Article Sidebar

Submitted
Dec 10, 2024
Published
May 10, 2025
Download
PDF
Statistic
Read Counter : 20 Download : 37

Main Article Content

Abstract

High crime rates can have an impact on social stability, quality of life, and economic development in a region. Therefore, it is important to know the socio-cultural factors that influence crime rates in order to provide new insights to support data-based decision making. The relationship between these factors and crime is often non-linear and can be influenced by spatial effects, so a method is needed that considers spatial effects such as the Spatial Autoregressive Moving Average (SARMA). SARMA is not only able to capture spatial autocorrelation patterns, but can also identify the influence of interdependence between regions over a certain period of time. Furthermore, the Lagrange multiplier test is used to see the presence of spatial autocorrelation specifically. From the results of the analysis carried out, there are 4 significant factors, namely population density, unemployment rate, number of places of worship, and average length of schooling. Furthermore, the results of the spatial autocorrelation analysis using LISA show that there are three districts/cities identified as having spatial autocorrelation with a significance level of 0.05. This means that Bone Regency, Maros Regency, and Pangkajene Kepulauan Regency have a more significant influence on the surrounding areas.

Keywords

crime, SARMA, lagrange multiplier test

Article Details

References

  1. [1] Aryanto, “Statistik Politik dan Keamanan 2023. Badan Pusat Statistik Indonesia,” Katalog: 4601001.73 Makassar, 2023.
  2. [2] Alghifary, R. Muhamad, “Analisa Yuridis Kriminalisasi Terhadap Penyalahguna Narkotika Oleh Aparat Penegak Hukum Dalam Perspektif Hukum Progresif Di Indonesia,” Universitas Islam Sultan Agung Semarang, 2023.
  3. [3] Desinta, Dinda, “Faktor-Faktor Yang Memengaruhi Kejadian Kejahatan Di Provinsi Jawa Barat Tahun 2018-2020. Media Edukasi Data Ilmiah Dan Analisis (MEDIAN), ” 5(01). 2022.
  4. [4] Ditya, Afriyenida, “Pemodelan Kriminalitas Di Sumatera Barat Dengan Metode Geographically Weighted Regression (Gwr) Pembobot Adaptive Gaussian Kernel Dan Adaptive Bisquare Kernel, ” Universitas Muhammadiyah Semarang, 2023.
  5. [5] Y. Zhou, F. Wang, and S. Zhou, “The Spatial Patterns of the Crime Rate in London and Its Socio-Economic Influence Factors. Social Sciences,” 2023, 12(6), 340. DOI: 10.3390/socsci12060340
  6. [6] IEOM Society International, “Proceedings of the International Conference on Industrial Engineering and Operations Management. Paris, France,” 2018, Retrieved from: IEOM
  7. [7] H. Yasin, B. Warsito, A. R. Hakim, “Regresi Spasial (Aplikasi dengan R),” WADE GROUP, Ponorogo, 2020.
  8. [8] J. P. LeSage, “The Theory and Practice of Spatial Econometrics, Department of Economics, University of Teledo,” 1999.
  9. [9] L. Anselin, “Spatial Econometrics: Methods and Models, Departments of Geography and Economics, ” University of California, Santa Barbara, 1998.
  10. [10] B. Nugraha, “Pengembangan uji statistik: Implementasi metode regresi linier berganda dengan pertimbangan uji asumsi klasik,” Pradina Pustaka, Sukoharjo, 2022.
  11. [11] S. Chatterjee, A. S. Hadi, “Regression analysis by example,” John Wiley & Sons, New Jersey, 2015.
  12. [12] S. N. Aulele, V. Y. I. Ilwaru, E. R. Wuritimur, dan M. Y. Matdoan, “Analisis jumlah penduduk miskin di provinsi maluku dengan menggunakan pendekatan regresi spasial,” Jurnal Aplikasi Statistika dan Komputasi Statistik, No.2,Vol.13, 23–3, : https://jurnal.stis.ac.id/index.php/jurnalasks/ article/view/294/103, 2021.
  13. [13] K. Suryowati, R. D. Bekti, dan A. Faradila, “A comparison of weights matrices on computation of dengue spatial autocorrelation,” IOP Conference Series: Materials Science and Engineering, : https://iopscience.iop.org/ article /10.1088/1757-899X/335 /1/012052/meta, 2018.
  14. [14] M. Agustina, B. Abapihi, G. N. A. Wibawa, dan I. Yahya, “Pemodelan faktor-faktor yang mempengaruhi tingkat pengangguran terbuka di indonesia dengan pendekatan regresi spasial,” Seminar Nasional Sains dan Terapan VI, Manado, 25 April, 2022.
  15. [15] J. Supriyanto, D. Alita, dan A. R. Isnain,“Penerapan Algoritma K-Nearest Neighbor (K-NN) Untuk Analisis Sentimen Publik Terhadap Pembelajaran Daring, Jurnal Informatika dan Rekayasa Perangkat Lunak,” No.1, Vol. 4, 74–80, : https://jim.teknokrat.ac.id/index.php/informatika/article/view/246 8. 2023.
  16. [16] I. G. N. M Jaya, B. Tantular, Zulhanif, “Optimalisasi matrik bobot spasial berdasarkan k-nearest neighbor dalam spasial lag model, Konferensi Nasional Penelitian Matematika dan Pembelajarannya II (KNPMP II), ” Surakarta, 18 Maret, 2017.
  17. [17] N. P. Yuriantari, M. N. Hayati, dan S. Wahyuningsih, “Analisis autokorelasi spasial titik panas di Kalimantan Timur menggunakan indeks moran dan local indicator of spatial autocorrelation (LISA). Eksponensial, ” No.1, Vol.8, 63–70, : https://jurnal.fmipa.unmul. ac.id/index.php/exponensial/article/view/78, 2017.
  18. [18] N. S. Purba, dan L. A. Soleman, “Analisis Spasial Mengenai Faktor-Faktor Yang Memengaruhi Kemiskinan Di Provinsi Papua Tahun 2019, ” Seminar Nasional Hasil Penelitian dan Pengabdian Kepada Masyarakat, ” Banten, oktober 2020.