Incremental Associative Mining based Risk-Mapping System for Earthquake Analysis in Indonesia

Renovita Edelani - Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia
Ali Barakbah - Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia
Tri Harsono - Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia
Louis Arif - Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.3.4.319

Abstract


Indonesia is one of the largest archipelagic countries in the world that has the highest risk of an earthquake. The major causes of earthquakes in this country are plate movements and volcanic activity. Earthquakes in Indonesia has a cause and effect relationship between each province. This disaster caused severe damage including a lot of people to get killed, injured and lose their money and property. We must minimize the impact of the earthquake by forming earthquake risk mapping. The risk of seismicity in Indonesia can vary each year, so it needs to be analyzed how the changes in risk are each addition of earthquake data. This paper proposes an earthquake risk mapping system with Associative Mining based on incremental earthquake data that have the highest values of confidence rates from the seismic association between provinces in Indonesia. The system uses the Incremental Association rule method to see the trend in the value of changes in confidence for each addition of earthquake data every 5 years. This system proposes 3 main features, which are (1) Data Retrieval and Preprocessing, (2) Association Rule Mining, (3) Incremental Associative Mining based risk mapping. For the experimental study, the system used data from 1963-2018. The results show that the provinces of Maluku, North Maluku, Nusa Tenggara Timur, North Sulawesi, and Papua have an incremental association risk of an earthquake.

Keywords


Earthquake, Risk Mapping, Association rule, Incremental Data Analysis, Apriori

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References


Sunarjo, M. T. Gunawan dan S. Pribadi, Gempabumi Edisi Populer, Jakarta: Badan Meteorologi Klimatologi dan Geofisika, 2012.

L. Kurniawan, B. W. Widjaja and W. Rampangilei, Risiko Bencana Indonesia (RBI), Jakarta: BNPB (Badan Nasional Penanggulangan Bencana), 2016.

B. A. Wicaksono, "Ternyata Gempa Jakarta dan Gempa Amerika Berkaitan," Viva, 24 January 2018. [Online]. Available: https://www.viva.co.id/berita/nasional/1000084-ternyata-gempa-jakarta-dan-gempa-amerika-berkaitan. [Accessed 10 April 2019].

A. Ikram and U. Qamar, "Developping an expert system based on association rules and predicate logic for earthquake prediction," Knowledge-Based Systems, Vol. 75, No. C, pp. 87-103, 2015.

Y. Zhou and L. Gao, "An Apriori Based Algorithm Associated Point Line Pattern Applied in Seismic Spatial Data," in International Conference on Artificial Intelligence: Technologies and Applications, 2016.

J. A. Lee, J. Han and K. H. Chi, "Mining quantitative association rule of earthquake data," in International Conference on Hybrid Information Technology, 2009.

Z. Li, J. An, H. Yin, Y. Tian and W. Yu, "Study on Association Rules Between Earthquake Event and Earthquake Precursory Information Anomalies," in International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, 2018.

R. Edelani, A. R. Barakbah, T. Harsono and A. Sudarsono, "Association Analysis Of Earthquake Distribution in Indonesia For Spatial Risk Mapping," in International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), 2017.

A. E. Suliswati, A. R. Barakbah, T. Harsono and Y. Setyowati, "Earthquake density measurement using Automatic Clustering," in Knowledge Creation dan Intelligent Computing (KCIC), 2014.

A. R. Barakbah, T. Harsono and A. Sudarsono, "Automatic Cluster-oriented Seismicity Prediction Analysis of Earthquake Data Distribution in Indonesia," International Journal on Advanced Science Information Technology, Vol. 9, No. 2, pp. 587-593, 2019.

M. N. Shodiq, D. H. Kusuma, M. G. Rifqi, A. R. Barakbah and T. Harsono, "Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia," Internation Journal onf Informatics Visualization, Vol. 2, No. 1, pp. 38-43, 2018.

M. N. Shodiq, A. R. Barakbah and T. Harsono, "Spatial Analysis of Earthquake Distribution with Automatic Clustering for Prediction of Earthquake Seismicity in Indonesia," in The Fourth Indonesian-Japanese Conference on Knowledge Creation and Intelligent Computing (KCIC), 2015.

M. N. Shodiq, D. H. Kusuma, M. G. Rifqi, A. R. Barakbah and T. Harsono, "Spatial Analisys of Magnitude Distribution for Earthquake Prediction using Neural Network Based On Automatic Clustering in Indonesia," in 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), 2017.

M. N. Shodiq, D. H. Kusuma, M. G. Rifqi, A. R. Barakbah and T. Harsono, "Adaptive Neural Fuzzy Inference System and Automatic Clustering for Earthquake Prediction in Indonesia," International Journal on Informatics Visualization, Vol. 3, No. 1, pp. 47-53, 2019.

X. Ding, X. Wang, L. Wang and Y. Zheng, "The development of catastrophe earthquake risk estimation system based on GIS," in IEEE International Geoscience and Remote Sensing Symposium, 2011.

A. Fariza, N. P. Abhimata and J. A. N. Hasim, "Earthquake Disaster Risk Map in East Java, Indonesia, using Analytical Hierarchy Process - Natural Break Classification," in Knowledge Creation and Intelligent Computing (KCIC), 2016.

J. Zhang, "A fuzzy approach for reforming earthquake map," in International Conference of the North American Fuzzy Information Processing Society, 2003.

A. R. Barakbah, T. Harsono, A. Sudarsono and R. A. Aliefyan, "Big Data Analysis for Spatio-Temporal Earthquake Risk-Mapping System in Indonesia with Automatic Clustering," in The International Conference on Big Data Research (ICBDR) 2017, 2017.

A. R. Barakbah, T. Harsono, A. Sudarsono and M. Askari, "A Mobile Application for Cluster-based Visualization of Spatio-Temporal Earthquake Data Distribution in Indonesia," in 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), 2017.

M. N. Shodiq, A. R. Barakbah and T. Harsono, "Cluster Oriented Spatio Temporal Multidimensional Data Visualization of Earthquake in Indonesia," EMITTER International Journal of Engineering Technology, Vol. 3, No. 1, pp. 53-67, 2015.

I. Budi, S. Bressan and N. , "Co-Reference Resolution for Indonesian Languange Using Association rules," in The Eighth International Conference on Information Integration and Web-based Applications Services, 2006.

R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rule," in IBM Almaden Research Center, 1994.

J. Han, M. Kamber and J. Pei, Data Mining: Concepts and Techniques Edition 3, Waltham: Morgan Kaufmann , pp. 243-256, 2000.

R. J. R. Bayardo, "Mining the Most Interesting Rule," in KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, 1999.

C.-H. Lee, C.-R. Lin and M.-S. Chen, "Sliding-Window Filtering: An Efficient Algorithm for Incremental Mining," in ACM 10th International Conference on Information and Knowledge Management, 2001.

W.-G. Teng and M.-S. Chen, Incremental Mining on Association Rules, Taipei:Department of Electrical Engineering, National Taiwan University, pp. 1-38.

A. R. Barakbah, T. Harsono, A. Sudarsono, Pemanfaatan Klasterisasi Otomatis untuk Analisis Gempa, Surabaya: Revka Prima Media, 2019.

Tim Pusat Studi Gempa Nasional, Peta Bahaya dan Sumber Gempa Indonesia Tahun 2017, Bandung: Kementrian Pekerjaan Umum dan Perumahan Rakyat, 2017.