Accuracy of classification poisonous or edible of mushroom using naïve bayes and k-nearest neighbors

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Roni Hamonangan
Meidika Bagus Saputro
Cecep Bagus Surya Dinata Karta Atmaja

Abstract

Mushrooms are plants that are widely consumed by the general public, but not all mushrooms can be consumed directly, because the types of mushrooms are feasible and it is still too difficult to distinguish, then there are several ways to identify fungi, namely by means of morphology. The morphology referred to in this paper is the morphology of fungi which includes color, habitat, class, and others. We got the morphology of this mushroom from a datasets we get from UCI Machine Learning with the 23 atribut that we use in the program. In determining the classification of this fungus we use the Naive Bayes algorithm which produces an accuracy of around 90,2% which we then improve again so that it reaches 100% accuracy using the K-Nearest Neighbors algorithm. Furthermore, in this case to prove accuracy  that we had before, we use calculation accuracy with confusion matrix to show it the accuracy of classification poisonous or edible mushroom.

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How to Cite
[1]
R. Hamonangan, M. B. Saputro, and C. B. S. D. K. Atmaja, “Accuracy of classification poisonous or edible of mushroom using naïve bayes and k-nearest neighbors”, JOSCEX, vol. 2, no. 1, pp. 53-60, Mar. 2021.
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References

H. G. Lewis and M. Brown, “A generalized confusion matrix for assessing area estimates from remotely sensed data,” Int. J. Remote Sens., vol. 22, no. 16, pp. 3223–3235, 2001, doi: 10.1080/01431160152558332.

U. Lindequist, T. H. J. Niedermeyer, and W. D. Jülich, “The pharmacological potential of mushrooms,” Evidence-based Complement. Altern. Med., vol. 2, no. 3, pp. 285–299, 2005, doi: 10.1093/ecam/neh107.

A. Srivastava, V. Singh, and G. S. Drall, “Sentiment analysis of twitter data: A hybrid approach,” Int. J. Healthc. Inf. Syst. Informatics, vol. 14, no. 2, pp. 1–16, 2019, doi: 10.4018/IJHISI.2019040101.

Kautsarina, A. N. Hidayanto, B. Anggorojati, Z. Abidin, and K. Phusavat, “Data modeling positive security behavior implementation among smart device users in Indonesia: A partial least squares structural equation modeling approach (PLS-SEM),” Data Br., vol. 30, p. 105588, 2020, doi: 10.1016/j.dib.2020.105588.

R. N. Devita, H. W. Herwanto, and A. P. Wibawa, “Perbandingan Kinerja Metode Naive Bayes dan K-Nearest Neighbor untuk Klasifikasi Artikel Berbahasa indonesia,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 4, p. 427, 2018, doi: 10.25126/jtiik.201854773.

G. Abdurrahman and J. T. Wijaya, “Analisis Klasifikasi Kelahiran Caesar Menggunakan Algoritma Naive Bayes,” JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones., vol. 4, no. 2, p. 46, 2019, doi: 10.32528/justindo.v4i2.2616.

A. De Ramón Fernández, D. Ruiz Fernández, and M. T. Prieto Sánchez, “A decision support system for predicting the treatment of ectopic pregnancies,” Int. J. Med. Inform., vol. 129, pp. 198–204, 2019, doi: 10.1016/j.ijmedinf.2019.06.002.

H. Manik, M. F. G. Siregar, R. Kintoko Rochadi, E. Sudaryati, I. Yustina, and R. S. Triyoga, “Maternal mortality classification for health promotive in Dairi using machine learning approach,” IOP Conf. Ser. Mater. Sci. Eng., vol. 851, no. 1, 2020, doi: 10.1088/1757-899X/851/1/012055.

S. Sutarti, A. T. Putra, and E. Sugiharti, “Comparison of PCA and 2DPCA Accuracy with K-Nearest Neighbor Classification in Face Image Recognition,” Sci. J. Informatics, vol. 6, no. 1, pp. 64–72, 2019, doi: 10.15294/sji.v6i1.18553.

P. Radha and B. Srinivasan, “Predicting Diabetes by cosequencing the various Data Mining Classification Techniques,” .IJISET. vol. 1, no. 6, pp. 334–339, 2014.

N. R. Indraswari and Y. I. Kurniawan, “Aplikasi Prediksi Usia Kelahiran Dengan Metode Naive Bayes,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 9, no. 1, pp. 129–138, 2018, doi: 10.24176/simet.v9i1.1827.

P. Ray and A. Chakrabarti, “Twitter sentiment analysis for product review using lexicon method,” 2017 Int. Conf. Data Manag. Anal. Innov. ICDMAI 2017, pp. 211–216, 2017, doi: 10.1109/ICDMAI.2017.8073512.

A. Al-Thubaity, Q. Alqahtani, and A. Aljandal, “Sentiment lexicon for sentiment analysis of Saudi dialect tweets,” Procedia Comput. Sci., vol. 142, pp. 301–307, 2018, doi: 10.1016/j.procs.2018.10.494.

K. Fukunaga and P. M. Narendra, “A Branch and Bound Algorithm for Computing k-Nearest Neighbors,” IEEE Trans. Comput., vol. C–24, no. 7, pp. 750–753, 1975, doi: 10.1109/T-C.1975.224297.