CLASSIFICATION OF THE GEOCHEMICAL COMPOSITION OF METEORITE OF PUNGGUR (ASTOMULYO) BY k-NEAREST NEIGHBOR ALGORITHM
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Oct 31, 2023
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Abstract
The fall of a meteorite in Astomulyo Village, Punggur, Lampung Province in early 2021 is an interesting topic for further study. This rare object has been suggested to have a unique geochemical composition and a special connection with other meteorites. We aimed to trace its classification by comparing it with other well-known meteorites studied previously. We approach the classification process using the k-nearest neighbor algorithm. The database used 211 represents the geochemical data for each known meteorite group from chemical analyses of meteorites. As a result, we identified that with a k-value = 5 and the proportion of test data 5/95 (in %), the geochemical composition of this meteorite is relatively close to that of the H-type chondrite group with a value accuracy of 91.67%. These results are consistent with the fact that the meteorite of Punggur has a high total iron and metallic composition.
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References
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[6] D. G. Harbowo, B. Priadi, T. Julian, R. N. Amelia, D. . J. P. Sihombing and F. S. Kencana, "A preliminary study on the element abundance in the Hulusimpang Formation, Way Kalianda, Pesawaran, Lampung, Indonesia," in IOP Conference Series: Earth and Environmental Science, 2021.
[7] T. Muliawati, D. G. Harbowo, A. M. F. Lubis, J. D. Turnip, E. R. Irda, A. Azahra and Y. Marito, "k-Means Clustering to Enhance the Petrified Wood Composition Data Analyses and Its Interpretation," Indonesian Journal of Applied Mathematics, vol. 3, no. 1, pp. 26-33, 2023.
[8] M. I. Sultoni, B. Hidayat and A. S. Subandrio, "Classification of igneous rock types through color imagery using local binary pattern and k-nearest neighbor methods," imagery using local binary pattern and k-nearest neighbor methods Telecommunications, Control, Computers, Electrical, and Electronics, vol. 4, no. 1, pp. 10-15, 2019.
[9] S. N. Wibowo, B. Hidayat and J. Arif, "Identification of Igneous Rock Types Looking at Rock Texture Using the Discrete Wavelet Transform (dwt) and K-nearest Neighbor (knn) Methods," in eProceedings of Engineering, 2017.
[10] F. C. Anggian, N. Hidayat and M. T. Furqon, "Technology Development and Computer Science," Journal of Information Technology Development and Computer Science Classification of Volcano Status, vol. 3, no. 12, pp. 11027-11033, 2020.
[11] F. Tempola, M. Muhammad and A. Khairan, "Comparison of Classification Between KNN and Naive Bayes in Determining Volcano Status with K-Fold Cross Validation," Journal of Information Technology and Computer Science, vol. 5, no. 5, pp. 577-584, 2018.
[12] H. J. Kuen, B. Hidayat, and J. Arif, "Identification of Human Age Range Through Molar Tooth Fossils Based on Digital Image Processing Using the Discrete Wavelet Transform and K-nearest Neighbor (k-nn) Methods," in eProceedings of Engineering, 2018.
[13] E. Jarosewich, "Chemical analyses of meteorites: A compilation of stony and iron meteorite analyses," Meteoritics, vol. 25, no. 4, pp. 323-337, 1990.
[14] P. Cunningham and S. J. Delany, "k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)," 2020.
[15] M. Kuhkan, "A method to improve the accuracy of k-nearest neighbor algorithm," International Journal of Computer Engineering and Information Technology, vol. 8, no. 6, p. 90, 2016.
[16] K. Yu, G. D. Guo, J. Li, and S. Lin, "Quantum algorithms for similarity measurement based on Euclidean distance," International Journal of Theoretical Physics, vol. 59, pp. 3134-3144, 2020.
[17] J. T. Townsend, "Theoretical analysis of an alphabetic confusion matrix," Perception & Psychophysics, vol. 9, pp. 40-50, 1971.
[18] K. R. Godfrey, "Correlation methods," Automatica, vol. 16, no. 5, pp. 527-534, 1980.
[19] M. K. Weisberg, T. J. McCoy, and A. N. Krot, "Systematics and evaluation of meteorite classification," Meteorites and the early solar system II, vol. 19, pp. 19-52, 2006.