Classification of marine mammals based on nucleotide using machine earning
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Abstract
This study analyzing the nucleotide of marine mammals using machine learning techniques. Analysis on a nucleotide scale in marine mammals can help facilitate the identification process if done properly. Three types of marine mammals used for nucleotide analysis were Delphinus capensis, Dugong dugon, and Orcaella brevirostris. The solutions offered by machine learning provide a more elegant and effective solution for species identification at the nucleotide scale. This study analyzed the nucleotide s of marine mammals using various classification techniques. Based on this research, it can be concluded that the identification of marine mammals can be done easily based on nucleotide. Different classifiers have been used for analytical purposes such as Random Forest, Decision Tree, Naïve Bayes, K-Nearest Neighbor, and Multilayer Perceptron. Based on the analysis of the results, it was found that the classification method that had been applied had sufficient performance by being tested on several model performance metrics such as accuracy, precision, recall and f1 score. The study also highlights the best classifiers in the various scenarios and recommendations are given.
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How to Cite
Lukman, Tiara Dinda Hapsari, Abdul Malik, Ester Frescilla Simbolon, Ishak Ariawan, & Nadia Yusuf Istiqomah. (2022). Classification of marine mammals based on nucleotide using machine earning. International Journal of Basic and Applied Science, 11(2), 62–70. https://doi.org/10.35335/ijobas.v11i1.90
References
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[2] Z. B. S. Azminuddin I. S. Azis, Machine Learning & Reasoning Fuzzy Logic Algoritma, Manual, Matlab, & Rapid Miner. 2019.
[3] Y. F. Kao and R. Venkatachalam, “Human and Machine Learning,” Computational Economics, vol. 57, no. 3, 2021, doi: 10.1007/s10614-018-9803-z.
[4] J. Soni, U. Ansari, D. Sharma, and S. Soni, “Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction,” International Journal of Computer Applications, vol. 17, no. 8, 2011, doi: 10.5120/2237-2860.
[5] K. Y. Li et al., “An automated machine learning framework in unmanned aircraft systems: New insights into agricultural management practices recognition approaches,” Remote Sensing, vol. 13, no. 16, 2021, doi: 10.3390/rs13163190.
[6] A. T. W. Min, R. Sagarna, A. Gupta, Y. S. Ong, and C. K. Goh, “Knowledge Transfer Through Machine Learning in Aircraft Design,” IEEE Computational Intelligence Magazine, vol. 12, no. 4, 2017, doi: 10.1109/MCI.2017.2742781.
[7] I. K. Nti, A. F. Adekoya, and B. A. Weyori, “A systematic review of fundamental and technical analysis of stock market predictions,” Artificial Intelligence Review, vol. 53, no. 4, 2020, doi: 10.1007/s10462-019-09754-z.
[8] I. Ariawan, A. A. Rosalia, L. Anzani, L. Lukman, and others, “IDENTIFIKASI SPESIES MANGROVE MENGGUNAKAN ALGORITME RANDOM FOREST,” Jurnal Kemaritiman: Indonesian Journal of Maritime, vol. 2, no. 2, pp. 118–128, 2021.
[9] D. Suryanto, “Melihat keanekaragaman organisme melalui beberapa teknik genetika molekuler,” Universitas Sumatera Utara. USU Digital Library. Medan, 2003.
[10] A. M. M. Sequeira et al., “Convergence of marine megafauna movement patterns in coastal and open oceans,” Proc Natl Acad Sci U S A, vol. 115, no. 12, 2018, doi: 10.1073/pnas.1716137115.
[11] R. Sanjuán and P. Domingo-Calap, “Genetic Diversity and Evolution of Viral Populations,” in Encyclopedia of Virology, 2021. doi: 10.1016/b978-0-12-809633-8.20958-8.
[12] D. Dharmadi, R. Faizah, and N. N. Wiadnyana, “FREKUENSI PEMUNCULAN, TINGKAH LAKU, DAN DISTRIBUSI MAMALIA LAUT DI LAUT SAWU, NUSA TENGGARA TIMUR,” BAWAL Widya Riset Perikanan Tangkap, vol. 3, no. 3, 2017, doi: 10.15578/bawal.3.3.2010.209-216.
[13] Z. Gao, Y. Liu, X. Wang, X. Wei, and J. Han, “DNA Mini-Barcoding: A Derived Barcoding Method for Herbal Molecular Identification,” Frontiers in Plant Science, vol. 10. 2019. doi: 10.3389/fpls.2019.00987.
[14] A. R. Simbolon and L. P. Aji, “IDENTIFIKASI MOLEKULAR DAN STRUKTUR FILOGENETIK MOLUSKA (GASTROPODA DAN BIVALVIA) DI PERAIRAN BIAK, PAPUA,” BAWAL Widya Riset Perikanan Tangkap, vol. 13, no. 1, pp. 11–21, 2021.
[15] G. Rizk, D. Lavenier, and R. Chikhi, “DSK: K-mer counting with very low memory usage,” Bioinformatics, vol. 29, no. 5, 2013, doi: 10.1093/bioinformatics/btt020.
[16] B. Chor, D. Horn, N. Goldman, Y. Levy, and T. Massingham, “Genomic DNA k-mer spectra: Models and modalities,” Genome Biology, vol. 10, no. 10, 2009, doi: 10.1186/gb-2009-10-10-r108.
[17] F. K. S. Dewi, “KLASIFIKASI BERITA MENGGUNAKAN METODE MULTINOMIAL NAÏVE BAYES,” SCAN - Jurnal Teknologi Informasi dan Komunikasi, vol. 16, no. 3, 2021, doi: 10.33005/scan.v16i3.2870.
[18] M. T. H. Bunga, B. S, Djahi, and Y. Y. Nabuasa, “Multinomial Naive Bayes Untuk Klasifikasi Status Kredit Mitra Binaan Di Pt . Angkasa Pura I Program Kemitraan,” J-Icon, vol. 6, no. 2, 2018.
[19] D. Sartika and D. I. Sensuse, “Perbandingan Algoritma Klasifikasi Naive Bayes, Nearest Neighbour, dan Decision Tree pada Studi Kasus Pengambilan Keputusan Pemilihan Pola Pakaian,” Jurnal Teknik Informatika Dan Sistem Informasi, vol. 1, no. 2, 2017.
[20] Y. Y. Song and Y. Lu, “Decision tree methods: applications for classification and prediction,” Shanghai Archives of Psychiatry, vol. 27, no. 2, 2015, doi: 10.11919/j.issn.1002-0829.215044.
[21] N. Ye, Data mining Theories, Algorithms and Examples, vol. 16, no. 4. 1997.
[22] R. A. Putranto, T. Wuryandari, and Sudarno, “Perbandingan Analisis Klasifikasi antara Decision Tree dan Support Vector Machine Multiclass untuk Penentuan Jurusan pada Siswa SMA,” Jurnal Gaussian, vol. 4, no. 4, 2015.
[23] H. Santoso, “Performa Random Forest Group untuk Klasifikasi Penyakit Busuk Pangkal Batang yang Disebabkan oleh Ganoderma boninense pada Perkebunan Kelapa Sawit,” Jurnal Penelitian Kelapa Sawit, vol. 28, no. 3, pp. 133–146, 2020.
[24] A. Cutler, D. R. Cutler, and J. R. Stevens, “Random forests,” in Ensemble machine learning, Springer, 2012, pp. 157–175.
[25] M. Lestari, “Penerapan Algoritma Klasifikasi Nearest Neighbor (K-NN) untuk Mendeteksi Penyakit Jantung,” Faktor Exacta, vol. 7, no. September 2010, 2014.
[26] A. S. Shirazi and I. Frigaard, “Slurrynet: Predicting critical velocities and frictional pressure drops in oilfield suspension flows,” Energies (Basel), vol. 14, no. 5, 2021, doi: 10.3390/en14051263.
[27] W. Castro, J. Oblitas, R. Santa-Cruz, and H. Avila-George, “Multilayer perceptron architecture optimization using parallel computing techniques,” PLoS ONE, vol. 12, no. 12, 2017, doi: 10.1371/journal.pone.0189369.
[28] B. Juba and H. S. Le, “Precision-Recall versus accuracy and the role of large data sets,” 2019. doi: 10.1609/aaai.v33i01.33014039.
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