Prediction of Crime Rate in Banjarmasin City Using RNN-GRU Model
Keywords:
Banjarmasin, crime, inflation, prediction, RNN-GRUAbstract
Crime is a crime that violates the law and social norms so that it can harm society. Every year there is an increase in criminal cases. The rise of various criminal acts caused disturbances to the community's comfort and the surrounding environment, especially in Banjarmasin City, the capital of South Kalimantan Province in Indonesia. The economic status of a region, such as the inflation rate and the local population's lack of purchasing power, can contribute to crime. This study proposes a model to predict the crime rate in Banjarmasin using the Recurrent Neural Network (RNN) with the Gated Recurrent Unit (GRU) architecture, taking inflation rate and discretionary income into consideration. This study utilizes data on criminal offences handled by the Banjarmasin District Court and data on inflation and the cost of staple foods in the Banjarmasin City markets. We evaluate the model by using RMSE and R-Squared. The results showed that the GRU-RNN model showed promising results with an R-Squared value of 0.84 and an RMSE value of 2.21.
Downloads
References
HKR Toppireddy, B. Saini, And G. Mahajan, "Crime Prediction & Monitoring Framework Based On Spatial Analysis," Procedia Comput Sci, Vol. 132, pp. 696–705, 2018.
E. Susanti And E. Raharjo, "Teaching Books of Law and Criminology." Lampung: Aura, 2013.
MR Aditama, "On the Level of Economic Crime in 17," 2021.
Nouby M. Ghazaly, A. H. H. . (2022). A Review of Using Natural Gas in Internal Combustion Engines. International Journal on Recent Technologies in Mechanical and Electrical Engineering, 9(2), 07–12. https://doi.org/10.17762/ijrmee.v9i2.365
C. Suryawati, "Understanding Poverty Multidimensionally," Journal of Health Service Management, Vol. 08, No. 03, PP. 585–597, 2005.
Y. Du, J. Wang, W. Feng, S. Pan, T. Qin, And C. Wang, "Adarnn: Adaptive Learning And Forecasting Of Time Series," Arxiv Preprint Arxiv: 2108.04443, 2021.
ASB Karno And W. Hastomo, "Optimizing Limited Data for Long-Term Predictions of Covid-19 With a Combination of LSTm and Gru," Proceedings of Sentik, Vol. 4, No. 1, PP. 181–191, 2020.
AA Rizal And S. Soraya, "Multi Time Steps Prediction With Recurrent Neural Network Long Short Term Memory," Matrix: Journal of Management, Informatics and Computer Engineering, Vol. 18, No. 1, PP. 115–124, 2018, Doi: 10.30812/Matrik.V18i1.344.
AA Rizal And S. Hartati, "Predicting Tourist Visits to Lombok Island By Implementing Recurrent Neural Networks With Extended Kalman Filter Training Algorithm," Scientific Journal of Computer Science, Vol. X, No. 1, PP. 7–18, 2017.
ML Ashari And M. Sadikin, "Time Series Sales Transaction Data Prediction Using Lstm Regression," National Journal of Informatics Engineering Education (Janapati), Vol. 9, No. 1, P. 1, 2020, Doi:10.23887/Janapati.V9i1.19140.
W. Hastomo, ASB Karno, N. Kalbuana, E. Nisfiani, And L. Etp, "Optimization of Deep Learning for Stock Prediction During the Covid-19 Pandemic," (Journal of Informatics Education and Research), Vol. 7, No. 2, PP. 133–140, 2021, Online.. Available: Https://Jurnal.Untan.Ac.Id/Index.Php/Jepin/Article/View/47411
P. Singh, "Time Series Forecasting On Crime Data In Amsterdam For A Software Company." 2018.
Vanitha, D. D. . (2022). Comparative Analysis of Power switches MOFET and IGBT Used in Power Applications. International Journal on Recent Technologies in Mechanical and Electrical Engineering, 9(5), 01–09. https://doi.org/10.17762/ijrmee.v9i5.368
TE Meskela, YK Afework, NA Ayele, MW Teferi, And TB Mengist, "Designing Time Series Crime Prediction Model Using Long Short-Term Memory Recurrent Neural Network".
X. Wei, L. Zhang, H.-Q. Yang, L. Zhang, And Y.-P. Yao, "Machine Learning For Pore-Water Pressure Time-Series Prediction: Application Of Recurrent Neural Networks," Geoscience Frontiers, Vol. 12, No. 1, PP. 453–467, 2021.
Yang, J.-P. . “A Novel Storage Virtualization Scheme for Network Storage Systems”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 1, Jan. 2022, pp. 08-13, doi:10.17762/ijritcc.v10i1.5514.
S. Mcnally, J. Roche, And S. Caton, "Predicting The Price Of Bitcoin Using Machine Learning," Proceedings - 26th Euromicro International Conference On Parallel, Distributed, And Network-Based Processing, Pdp 2018, Pp. 339–343, 2018, Doi: 10,1109/Pdp2018.2018.00060.
Q. Abbas And S.-B. Jang, "A Comparative Study Of Machine Learning Algorithms Based On Tensorflow For Data Prediction," Kips Transactions On Computer And Communication Systems, Vol. 10, No. 3, PP. 71–80, 2021, Doi: 10.3745/Ktccs.2021.10.3.71.
M. Pavithra, K. Saruladha, And K. Sathyabama, "Gru Based Deep Learning Model For Prognosis Prediction Of Disease Progression," In 2019 3rd International Conference On Computing Methodologies And Communication (Iccmc), 2019, Pp. 840–844.
Roy, R., and D. A. . Kalotra. “Vehicle Tracking System Using Technological Support for Effective Management in Public Transportation”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 2, Mar. 2022, pp. 11-20, doi:10.17762/ijritcc.v10i2.5515.
Y. Zhuang, M. Almeida, M. Morabito, And W. Ding, "Crime Hot Spot Forecasting: A Recurrent Model With Spatial And Temporal Information," In 2017 Ieee International Conference On Big Knowledge (Icbk), 2017, Pp. 143–150.
Z. Xu, W. Yixian, C. Yunlong, C. Xueting, And G. Lei, "Short-Term Wind Speed Prediction Based On Gru," In 2019 Ieee Sustainable Power And Energy Conference (Ispec), 2019, Pp. 882–887.
K. Mendome, N. Nainggolan, And J. Kekenusa, “Penerapan Model Arima Dalam Memprediksi Jumlah Tindak Kriminalitas Di Wilayah Polresta Manado Provinsi Sulawesi Utaraklorofil,” Jurnal Mipa, Vol. 5, No. 2, Pp. 113–116, 2016.
I. Darmayanti, P. Subarkah, LR Anunggilarso, And J. Suhaman, “Prediksi Potensi Siswa Putus Sekolah Akibat Pandemi Covid-19 Menggunakan Algoritme K-Nearest Neighbor,” Jst (Jurnal Sains Dan Teknologi), Vol. 10, No. 2, Pp. 230–238, 2021.
N. Handayani, H. Wahyono, J. Trianto, And DS Permana, “Prediksi Tingkat Risiko Kredit Dengan Data Mining Menggunakan Algoritma Decision Tree C. 45,” Jurikom (Jurnal Riset Komputer), Vol. 8, No. 6, Pp. 198–204, 2021.
F. Rahmadani, AMH Pardede, And N. Nurhayati, “Jaringan Syaraf Tiruan Prediksi Jumlah Pengiriman Barang Menggunakan Metode Backpropagation (Studikasus: Kantor Pos Binjai),” Jtik (Jurnal Teknik Informatika Kaputama), Vol. 5, No. 1, Pp. 100–106, 2021.
Z. Liu, Z. Zhu, J. Gao, And C. Xu, "Forecast Methods For Time Series Data: A Survey," Ieee Access, Vol. 9, Pp. 91896–91912, 2021, Doi: 10.1109/Access.2021.3091162.
N. Selle, N. Yudistira, And C. Dewi, “Perbandingan Prediksi Penggunaan Listrik Dengan Menggunakan Metode Long Short-Term Memory (Lstm) Dan Recurrent Neural Network (Rnn),” Jurnal Teknologi Informasi Dan Ilmu Komputer (Jtiik), Vol. 9, No. 1, Pp. 155–162, 2022, Doi: 10.25126/Jtiik.202295585.
JM Ackerson, R. Dave, And J. Seliya, "Applications Of Recurrent Neural Network For Biometric Authentication & Anomaly Detection," Information (Switzerland), Vol. 12, No. 7, 2021, Doi: 10.3390/Info12070272.
MD Rahman, A. Djunaidy, And F. Mahananto, “Penerapan Weighted Word Embedding Pada Pengklasifikasian Teks Berbasis Recurrent Neural Network Untuk Layanan Pengaduan Perusahaan Transportasi,” Jurnal Sains Dan Seni Its, Vol. 10, No. 1, 2021, Online.. Available: Http://Repository.Its.Ac.Id/Id/Eprint/79946
AI Caniago, W. Kaswidjanti, And J. Juwairiah, "Recurrent Neural Network With Gate Recurrent Unit For Stock Price Prediction," Telematika, Vol. 18, No. 3, P. 345, 2021, Doi: 10.31315/Telematika.V18i3.6650.
J. Christy Jackson, J. Prassanna, Md. Abdul Quadir, And V. Sivakumar, "Stock Market Analysis And Prediction Using Time Series Analysis," Materials Today: Proceedings, No. Xxxx, 2021, Doi: 10.1016/J.Matpr.2020.11.364.
IR Julianto, Indwiarti, And AA Rohmawati, “Prediksi Jumlah Kunjungan Wisatawan Di Jawa Barat Dengan Model Arimax Dan Sarimax Menggunakan Data Google Trends,” E-Proceeding Of Engineering, Vol. 8, No. 4, Pp. 4229–4241, 2021.
K. Cho, B. Van Merriënboer, D. Bahdanau, And Y. Bengio, "On The Properties Of Neural Machine Translation: Encoder-Decoder Approaches," Arxiv Preprint Arxiv:1409.1259, 2014.
J. Chung, C. Gulcehre, K. Cho, And Y. Bengio, "Empirical Evaluation Of Gated Recurrent Neural Networks On Sequence Modeling," Arxiv Preprint Arxiv:1412.3555, 2014.
JWG Putra, “Pengenalan Konsep Pembelajaran Mesin Dan Deep Learning,” Tokyo. Jepang, 2019.
MS Islam And E. Hossain, "Foreign Exchange Currency Rate Prediction Using A Gru-Lstm Hybrid Network," Soft Computing Letters, Vol. 3, No. October 2020, P. 100009, 2021, Doi: 10.1016/J.Socl.2020.100009.
A. Mellit, AM Pavan, And V. Lughi, "Deep Learning Neural Networks For Short-Term Photovoltaic Power Forecasting," Renewable Energy, Vol. 172, Pp. 276–288, 2021, Doi: 10.1016/J.Renene.2021.02.166.
KE Arunkumar, DV Kalaga, CMS Kumar, M. Kawaji, And TM Brenza, "Forecasting Of Covid-19 Using Deep Layer Recurrent Neural Networks (Rnns) With Gated Recurrent Units (Grus) And Long Short-Term Memory (Lstm) Cells," Chaos, Solitons And Fractals, Vol. 146, P. 110861, 2021, Doi: 10.1016/J.Chaos.2021.110861.
KE Arunkumar, DV Kalaga, C. Mohan Sai Kumar, M. Kawaji, And TM Brenza, "Comparative Analysis Of Gated Recurrent Units (Gru), Long Short-Term Memory (Lstm) Cells, Autoregressive Integrated Moving Average (Arima), Seasonal Autoregressive Integrated Moving Average (Sarima) For Forecasting Covid-19 Trends," Alexandria Engineering Journal, Vol. 61, No. 10, Pp. 7585–7603, 2022, Doi: 10.1016/J.Aej.2022.01.011.
X. Qing And Y. Niu, "Hourly Day-Ahead Solar Irradiance Prediction Using Weather Forecasts By Lstm," Energy, Vol. 148, Pp. 461–468, 2018, Doi: 10.1016/J.Energy.2018.01.177.
N. Aldahoul Et Al., "Suspended Sediment Load Prediction Using Long Short-Term Memory Neural Network," Scientific Reports, Vol. 11, No. 1, Pp. 1–22, 2021, Doi: 10.1038/S41598-021-87415-4.
MM Diani, Tugas Akhir – Ks 141501. 2017.
T. Chai And RR Draxler, "Root Mean Square Error (Rmse) Or Mean Absolute Error (Mae)? -Arguments Against Avoiding Rmse In The Literature," Geoscientific Model Development, Vol. 7, No. 3, Pp. 1247–1250, 2014, Doi: 10.5194/Gmd-7-1247-2014.
X. Zhang, Q. Zhang, G. Zhang, Z. Nie, Z. Gui, And H. Que, "A Novel Hybrid Data-Driven Model For Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based On Ensemble Empirical Mode Decomposition," International Journal Of Environmental Research And Public Health, Vol. 15, No. 5, 2018, Doi: 10.3390/Ijerph15051032.
F. Shahid, A. Zameer, And M. Muneeb, "Predictions For Covid-19 With Deep Learning Models Of Lstm, Gru And Bi-Lstm," Chaos, Solitons And Fractals, Vol. 140, P. 110212, 2020, Doi: 10.1016/J.Chaos.2020.110212.
S. Gao Et Al., "Short-Term Runoff Prediction With Gru And Lstm Networks Without Requiring Time Step Optimization During Sample Generation," Journal Of Hydrology, Vol. 589, P. 125188, 2020, Doi: 10.1016/J.Jhydrol.2020.125188.
E. Kasuya, "On The Use Of R And R Squared In Correlation And Regression," Ecological Research, Vol. 34, No. 1, Pp. 235–236, 2019, Doi: 10.1111/1440-1703.1011.
Ghazaly, N. M. . (2022). Data Catalogue Approaches, Implementation and Adoption: A Study of Purpose of Data Catalogue. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(1), 01–04. https://doi.org/10.17762/ijfrcsce.v8i1.2063
D. Chicco, MJ Warrens, And G. Jurman, "The Coefficient Of Determination R-Squared Is More Informative Than Smape, Mae, Mape, Mse And Rmse In Regression Analysis Evaluation," Peerj Computer Science, Vol. 7, Pp. 1–24, 2021, Doi: 10.7717/Peerj-Cs.623.
Chawla, A. (2022). Phishing website analysis and detection using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 10–16. https://doi.org/10.18201/ijisae.2022.262
U. Khair, H. Fahmi, S. Al Hakim, And R. Rahim, "Forecasting Error Calculation With Mean Absolute Deviation And Mean Absolute Percentage Error," Journal Of Physics: Conference Series, Vol. 930, No. 1, 2017, Doi: 10.1088/1742-6596/930/1/012002.
AP Setyan, BK Telematika-Petik, And ..., Prediksi Kerawanan Lokasi Terhadap Kasus Pencurian Kendaraan Menggunakan Algoritma Jaringan Syaraf Tiruan, No. August. 2021. Doi: 10.13140/Rg.2.2.16335.20646.
Garg, D. K. . (2022). Understanding the Purpose of Object Detection, Models to Detect Objects, Application Use and Benefits. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 01–04. https://doi.org/10.17762/ijfrcsce.v8i2.2066
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
