 8 and r = 3 with P = 16. The dataset used has 20 data, the data divided into 14 training data and 6 testing data.

Article Details

How to Cite
Rohman, F., Adiwijaya, A., & Utama, D. (2019). KLASIFIKASI GIGITAN ULAR MENGGUNAKAN LOCAL BINARY PATTERN DAN NAÏVE BAYES. JURNAL TEKNOLOGIA, 2(1). Retrieved from https://aperti.e-journal.id/teknologia/article/view/34


[1] E. Alirol, S. K. Sharma, H. S. Bawaskar, U. Kuch, and F. Chappuis. Snake bite in south asia: a review. PLoS neglected tropical diseases, 4(1):e603, 2010.
[2] A. H. R. Z. Arifin, M. S. Mubarok, and A. Adiwijaya. Learning struktur bayesian networks menggunakan novel modified binary differential evolution pada klasifikasi data. In Indonesia Symposium on Computing (IndoSC) 2016, 2016..
[3] R. A. Aziz, M. S. Mubarok, and A. Adiwijaya. Klasifikasi topik pada lirik lagu dengan metode multinomial naive bayes. In Indonesia Symposium on Computing (IndoSC) 2016, 2016.
[4] B. S. Gold, R. C. Dart, and R. A. Barish. Bites of venomous snakes. New England Journal of Medicine, 347(5):347–356, 2002.
[5] Z. Guo, L. Zhang, and D. Zhang. A completed modeling of local binary pattern operator for texture classification. IEEE transactions on image processing, 19(6):1657–1663, 2010.
[6] N. Hernawati, D. Utama, et al. Image processing for snake indentification based on bite using local binarypattern and support vector machine method. In Journal of Physics: Conference Series, volume 1192, page 012007. IOP Publishing, 2019.
[7] A. James. Snake classification from images. PeerJ Preprints, 5:e2867v1, 2017.
[8] G. H. John and P. Langley. Estimating continuous distributions in bayesian classifiers. In Proceedings of the Eleventh conference on Uncertainty in artificial intelligence, pages 338–345. Morgan Kaufmann Publishers Inc., 1995.
[9] A. Kasturiratne, A. R. Wickremasinghe, N. de Silva, N. K. Gunawardena, A. Pathmeswaran, R. Premaratna, L. Savioli, D. G. Lalloo, and H. J. de Silva. The global burden of snakebite: a literature analysis and modelling based on regional estimates of envenoming and deaths. PLoS medicine, 5(11):e218, 2008.
[10] W. Lou, X. Wang, F. Chen, Y. Chen, B. Jiang, and H. Zhang. Sequence based prediction of dna-binding proteins based on hybrid feature selection using random forest and gaussian naive bayes. PloS one, 9(1):e86703, 2014.
[11] Y. Luo, C.-m. Wu, and Y. Zhang. Facial expression recognition based on fusion feature of pca and lbp with svm. Optik-International Journal for Light and Electron Optics, 124(17):2767–2770, 2013.
[12] M. S. Mubarok, Adiwijaya, and M. D. Aldhi. Aspect-based sentiment analysis to review products using naïve bayes. In AIP Conference Proceedings, volume 1867, page 020060. AIP Publishing, 2017.
[13] M. S. Mubarok, A. Adiwijaya, et al. Klasifikasi multi-label pada topik ayat qur’an terjemahan bahasa inggris menggunakan tree augmented na¨ıve bayes (tan). eProceedings of Engineering, 5(1), 2018.
[14] S. D. A. Nishioka, P. V. P. Silveira, and F. A. Bauab. Bite marks are useful for the differential diagnosis of snakebite in brazil. Wilderness & environmental medicine, 6(2):183–188, 1995.
[15] R. A. Pane, M. S. Mubarok, N. S. Huda, et al. A multi-lable classification on topics of quranic verses inenglish translation using multinomial naive bayes. In 2018 6th International Conference on Information andCommunication Technology (ICoICT), pages 481–484. IEEE, 2018.
[16] M. D. Purbolaksono, K. C. Widiastuti, M. S. Mubarok, F. A. Ma’ruf, et al. Implementation of mutual information and bayes theorem for classification microarray data. In Journal of Physics: Conference Series, volume 971, page 012011. IOP Publishing, 2018.
[17] R. M. Putra, D. Q. Utama, et al. Snake bite classification using chain code and k nearest neighbour. In Journal of Physics: Conference Series, volume 1192, page 012015. IOP Publishing, 2019.
[18] Y. Sasaki et al. The truth of the f-measure. Teach Tutor mater, 1(5):1–5, 2007.
[19] H. Zhang. The optimality of naive bayes. AA, 1(2):3, 2004.