Model ARIMA Terbaik Prediksi Latitude dan Longitude Kegiatan Kapal Imigran Ilegal
DOI:
https://doi.org/10.30865/mib.v5i4.3301Keywords:
Time Series Forecasting, ARIMA, Illegal Immigrant, Malaka Strait, Riau IslandsAbstract
The migration of a person to another country without following the law is illegal immigration. Many problems are caused by this activity, ranging from population problems to increased crime. Predicting the emergence of ships carrying illegal immigrants can assist border patrols in planning patrols to planning defense equipment. Time series forecasting to predict the latitude and longitude of boats carrying illegal immigrants is the Autoregressive Integrated Moving Average (ARIMA) model. The case studies for this research are the Straits of Malacca and the Riau Islands. The prediction range is from one to four weeks to find the model with the smallest error. The ARIMA model for one-week prediction distance succeeded in obtaining the smallest RMSE. However, the smallest RMSE result (0.28730) was obtained for a four-week prediction distance with ARIMA model parameters (4,0,2) for longitude prediction. Meanwhile, the prediction of latitude. The best model is ARIMA (4,0,1), with an RMSE of 0.11457. For latitude and longitude predictions in the Riau Islands, the best models are ARIMA (3,0,0) with RMSE of 0.009074 and ARIMA (2,0,0) with RMSE 0.045815. Based on this study, the ARIMA model is suitable for predicting latitude and longitude data with a short prediction distance (one week)References
E. Johan, “Kebijakan Indonesia Terhadap Imigran Ilegal,†Yuridika, vol. 28, no. 6, pp. 1–12, 2013.
R. Yulia and A. Prakarsa, “Illegal Immigrant Issues in Criminal Policy Perspective,†Jt. Int. Conf. Call Pap. Indones. 2017. Islam. Univ. Kalimantan-The Natl. Univ. Malaysia, pp. 101–116, 2017, [Online]. Available: www.jurnalasia.id.
M. H. Böhme, A. Gröger, and T. Stöhr, “Searching for a better life: Predicting international migration with online search keywords,†J. Dev. Econ., vol. 142, 2020, doi: 10.1016/j.jdeveco.2019.04.002.
M. Saeri, “Karakteristik dan Permasalahan Selat Malaka,†J. Transnasional, vol. 4, no. 2, pp. 809–822, 2013.
F. Topputo et al., “Space shepherd: Search and rescue of illegal immigrants in the mediterranean sea through satellite imagery,†Int. Geosci. Remote Sens. Symp., vol. 2015-Novem, pp. 4852–4855, 2015, doi: 10.1109/IGARSS.2015.7326917.
R. Makahingide, “Upaya Pemerintah Indonesia Dalam Menangani Persoalan Di Wilayah Perbatasan Antara Pulau Marore Dan Philipina Selatan,†J. Polit., vol. 10, no. 2, 2021.
E. Heizier, “Selat Malaka, Kejayaan Masa Lalu dan Kini Patroli Laut Diintensifkan,†tempo, 2021. https://nasional.tempo.co/read/1529024/selat-malaka-kejayaan-masa-lalu-dan-kini-patroli-laut-diintensifkan/full&view=ok (accessed Dec. 01, 2021).
D. Fantazzini, J. Pushchelenko, A. Mironenkov, and A. Kurbatskii, “Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg,†Forecasting, vol. 3, no. 4, pp. 774–804, 2021, doi: 10.3390/forecast3040048.
M. Alzyout, M. Alsmirat, and M. I. Al-Saleh, “Automated ARIMA Model Construction for Dynamic Vehicle GPS Location Prediction,†2019 6th Int. Conf. Internet Things Syst. Manag. Secur. IOTSMS 2019, pp. 380–386, 2019, doi: 10.1109/IOTSMS48152.2019.8939197.
M. Ivanovic and V. Kurbalija, “Time series analysis and possible applications,†2016 39th Int. Conv. Inf. Commun. Technol. Electron. Microelectron. MIPRO 2016 - Proc., pp. 473–479, 2016, doi: 10.1109/MIPRO.2016.7522190.
S. Mehrmolaei and M. R. Keyvanpour, “Time series forecasting using improved ARIMA,†2016 Artif. Intell. Robot. IRANOPEN 2016, pp. 92–97, 2016, doi: 10.1109/RIOS.2016.7529496.
S. McDonald, S. Coleman, T. M. McGinnity, and Y. Li, “A hybrid forecasting approach using ARIMA models and self-organising fuzzy neural networks for capital markets,†Proc. Int. Jt. Conf. Neural Networks, 2013, doi: 10.1109/IJCNN.2013.6706965.
R. R. Sharma, M. Kumar, S. Maheshwari, and K. P. Ray, “EVDHM-ARIMA-Based Time Series Forecasting Model and Its Application for COVID-19 Cases,†IEEE Trans. Instrum. Meas., vol. 70, 2021, doi: 10.1109/TIM.2020.3041833.
C. Chatfield, The Analysis of Time Series, 6th ed., vol. 46, no. 1. Chapman & Hall/CRC, 2004.
S. Jha, E. Yang, A. O. Almagrabi, A. K. Bashir, and G. P. Joshi, “Comparative analysis of time series model and machine testing systems for crime forecasting,†Neural Comput. Appl., vol. 33, no. 17, pp. 10621–10636, 2021, doi: 10.1007/s00521-020-04998-1.
P. T. Yamak, L. Yujian, and P. K. Gadosey, “A comparison between ARIMA, LSTM, and GRU for time series forecasting,†PervasiveHealth Pervasive Comput. Technol. Healthc., pp. 49–55, 2019, doi: 10.1145/3377713.3377722.
D. A. Petrusevich, “Review of missing values procession methods in time series data,†J. Phys. Conf. Ser., vol. 1889, no. 3, 2021, doi: 10.1088/1742-6596/1889/3/032009.
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