PREDIKSI PENCURIAN SEPEDA MOTOR MENGGUNAKAN MODEL TIME SERIES (STUDI KASUS: POLRES KOTABUMI LAMPUNG UTARA)

  • Meli Pranata UNIVERSITAS ISLAM NEGERI RADEN INTAN
  • Dian Anggraini Institut Teknologi Sumatra
  • Deden Makbuloh Universitas Islam Negeri Raden Intan Lampung
  • Achi Rinaldi Universitas Islam Negeri Raden Intan Lampung
DOI: https://doi.org/10.30598/barekengvol14iss3pp425-434
Keywords: Crime, Time Series, AR, MA, ARIMA, Prediction

Abstract

Crime is a crime that violates the laws of a country or violates the norms in force in society. Theft is a form of crime. The impact of theft is a feeling of insecurity, fear and insecurity. One model used to predict the number of theft cases is the time series model. A time series model is a set of values ​​observed in an activity, event, or event where data is then arranged in chronological order. Generally, in intervals of the same length. This study aims to model the data of criminal acts of motorcycle theft in North Lampung Police with Autoregressive (AR), Moving Average (MA), and Autoregressive Integrated Moving Average (ARIMA) models. Furthermore, the best models will be used for forecasting for the next 6 months. The results of the AR model (1), AR (3) model, MA model (1), ARIMA (1,1,1), and ARIMA model (3,1,1). The MA model (1) has a significant parameter coefficient, fulfills diagnostic tests and has the smallest RMSE and AIC values ​​with an RMSE value of 6.5612926 and an AIC value of 394.82. The predicted results of the MA model (1) for the next 6 months tend to be horizontally different from the original data which tends to decrease.

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References

K. Kartini, Patologi Sosial, 1st ed. Jakarta: Raja Grafindo Persada, 1999.

D. Anggraini and Y. Wijaya, “Obligasi Bencana Alam Dengan Suku Bunga Stokastik Dan Pendekatan Campuran,†Al-Jabar J. Pendidik. Mat., vol. 7, no. 1, pp. 49–62, Jun. 2016, doi: 10.24042/ajpm.v7i1.130.

I. Yusnita, R. Masykur, and S. Suherman, “Modifikasi Model Pembelajaran Gerlach dan Ely Melalui Integrasi Nilai-Nilai Keislaman Sebagai Upaya Meningkatkan Kemampuan Representasi Matematis,†Al-Jabar J. Pendidik. Mat., vol. 7, no. 1, 2016.

N. L. K. D. Murniati, Indiwarti, and A. A. Rohmawati, “Implemetasi Model Autoregressive (AR) Dan Autoregressive Conditional Heteroskedasticity (ARCH) Untuk Memprediksi Harga Emas,†Ind. J. Comput., vol. 2, no. 2, pp. 29–44, Sep. 2018.

W. W. S. Wei, Ed., Time Series Analysis Univarite and Multivariate Methods, Second. Temple University: Publication Data, 2006.

A. Nurlifa and S. Kusumadewi, “Sistem Peramalan Jumlah Penjualan Menggunakan Metode Moving Average pada Rumah Jilbab Zaky,†J. Inovtek Polbeng, vol. 2, no. 1, 2017.

A. Kumila, B. Sholihah, E. Evizia, N. Safitri, and S. Fitri, “Perbandingan Metode Moving Average dan Metode Naïve Dalam Peramalan Data Kemiskinan,†J. Teor. dan Apl. Mat., vol. 3, no. 1, pp. 65–73, Apr. 2019, doi: 10.31764/jtam.v3i1.764.

A. P. Desvina and M. Syahfitra, “Aplikasi Metode Box-Jenkins dalam Memprediksi Pertumbuhan Perdagangan Luar Negeri Provinsi Riau,†J. Sains Mat. dan Stat., vol. 2, no. 2, p. 9, 2016.

Wardono, S. Mariani, and Y. Fathonah, “Implementation of Autoregressive Integrated Moving Average (ARIMA) Methods for Forecasting Many Applicants Making Driver’s License a with Eviews 7 in Pati Indonesia,†Wardono, Scolastika Marian. dan Yuliyana Fathonah, “Implementation Autoregressive Integr. Mov. Aver. Methods Forecast. Many Appl. Mak. Driver’s Licens. a with Eviews 7 Pati Indones. J. Theor. Apllied , 2017.

Mutia Indah Sari, “PEMODELAN HARGA SAHAM MENGGUNAKAN GENERALISASI PROSES WIENER DAN MODEL ARIMA,†PhD Thesis, Institut Pertanian Bogor, Bogor, 2011.

S. H. A. Salmon, N. Nainggolan, and D. Hatidja, “Pemodelan ARIMA Dalam Prediksi Penumpang Pesawat Terbang Pada Bandara Internasional Sam Ratulangi Manado,†JdC, vol. 4, no. 1, p. 9, 2015.

S. Makridakis, Steven C. Wheelwright, and Victor E. McGee, Metode dan Aplikasi Peramalan Jilid 1, Kedua. Jakarta: Erlangga, 1995.

K. Mendome, N. Nainggolan, and J. Kekenusa, “Penerapan Model ARIMA dalam Memprediksi Jumlah Tindak Kriminalitas di Wilayah POLRESTA Manado Provinsi Sulawesi UtaraKlorofil,†J. MIPA UNSRAT ONLINE, vol. 5, no. 2, pp. 113–116, Oct. 2016.

A. A. Nugroho, R. W. Y. Putra, F. G. Putra, and M. Syazali, “Pengembangan Blog Sebagai Media Pembelajaran Matematika,†Al-Jabar J. Pendidik. Mat., vol. 8, no. 2, 2017.

R. S. Faustina, “Model HYBRID ARIMA-GARCH untuk Estimasi Volatilitas Harga Emas menggunakan Software R,†Universitas Negeri Semarang, 2016.

R. Monica, Suyono, and V. M. Santi, “roses Autoregressive Conditional Heteroscedasticity dengan Dugaan Variansi Inflasi Indonesia,†J. Stat. Dan Apl. 1, no. 1, 2017.

Published
2020-10-11
How to Cite
[1]
M. Pranata, D. Anggraini, D. Makbuloh, and A. Rinaldi, “PREDIKSI PENCURIAN SEPEDA MOTOR MENGGUNAKAN MODEL TIME SERIES (STUDI KASUS: POLRES KOTABUMI LAMPUNG UTARA)”, BAREKENG: J. Math. & App., vol. 14, no. 3, pp. 425-434, Oct. 2020.
Issue
Vol 14 No 3 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Section
Articles

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