Classification of Volcanic Status Events Using Autocorrelation and Support Vector Machine Methods

Authors

  • Fridy Mandita Universitas 17 Agustus 1945 Surabaya
  • Muhammad Arif Fajriyansah Universitas 17 Agustus 1945 Surabaya

DOI:

https://doi.org/10.30996/jitcs.133023

Keywords:

autocorrelation, seismic signal classification, support vector machine, volcano

Abstract

Volcanic eruption disasters occur frequently in Indonesia due to the high density of active volcanoes, posing persistent risks to surrounding communities and infrastructure. Effective mitigation of these hazards is challenged by limitations in monitoring systems, particularly related to instrumentation coverage and the availability of expert human resources. One critical aspect of volcanic monitoring is the accurate classification of seismic activity, which reflects subsurface volcanic processes and supports timely hazard assessment. This study addresses the challenge of reliably classifying volcanic seismic events by proposing an integrated framework that combines autocorrelation-based signal characterization with Support Vector Machine (SVM)–based multi-class classification, supported by Z-score normalization during data preprocessing. The framework is designed to enhance feature consistency and robustness against noise commonly present in volcanic seismic signals. To evaluate its effectiveness, three SVM kernel functions—linear, polynomial, and radial basis function (RBF)—are systematically assessed under identical experimental conditions. The results demonstrate that the polynomial SVM kernel with a degree of two provides the most reliable classification performance, achieving an accuracy of 0.9605. In addition, the application of Z-score normalization substantially improves model stability and overall performance across all kernel configurations, indicating that feature scaling plays a critical role in SVM-based seismic classification. Performance variations among kernels suggest that non-linear feature representations are better suited to capture the complex characteristics of volcanic seismic signals, while classification errors are primarily influenced by class imbalance in underrepresented event types. These findings indicate that the proposed framework effectively supports automated volcanic seismic signal analysis and has the potential to enhance the reliability of seismic-based volcanic activity monitoring.

Downloads

Download data is not yet available.

Author Biographies

Fridy Mandita, Universitas 17 Agustus 1945 Surabaya

Department of Robotics and Artificial Intelligence

Muhammad Arif Fajriyansah, Universitas 17 Agustus 1945 Surabaya

Department of Informatics Engineering

References

Alzubi, J., Nayyar, A., & Kumar, A. (2018). Machine Learning from Theory to Algorithms: An Overview. Journal of Physics: Conference Series, 1142. doi:https://doi.org/10.1088/1742-6596/1142/1/012012

Ambarwari, A., Adrian, Q. J., & Herdiyeni, Y. (2020). Analysis of the Effect of Data Scaling on the Performance of the Machine Learning Algorithm for Plant Identification. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(1), 117-122. doi:https://doi.org/10.29207/resti.v4i1.1517

Anggian, F. C., Hidayat, N., & Furqon, M. T. (2020). Implementasi Metode Modified K-Nearest Neighbor untuk Klasifikasi Status Gunung Berapi. JPTIIK (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer), 3(12), 11027-11033. Retrieved January 29, 2026, from https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/6843

Bergen, K. J., Johnson, P. A., Hoop, M. V., & Beroza, G. C. (2019). Machine learning for data-driven discovery in solid Earth geoscience. Science, 363(6433). doi:https://doi.org/10.1126/science.aau0323

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York: Springer.

Chouet, B. A., & Matoza, R. S. (2013). A multi-decadal view of seismic methods for detecting precursors of magma movement and eruption. Journal of Volcanology and Geothermal Research, 252, 108-175. doi:https://doi.org/10.1016/j.jvolgeores.2012.11.013

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning(20), 273–297. doi:https://doi.org/10.1007/BF00994018

Gibbons, S. J., & Ringdal, F. (2006). The detection of low magnitude seismic events using array-based waveform correlation. Geophysical Journal International, 165(1), 149–1667. doi:https://doi.org/10.1111/j.1365-246X.2006.02865.x

Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. Morgan Kaufmann.

Handayanto, A., Latifa, K., Saputro, N. D., & Waliyansyah, R. R. (2019). Analisis dan Penerapan Algoritma Support Vector Machine (SVM) dalam Data Mining untuk Menunjang Strategi Promosi. JUITA: Jurnal Informatika, 7(2), 71–79. doi:https://doi.org/10.30595/juita.v7i2.4378

Karo, I. M., & Hendriyana, H. (2022). Klasifikasi Penderita Diabetes menggunakan Algoritma Machine Learning dan Z-Score. Jurnal Teknologi Terpadu, 8(2), 94–99. doi:https://doi.org/10.54914/jtt.v8i2.564

Lara-Cueva, R., Benítez, D. S., Paillacho, V., Villalva, M., & Rojo-Álvarez, J. L. (2018). On the use of multi-class support vector machines for classification of seismic signals at Cotopaxi volcano. 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). Ixtapa, Mexico: IEEE. doi:https://doi.org/10.1109/ROPEC.2017.8261613

Malfante, M., Mura, M. D., Mars, J. I., Métaxian, J.-P., Macedo, O., & Inza, A. (2018). Automatic Classification of Volcano Seismic Signatures. Journal of Geophysical Research: Solid Earth, 123(12), 10,645-10,658. doi:https://doi.org/10.1029/2018JB015470

Manley, G. F., Mather, T. A., Pyle, D. M., Clifton, D. A., Rodgers, M., Thompson, G., & Londo, J. M. (2022). A Deep Active Learning Approach to the Automatic Classification of Volcano-Seismic Events. Frontiers in Earth Science, 10. doi: https://doi.org/10.3389/feart.2022.807926

Marzocchi, W., Selva, J., & Jordan, T. H. (2021). A unified probabilistic framework for volcanic hazard and eruption forecasting. Natural Hazards and Earth System Sciences, 21(11), 3509–3517. doi:https://doi.org/10.5194/nhess-21-3509-2021

McNutt, S. R. (2025). Volcanic seismology. Annual Review of Earth and Planetary Sciences, 33, 461-491. doi:https://doi.org/10.1146/annurev.earth.33.092203.122459

Meier, M.-A., Ross, Z. E., Ramachandran, A., Balakrishna, A., Nair, S., Kundzicz, P., . . . Yue, Y. (2019). Reliable Real-Time Seismic Signal/Noise Discrimination With Machine Learning. Journal of Geophysical Research: Solid Earth, 124(1), 788-800. doi:https://doi.org/10.1029/2018JB016661

Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., & Beroza, G. C. (2020). Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature Communications, 11. doi:https://doi.org/10.1038/s41467-020-17591-w

Perdana, R. S., Fatichah, C., & Purwitasari, D. (2015). Pemilihan Kata Kunciuntuk Deteksi Kejadian Trivial pada Dokumen Twitter Menggunakan Autocorrelation Wavelet Coefficients. JUTI: Jurnal Ilmiah Teknologi Informasi, 13(2), 152–159. doi:https://doi.org/10.12962/j24068535.v13i2.a484

Rahutomo, F., Saputra, P. Y., & Fidyawan, M. A. (2018). Implementasi Twitter Sentiment Analysis untuk Review Film Menggunakan Algoritma Support Vector Machine. Jurnal Informatika Polinema, 4(2), 93-100. doi:https://doi.org/10.33795/jip.v4i2.152

Ririh, K. R., Laili, N., Wicaksono, A., & Tsurayya, S. (2020). Studi Komparasi dan Analisis Swot pada Implementasi Kecerdasan Buatan (Artificial Intelligence) di Indonesia. J@ti Undip: Jurnal Teknik Industri, 15(2), 122-133. Retrieved January 28, 2026, from https://ejournal.undip.ac.id/index.php/jgti/article/view/29183

Ross, Z. E., Meier, M.‐A., Hauksson, E., & Heaton, T. H. (2018). Generalized Seismic Phase Detection with Deep Learning. Bulletin of the Seismological Society of America, 108(5A)), 2894–2901. doi:https://doi.org/10.1785/0120180080

Schölkopf, B., & Smola, A. J. (2001). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press.

Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97. doi:https://doi.org/10.1016/j.asoc.2019.105524

Tang, L., Zhang, M., & Wen, L. (2020). Support Vector Machine Classification of Seismic Events in the Tianshan Orogenic Belt. Journal of Geophysical Research: Solid Earth, 125(1). doi:https://doi.org/10.1029/2019JB018132

Tempola, F., Muhammad, M., & Khairan, A. (2018). Comparison of Classification between KNN and Naive Bayes at the Determination of the Volcanic Status with k-Kold Cross Validation. Jurnal Teknologi Informasi dan Ilmu Komputer, 5(5), 577-584. doi:https://doi.org/10.25126/jtiik.201855983

Thelen, W. A., Matoza, R. S., & Hotovec-Ellis, A. J. (2022). Trends in volcano seismology: 2010 to 2020 and beyond. Bulletin of Volcanology, 84. doi:https://doi.org/10.1007/s00445-022-01530-2

Titos, M., Bueno, A., García, L., Benítez, M. C., & Ibañez, J. (2019). Detection and Classification of Continuous Volcano-Seismic Signals With Recurrent Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 57(4), 1936-1948. doi:https://doi.org/10.1109/TGRS.2018.2870202

Vapnik, V. N. (1998). Statistical Learning Theory. John Wiley & Sons.

Downloads

Published

2026-02-02

How to Cite

Mandita, F., & Fajriyansah, M. A. (2026). Classification of Volcanic Status Events Using Autocorrelation and Support Vector Machine Methods. Journal of Information Technology and Cyber Security, 4(1), 26–40. https://doi.org/10.30996/jitcs.133023

Issue

Vol. 4 No. 1 (2026): January

Section

Research Article

License

Copyright (c) 2026 The Author(s)

Creative Commons License

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

 

Copyright Notice based on COPE (Committee on Publication Ethics) for JITCS: Journal of Information Technology and Cyber Security

  1. Ownership and Copyright:

    1. JITCS: Journal of Information Technology and Cyber Security respects the intellectual property rights of authors. The copyright for individual articles published in JITCS is retained by the respective authors, unless otherwise specified.
    2. The articles published in JITCS are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial, and no modifications or adaptations are made.
    3. JITCS serves as the initial publisher of the articles, providing them with the first publication platform.

  2. Permissions and Usage:

    1. Distribution for Non-Commercial Purposes: Permitted: Users are allowed to distribute the article for non-commercial purposes, provided the original work is properly cited and no modifications or adaptations are made.
    2. Distribution for Commercial Purposes: Not Permitted: The article may not be distributed for any commercial purposes without obtaining prior written permission from the author(s).
    3. Inclusion in a Collective Work (e.g., Anthology) for Non-Commercial Purposes: Permitted: Users are allowed to include the article in a collective work, such as an anthology, as long as the use is non-commercial and the work remains unchanged.
    4. Inclusion in a Collective Work for Commercial Purposes: Not Permitted: The article may not be included in any collective work or anthology intended for commercial purposes without prior permission from the author(s).
    5. Creation and Distribution of Revised Versions, Adaptations, or Derivative Works (e.g., Translation) for Non-Commercial Purposes: Not Permitted: Users may not create or distribute revised versions, adaptations, or derivative works, including translations, for non-commercial purposes.
    6. Creation and Distribution of Revised Versions, Adaptations, or Derivative Works for Commercial Purposes: Not Permitted: Users may not create or distribute revised versions, adaptations, or derivative works, including translations, for commercial purposes.
    7. Text or Data Mining for Non-Commercial Purposes: Permitted: Users are permitted to engage in text or data mining of the article for non-commercial research purposes, provided the original work is properly attributed.
    8. Text or Data Mining for Commercial Purposes: Not Permitted: Users may not engage in text or data mining of the article for commercial purposes without obtaining explicit permission from the author(s).

  3. Attribution and Citation:

    1. Proper attribution and citation of the published work should be provided when using or referring to content from JITCS. This includes clearly indicating the authors, the title of the article, the journal name (JITCS), the volume/issue number, the publication year, and the article's DOI (Digital Object Identifier) when available.
    2. When adapting or modifying the published content, proper attribution to the original source should be given, and the adapted or modified content should be shared under the same CC BY-NC-ND 4.0 license.

  4. Plagiarism and Copyright Infringement:

    1. JITCS considers plagiarism and copyright infringement as serious ethical violations. Authors are responsible for ensuring that their submitted work is original and does not infringe upon the copyright or intellectual property rights of others.
    2. Any allegations of plagiarism or copyright infringement will be investigated promptly and thoroughly. If proven, appropriate actions, including rejection of the manuscript, retraction of the published article, or other corrective measures, will be taken.

  5. Open Access Licensing:

    1. JITCS supports open access publishing and encourages authors to consider publishing their work under the CC BY-NC-ND 4.0 license to promote the dissemination and use of knowledge in the field of information technology and cyber security.
    2. The specific terms and conditions of the CC BY-NC-ND 4.0 license will be clearly indicated on the published articles.

  6. Policy Review: This Copyright Notice will be periodically reviewed and updated to ensure its continued relevance and compliance with copyright laws, ethical standards, and open access principles in scholarly publishing. Any updates or revisions to the notice will be communicated to the relevant stakeholders.

By adhering to this Copyright Notice, JITCS aims to protect the rights of authors, promote proper attribution and citation practices, and facilitate the responsible and legal use of the published content in accordance with the CC BY-NC-ND 4.0 license.