Pelabelan tingkat guncangan gempa bumi menggunakan  clustering berbasis principal component analysis

Agung Teguh Wibowo; Renata Amalia; Supriyono

doi:10.33795/eltek.v24i2.9955

Authors

Agung Teguh Wibowo
Renata Amalia UIN Maulana Malik Ibrahim Malang
Supriyono

DOI:

https://doi.org/10.33795/eltek.v24i2.9955

Keywords:

Clustering, Earthquake, Principal Component Analysis

Abstract

Indonesia memiliki tingkat kerentanan tinggi terhadap bencana alam, khususnya gempa bumi. Perbedaan tingkat guncangan gempa memerlukan upaya mitigasi berbasis teknologi untuk mendukung analisis data gempa. Penelitian ini bertujuan mengelompokkan data gempa menggunakan metode Principal Component Analysis berbasis clustering. Data yang digunakan berupa data gempa harian tahun 2024 dari Badan Meteorologi, Klimatologi, dan Geofisika Stasiun Geofisika Kelas III Malang. Metode ini digunakan untuk mengekstraksi komponen utama yang merepresentasikan variasi dominan data. Hasil analisis menunjukkan bahwa komponen utama pertama mampu menjelaskan 34,1% variasi data dan didominasi oleh variabel longitude serta magnitudo. Nilai komponen utama pertama digunakan sebagai dasar proses clustering untuk mengidentifikasi pola gempa berdasarkan kemiripan karakteristik. Pengujian Calinski-Harabasz Index menghasilkan nilai sebesar 5,626,0269. Berdasarkan hasil tersebut, data gempa dikelompokkan menjadi kategori dirasakan dan tidak dirasakan mengacu pada Skala Intensitas Gempabumi Badan Meteorologi, Klimatologi, dan Geofisika. Hasil penelitian menunjukkan bahwa metode tersebut mampu mengurangi kompleksitas data dan membantu pengelompokan tingkat guncangan gempa secara efektif dan efisien.

ABSTRACT

Indonesia is highly vulnerable to natural disasters, particularly earthquakes. This study aims to classify earthquake data using a clustering-based Principal Component Analysis method. The dataset consists of daily earthquake records in 2024 from the Badan Meteorologi, Klimatologi, dan Geofisika Geophysics Station Class III Malang. Principal component analysis was used to extract the main components representing dominant data variations. The results showed that the first principal component explained 34,1% of the variance and was dominated by longitude and magnitude variables. The first principal component scores were then used for clustering based on earthquake characteristics similarity. The Calinski-Harabasz Index produced a value of 5,626.0269. Based on the clustering results, earthquakes were categorized into felt and unfelt groups according to the Earthquake Intensity Scale of Badan Meteorologi, Klimatologi, dan Geofisika. The findings demonstrate that the method effectively reduces data complexity and supports efficient earthquake shaking classification.

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