Prediksi Tingkat Kriminalitas Pencurian Sepeda Motor Mengunakan Metode Weighted Moving Average
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1223Keywords:
Crime; Motor Vehicle Theft; Forecasting; Weighted Moving Average; PredictionAbstract
Motor vehicle theft is a frequent criminal issue that causes public concern. Fluctuating case numbers create a need for a method to estimate future incidents, thereby aiding police decision-making. This study aims to develop a website-based prediction system for motorcycle theft rates in the Lubuklinggau City Police jurisdiction using the Weighted Moving Average (WMA) method. Historical motor vehicle theft data was processed using time-series forecasting techniques. The system features data management, a forecasting process, and prediction result presentation. Research findings indicate that the WMA method consistently predicts an upward trend in vehicle theft cases throughout 2025, with forecasted figures rising from 74 in January 2025 to 114 in December 2025. Evaluation results show a Mean Absolute Percentage Error (MAPE) of 1.45% and a Mean Absolute Error (MAE) of 757.29. The low MAPE value indicates a relatively small prediction error, demonstrating the WMA method's ability to deliver accurate forecasts. The relatively stable increase across periods suggests that the WMA method effectively captures historical data patterns and provides insight into future trends regarding vehicle theft. These predictions are expected to assist the police in formulating more effective prevention and security strategies.
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H. Syafwan, F. Siagian, P. Putri, M. Handayani, S. H., “Forecasting Jumlah Pengangguran Di Kabupaten Asahan Menggunakan Metode Weighted Moving Average,” J. Tek. Inform. Kaputama, vol. 5, no. 2, pp. 224–229, 2021, doi: https://doi.org/10.59697/jtik.v5i2.
M. H. Lubis And S. Sumijan, “Prediksi Tingkat Kriminalitas Menggunakan Metode Single Moving Average Mustopa,” J. Sistim Inf. Dan Teknol., vol. 3, no. 4, pp. 183–188, 2021, doi: 10.37034/jsisfotek.v3i4.63.
R. P. S. Daratullaila, “Prediksi Jumlah Kejahatan Di Indonesia Dengan Metode Autoregressive Integrate Moving Average (Arima),” J. Mat. Dan Terap., vol. 5, no. 2, pp. 60–67, 2023, doi: https://doi.org/10.33059/Jgp.V5i2.
E. S. S. N. Putri, I. Cholissodin, “Prediksi Jumlah Kriminalitas Menggunakan Metode Extreme Learning Machine ( Studi Kasus Di Kabupaten Probolinggo ),” J. Pengemb. Teknol. Inf. Dan Ilmu Komput., vol. 2, no. 11, pp. 4687–4693, 2018.
M. Rizqi, A. Cahya, And N. El Maida, “Implementasi Metode Weighted Moving Average Untuk Sistem Peramalan Penjualan Markas Coffee,” Informal Informatics J., vol. 6, no. 3, pp. 154, 2021, doi: 10.19184/isj.v6i3.28467.
T. A. Cinderatama and A. F. Dianta, “Aplikasi E-Marketplace Untuk Penjualan Ikan Cupang Berbasis Web,” Pengabdi. Pada Masy., vol. 2, no. 2, pp. 40–45, 2018, doi: 10.33795/jindeks.v3i1.114.
F. Harahap, N. E. Saragih, E. T. Siregar, and H. Sariangsah, “Penerapan Data Mining Dengan Algoritma Naive Bayes Classifier Dalam Memprediksi Pembelian Cat Kata Kunci Data Mining Purchase Of Paint Naive Bayes,” J. Ilm. Inform., vol. 09, no. 01, pp. 19–23, 2021, doi: 10.33884/jif.v9i01.3702.
H. D. Wijaya and S. Dwiasnati, “Implementasi Data Mining Dengan Algoritma Naïve Bayes Pada Penjualan Obat,” J. Inform., vol. 7, no. 1, pp. 1–7, 2020, doi: 10.31311/ji.v7i1.6203.
S. Alfarisi, “Sistem Prediksi Penjualan Gamis Toko Qitaz Menggunakan Metode Single Exponential Smoothing,” Jabe (Journal Appl. Bus. Econ., vol. 4, no. 1, pp. 80, 2022, doi: 10.30998/jabe.v4i1.1908.
D. P. Lestari and R. Panday, “Analisis Forecasting Jumlah Wisatawan Mancanegara Di Provinsi Bali Dengan Metode Least Square,” No. January, pp. 1–11, 2020.
F. R. Kustiawan And Hudori, “Forecasting Jumlah Wisatawan Di Taman Wisata Alam Kawah Ijen Dengan Metode Exponential Smoothing Berbantu Zaitun Time Series,” J. Pendidik. Mat. Mat., vol. I, no. 1, pp. 36–49, 2017, url: https://ejournal.unibabwi.ac.id/index.php/transformasi/article/view/101
N. Indah Kusuma Wardhani, I. Hartami S, And W. Dwi Puspitasari, “Sistem Forecasting Penjualan Beras Dengan Menerapkan Metode Trend Moment,” Jati (Jurnal Mhs. Tek. Inform., vol. 6, no. 2, pp. 901–907, 2022, doi: 10.36040/jati.v6i2.5780.
I. Solikin And S. Hardini, “Aplikasi Forecasting Stok Barang Menggunakan Metode Weighted Moving Average (WMA) Pada Metrojaya Komputer,” J. Inform. J. Pengemb. It, vol. 4, no. 2, pp. 100–105, 2019, doi: 10.30591/jpit.v4i2.1373.
S. Rifadli And R. Sari, “Implementasi Metode Weighted Moving Average (WMA) Pada Prediksi Penjualan Gas Elpiji Berbasis Website,” J. Desain Dan Anal. Teknol., vol. 3, no. 2, pp. 88–95, 2024, doi: 10.58520/jddat.v3i2.47.
C. A. Suhendra, M. Asfi, W. J. Lestari, And I. Syafrinal, “Sistem Peramalan Persediaan Sparepart Menggunakan Metode Weight Moving Average Dan Reorder Point,” Matrik J. Manajemen, Tek. Inform. Dan Rekayasa Komput., vol. 20, no. 2, pp. 343–354, 2021, doi: 10.30812/matrik.v20i2.1052.
F. Nur Hadiansyah, “Prediksi Harga Cabai Dengan Menggunakan Pemodelan Time Series Arima,” Indones. J. Comput., vol. 2, no. 1, pp. 71, 2022, doi: 10.21108/indojc.2017.2.1.144.
N. D. Maulana, B. D. Setiawan, And C. Dewi, “Implementasi Metode Support Vector Regression (SVR) Dalam Peramalan Penjualan Roti (Studi Kasus?: Harum Bakery),” J. Pengemb. Teknol. Inf. Dan Ilmu Komput., vol. 3, no. 3, pp. 2986–2995, 2019.
M. Munawar, and Y. Riadi. “Sistem Pendeteksi Berita Hoax Di Media Sosial Dengan Teknik Data Mining Scikit Learn,” vol. 4, pp. 173–179, 2019, doi: 10.47007/komp.v4i02.3140.
N. R. Silitonga, “Analisis Dan Penerapan Datamining Untuk Mendeteksi Berita Palsu ( Fake News ) Pada Social Media Dengan Scikit-Learn”, vol. 3, no. 3, pp. 170-184, 2019, url: https://digilib.esaunggul.ac.id/public/UEU-Undergraduate-12192-JURNAL.Image.Marked.pdf
R. W. Abdullah, D. Hartanti, And H. Permatasari, “Penerapan Data Mining Untuk Memprediksi Jumlah Produk Terlaris Menggunakan Algoritma Naive Bayes Studi Kasus ( Toko Prapti ),” Jurnal Ilmiah Informatika Global, vol. 13, no. 1, pp. 20–27, 2022, doi: 10.36982/jiig.v13i1.2060.
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