Klasifikasi Bintang RR Lyrae / Cepheid / Mira dari The All-Sky Automated Survey for Supernovae Menggunakan Algoritma Naïve Bayes

Authors

  • Ayu Lestari Hasanuddin University
  • Nur Hasanah Hasanuddin University
  • Eko Juarlin Hasanuddin University

DOI:

https://doi.org/10.24114/jiaf.v8i2.31947

Keywords:

fisika, astrofisika, cepheid, mira, naïve bayes, rr lyrae

Abstract

Penelitian ini menggunakan 58.423 data dari the All-Sky Automated Survey for Supernovae (ASAS-SN) untuk melakukan klasifikasi bintang RR Lyrae, Cepheid, dan Mira menggunakan pendekatan machine learning. Terdapat sembilan kolom yang dijadikan atribut dalam pembuatan model machine learning, yaitu: raj2000, dej2000, l, b, mean_vmag, amplitude, period, lksl_statistic, dan parallax dengan kolom variable_type digunakan sebagai target label. Dengan memanfaatkan training dataset (data latih) dan testing dataset (data uji), algoritma Naïve Bayes yang digunakan pada penelitian ini menghasilkan akurasi sebesar 98.6%. Sedangkan berdasarkan hasil evaluasi menggunakan confusion matrix, diperoleh presisi dari bintang RR Lyrae, Cepheid, dan Mira masing-masing sebesar 99%, 87%, dan 99%. Recall dari ketiga objek masing-masing adalah 99%, 88%, dan 99%, sedangkan nilai f1-score masing-masing sebesar 98%, 90%, dan 100%. Kesimpulan dari penelitian ini adalah algoritma Naïve Bayes dapat digunakan dalam klasifikasi objek astronomi dengan tingkat akurasi yang baik.

References

Bustami. (2014). Penerapan Algoritma Naïve Bayes untuk Mengklasifikasi Data Nasabah Asuransi. TECHSI: Jurnal Penelitian Teknik Informatika, 8(1):127-146.

Clarke, A.O., Scaife, A.M.M., Greenhalgh, R. & Griguta, V. (2020). Identifying Galaxies, Quasars, and Stars with Machine Learning: A new catalogue of Classifications for 111 Million SDSS Sources Without Spectra. Astronomy & Astrophysics.

Čokina, M., Krešňáková, V.M., Butka, P. & Parimucha, Š. (2021). Automatic Classification of Eclipsing Binary Stars Using Deep Learning Methods. Astronomy and Computing, 36.

Jayasinghe, T., Kochanek, C.S., Stanek, K.Z., Shappee, B.J., Holoien, T.W.-S., Thompson, T.A., Prieto, J.L., Dong, S., Pawlak, M., Pejcha, O., Pojmanski, G., Otero, S., Hurst, N. & Will, D. (2021). The ASAS-SN Catalog of Variable Stars IX: The Spectroscopic Properties of Galactic Variable Stars. Monthly Notices of the Royal Astronomical Society, 503(1):200-235.

Jayasinghe, T., Stanek, K.Z., Kochanek, C.S., Shappee, B.J., Holoien, T.W.-S., Thompson, T.A., Prieto, J.L., Dong, S., Pawlak, M., Pejcha, O., Shields, J.V., Pojmanski, G., Otero, S., Hurst, N., Britt, C.A. & Will, D. (2020). The ASAS-SN Catalogue of Variable Stars “ V. Variables in the Southern Hemisphere. Monthly Notices of the Royal Astronomical Society, 491(1).

Martin, G., Kaviraj, S., Hocking, A., Read, S.C. & Geach, J.E. (2019). Galaxy Morphological Classification in Deep-Wide Surveys via Unsupervised Machine Learning. Monthly Notices of the Royal Astronomical Society, 491:1408-1426.

Percy, J.R. (2014). Variable Stars: Action in the Sky!. Diktat, Departemen Astronomi dan Astrofisika, Universitas Toronto, Kanada.

Putra, J.W.G. (2020). Pengenalan Konsep Pembelajaran Mesin dan Deep Learning Edisi 1.4.

Rosandy, T. (2016). Perbandingan Metode Naïve Bayes Classifier Dengan Metode Decision Tree (C4.5) Untuk Menganalisa Kelancaran Pembiayaan (Study Kasus: KSPPS/BMT AL-FADHILA). Jurnal TIM Darmajaya, 2(1):52-62.

Setiawan, E. (2014). Analisis Penggunaan Kernel Density Estimation pada Metode Loss Distribution Approach untuk Risiko Operasional. Tesis, Program Magister Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Indonesia, Depok.

Sun, N., Sun, B., Lin, J. & Wu, M.Y.C. (2018). Lossless Pruned Naïve Bayes for Big Data Classifications. Big Data Res.

Xiao-Qing, W. & Jin-Meng, Y. (2020). Classification of Star/Galaxy/QSO and Star Spectral Type from LAMOST Data Release 5 with Machine Learning Approaches. Chinese Journal of Physics. https://doi.org/10.1016/j.cjph.2020.03.008.

VizieR. Catalog. Diakses 28 September 2021, dari https://vizier.u-strasbg.fr/viz-bin/VizieR?-source=J/AJ/137/4186.

Downloads

Published

2023-05-16

Issue

Vol. 8 No. 2 (2022): JURNAL IKATAN ALUMNI FISIKA UNIMED EDISI APRIL - JUNI 2022

Section

Articles

License

Copyright (c) 2023 JURNAL IKATAN ALUMNI FISIKA UNIVERSITAS NEGERI MEDAN

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

    1. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  1. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).