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PREDIKSI PENCURIAN SEPEDA MOTOR MENGGUNAKAN MODEL TIME SERIES (STUDI KASUS: POLRES KOTABUMI LAMPUNG UTARA) Pranata, Meli; Anggraini, Dian; Makbuloh, Deden; Rinaldi, Achi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 3 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : MATHEMATIC DEPARTMENT, FACULTY OF MATHEMATICS AND NATURAL SCIENCES, UNIVERSITY OF PATTIMURA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol14iss3pp425-434

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.