PEMANFAATAN TEKNOLOGI BIG DATA ANALYTICS UNTUK MENDUKUNG PERTAHANAN NEGARA (STUDI KASUS PREDIKSI KONFLIK SOSIAL DI DKI JAKARTA)

Penulis

  • Bunbunan Hesty Universitas Pertahanan Republik Indonesia
  • Andrian Andaya Lestari Universitas Pertahanan Republik Indonesia
  • Achmad Farid Wadjdi Universitas Pertahanan Republik Indonesia

DOI:

https://doi.org/10.33172/tp.v3i2.822

Abstrak

Jumlah dan perpindahan/pergerakan penduduk, dan sumber daya yang terbatas memberikan sumbangan untuk terlahirnya konflik. Dengan perannya sebagai ibu kota negara, konflik di Jakarta akan mempengaruhi keamanan negara. Penelitian ini adalah upaya penerapan Big Data Analytics dalam memprediksi konflik yang terjadi di wilayah Jakarta memanfaatkan hubungan korelatif pertumbuhan penduduk dan sosial media dengan kejadian konflik sehingga dapat membantu pemerintah untuk mengantisipasi konflik dalam konteks pertahanan negara. Metode penelitian menggunakan mixed-method dengan memanfaatkan data statistik kependudukan dan pengumpulan data twitter selama kurun 2019. Hasilnya adalah Konflik yang berhubungan dengan SARA, rusuh, dan kriminalitas yang paling berpeluang terjadi di DKI Jakarta secara keseluruhan. Konflik rusuh dengan nilai 0,34 berpeluang di Kepulauan Seribu. Konflik SARA dengan nilai 0,55 berpeluang di Jakarta Pusat. Konflik kriminalitas dengan nilai 0,59 berpeluang di Jakarta Utara. Konflik kriminalitas dengan nilai 0,261 berpeluang di Jakarta Barat. Konflik rusuh dengan nilai 0,56 berpeluang di Jakarta Selatan. Konflik SARA dengan nilai 0,45 berpeluang di Jakarta Timur. Akhirnya kami menyimpulkan bahwa Kepulauan Seribu adalah daerah dengan ranking resiko konflik teratas di DKI Jakarta sebesar 37.3% diikuti dengan Kota Jakarta Utara sebesar 17.7%, Kota Jakarta Barat sebesar 15.6%, Kota Jakarta Selatan sebesar 15.5%, Kota Jakarta Timur sebesar 7.3%, dan terakhir Kota Jakarta Pusat sebesar 6.7%.

Referensi

A. F. Wadjdi, E. M. Sianturi. (2018). The Implementation of Framing, Agenda-Setting, and Data Mining in Evaluation of Public Policies Case Study of the State Defense Program in Indonesia. 2nd International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia, pp. 1-5, DOI: 10.1109/ICICOS.2018.8621656.

Feldman, Ronen, J. (2006). The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. New York: Cambridge University Press.

J.M. Soler, M. F. (2012). Twitter as a tool for predicting elections results. IEEE/ACM International Conference on advances in social networks analysis and mining (ASONAM),. Piscataway: IEEE, 1194–1200.

Kremer, M. (1993). Population Growth and Technological Change: One Million B.C. to 1990. The Quarterly Journal of Economics.

Livi-Bacci, M. (2017). A Concise History of World Population. John Wiley & Sons Ltd.

Mcnicoll, G. (1984). Consequences of Rapid Population Growth : An Overview and Assessment. Population and Development Review. 10(2), 177–240.

Sudarsono, J. (2008). Berbagai Persoalan Pertahanan Negara. Biro Humas. Jakarta.

Unduhan

Diterbitkan

2022-01-07

Cara Mengutip

Hesty, B., Lestari, A. A., & Wadjdi, A. F. (2022). PEMANFAATAN TEKNOLOGI BIG DATA ANALYTICS UNTUK MENDUKUNG PERTAHANAN NEGARA (STUDI KASUS PREDIKSI KONFLIK SOSIAL DI DKI JAKARTA). Teknologi Penginderaan, 3(2). https://doi.org/10.33172/tp.v3i2.822

Terbitan

Vol 3 No 2 (2021): Jurnal Teknologi Penginderaan

Bagian

Articles

Lisensi

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Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access)