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All Journal EKSAKTA: Journal of Sciences and Data Analysis Journal of Science and Applicative Technology Jurnal Matematika UNAND Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Science and Technology Journal of Fundamental Mathematics and Applications (JFMA) Smart Society: Community Service and Empowerment Journal Indonesian Journal of Applied Mathematics
Mika Alvionita Sitinjak
Institut Teknologi Sumatera
Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Journal : Journal of Science and Applicative Technology

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Statistical Pattern Recognition of Lithosphere Anomalous Activity Along the Indonesian Ring of Fire S, Mika Alvionita; Satria, Ardika; Muliawati, Triyana; Lestari, Fuji; Harbowo, Danni Gathot
Journal of Science and Applicative Technology Vol. 9 No. 1 (2025): Journal of Science and Applicative Technology June Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/jsat.v9i1.1850

Abstract

The introduction of statistical pattern recognition becomes highly important for assessing disaster threats such as earthquakes. This approach is significantly more comprehensive and suitable for long-term event forecasting. Therefore, in the future, efforts can be promptly made to reduce the risk of disasters resulting from anomalies in lithospheric activity, especially frequent earthquakes in the Sumatra Island region, Indonesia. Statistical pattern analysis of lithospheric activity anomalies can be categorized through classification. Earthquake classification is performed based on magnitude scale and mathematical calculations of earthquake parameter unit conversion. The classification method employed in this research includes machine learning methods like k-nearest neighbor and support vector machine. The evaluation metrics used for machine learning models are model accuracy and confusion matrix tables.
Co-Authors Danni Gathot Harbowo Dhea Sukma Agustiana Dimas Wahyu Saputro Fadia Dilla Sabine Fuji Lestari Irawati, Febri Dwi Lis Nuraini Luluk Muthoharoh Muliawati Muliawati, Triyana Muthoharoh, Luluk Nadeak, Christyan Tamaro Nuramaliyah, Nuramaliyah Rahwani, Putri Hazizah Rekzi P. Manullang Riri Lestari Satria, Ardika Syaiful, Achmad Taj Shavira Tirta Setiawan, Tirta