Mapping Criminal Location Entity from Indonesian Online Newspapers

  • Authors

    • Neno Sulistyawan
    • Sari Widya Sihwi
    • Wiranto .
    2018-12-16
    https://doi.org/10.14419/ijet.v7i4.40.24415
  • Crime, Geocoding, Information Extraction, Rule Based Algorithm, Support Vector Machine.

  • Abstract

    The aim of this study is to extract entity locations on crime news in Indonesian online newspapers and to tag the locations into a map. The methods used in this study are rule-based algorithm, for identifying and extracting entity location of the crime, and SVM (Support Vector Machine) algorithm, for classifying which sentence containing the location of the crime. Every sentence containing criminal location was included in geocoding process so it could be mapped into a digital map. The accuracy of identifying the entity location by using rule-based algorithm is 96.2%. SVM model that has the best accuracy in classifying sentences that contains entity scene of the crime is Radial kernel whose accuracy is 95.77%.

     

     

     

  • References

    1. [1] S. D. of P. and S. Statistics, Statistik Kriminal 2016. Jakarta: Central Bureau of Statistics, 2016.

      [2] R. Feldman and J. Sanger, The Text Mining Handbook. New York, NY, USA: Cambridge University Press, 2006.

      [3] B. Liu, Web Data Mining Exploring Hyperlinks, Contents, and Usage Data, 2nd ed. Springer-Verlag Berlin Heidelberg, 2011.

      [4] S.-M.-R. Beheshti, S. Venugopal, S. H. Ryu, B. Benatallah, and W. Wang, “Big Data and Cross-Document Coreference Resolution: Current State and Future Opportunities,†no. November, 2013.

      [5] B. Alshaikhdeeb and K. Ahmad, “Biomedical Named Entity Recognition : A Review,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 6, no. 6, pp. 889–895, 2016.

      [6] B. Cowan, S. Zethelius, B. Luk, T. Baras, P. Ukarde, and D. Zhang, “Named Entity Recognition in Travel-Related Search Queries,†Proceeding AAAI’15 Proc. Twenty-Ninth AAAI Conf. Artif. Intell. Pages, pp. 3935–3941, 2015.

      [7] K. E. Saputro, S. S. Kusumawardani, and S. Fauziati, “Development of semi-supervised named entity recognition to discover new tourism places,†in 2016 2nd International Conference on Science and Technology-Computer (ICST), 2016, pp. 124–128.

      [8] T. Mahmood, G. Mujtaba, L. Shuib, N. Z. Ali, A. Bawa, and S. Karim, “Public bus commuter assistance through the named entity recognition of twitter feeds and intelligent route finding,†IET Intell. Transp. Syst., vol. 11, no. 8, pp. 521–529, 2017.

      [9] H. Shabat, “Named Entity Recognition in Crime News Documents Using Classifiers Combination,†vol. 23, no. 6, pp. 1215–1222, 2015.

      [10] I. Jayaweera and C. Sajeewa, “Crime Analytics : Analysis of Crimes Through Newspaper Articles Crime Analytics : Analysis of Crimes Through Newspaper Articles,†no. April, 2015.

      [11] R. Arulanandam, B. T. R. Savarimuthu, and M. A. Purvis, “Extracting Crime Information from Online Newspaper Articles,†Proc. Second Australas. Web Conf. (AWC 2014), Auckland, New Zeal., no. Awc, pp. 31–38, 2014.

      [12] A. S. Wibawa and A. Purwarianti, “Indonesian Named-entity Recognition for 15 Classes Using Ensemble Supervised Learning,†Procedia Comput. Sci., vol. 81, no. May, pp. 221–228, 2016.

      [13] T. F. Abidin, R. Ferdhiana, and H. Kamil, “Automatic Extraction of Place Entities and Sentences Containing the Date and Number of Victims of Tropical Disease Incidence from the Web,†J. Emerg. Technol. Web Intell., vol. 5, no. 3, pp. 302–309, 2013.

      [14] R. Alfred, L. C. Leong, C. K. On, and P. Anthony, “Malay Named Entity Recognition Based on Rule-Based Approach,†vol. 4, no. 3, 2014.

      [15] G. A. Leroy, “Crime Information Extraction from Police and Witness Narrative Reports,†2008.

      [16] Y. Li, K. Bontcheva, and H. Cunningham, “SVM based learning system for information extraction,†in Deterministic and statistical methods in machine learning, J. Winkler, M. Niranjan, and N. Lawrence, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 319–339.

      [17] H. Shabat, N. Omar, and K. Rahem, “Named Entity Recognition in Crime Using Machine Learning Approach,†in Information Retrieval Technology, A. Jaafar, N. Mohamad Ali, S. A. Mohd Noah, A. F. Smeaton, P. Bruza, Z. A. Bakar, N. Jamil, and T. M. T. Sembok, Eds. Cham: Springer International Publishing, 2014, pp. 280–288.

      [18] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. New York, NY, USA: Cambridge University Press, 2000.

  • Downloads

  • How to Cite

    Sulistyawan, N., Widya Sihwi, S., & ., W. (2018). Mapping Criminal Location Entity from Indonesian Online Newspapers. International Journal of Engineering & Technology, 7(4.40), 112-117. https://doi.org/10.14419/ijet.v7i4.40.24415

    Received date: 2018-12-19

    Accepted date: 2018-12-19

    Published date: 2018-12-16