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Journal : Scientific Journal of Computer Science

A Statistical Approach to Crime Rate Prediction Using Multiple Linear Regression Wijaya, Setiawan Ardi; Arribe, Edo; Muhitualdi
Scientific Journal of Computer Science Vol. 1 No. 2 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i2.2025.47

Abstract

The high crime rate in Riau Province poses a serious threat to social stability and public safety, requiring accurate prediction strategies to support crime prevention efforts. Based on data from the Central Statistics Agency (BPS), Riau ranked seventh among the provinces with the highest crime rates in Indonesia in 2022, indicating that conventional prevention efforts remain insufficient. However, studies applying statistical data-based prediction models to crime in Riau are still limited, creating a gap in data-driven decision making. This study aims to develop a crime rate prediction model in Riau Province using the Multiple Linear Regression (MLR) method with BPS crime data from 2019–2023. The independent variables include six types of crime: corruption, drug dealers, drug users, terrorism, illegal logging, and human trafficking, while the dependent variable is the total number of crimes per district or city. The research process involved data collection, understanding, preprocessing, application of linear regression algorithms, model training and testing, and evaluation using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results show that Pekanbaru City recorded the highest number of cases, mostly related to drug crimes. The model predicts an increase in Pekanbaru’s cases from 3,331 in 2024 to 5,852 in 2027, while Dumai City is projected to decline from 543 to 397 cases. The model demonstrates high accuracy in most areas, particularly in Kampar (MAPE 0.28%), Siak (0.52%), and Rokan Hilir (0.94%), though less accurate in the Meranti Islands (565.99%) due to data instability. These findings prove that the Multiple Linear Regression method effectively predicts crime trends and can serve as a quantitative decision-making tool for law enforcement and local governments. Further research should include socioeconomic factors such as poverty and unemployment, and compare results with alternative forecasting methods like ARIMA and Exponential Smoothing to enhance prediction accuracy.
Co-Authors Abdennasser, Dahmani Abdul Fadlil Abid Yanuar Badharudin Abraar, M. Said Ada, Yosia Agus Prasetyo Marsaid Ahmad Bukhari, Ahmad Al-Sabur, Raheem Alfian Ma’arif Aliyah khalista putri Angellina Nurul Qulbi Siburian Anton Yudhana Anton Yudhana Ariska Fitriyana Ningrum Aryanto Asno Azzawagama Firdaus Br Bangun, Elsi Titasari Brilianti Nafilah, Rizkiya Dahmani, Abdennasser Doni Winarso, Doni Driss, Zied Dwi Purnomo, Raka Edo Arribe, Edo Fanani, Galih Pramuja Inngam Fauzan, Al fana Fauzan, Wahyu Fazilla, Rahma Muti Firmansyah Firmansyah Fitri Anggraini Fladea, Saskia Azki Furizal, Furizal Galih Pramuja Inngam Fanani Gerry Julian Hafsari, Rizka Hasanah, Rakyatul Hasanah, Zakiyah Helwina Putri Rahmadani Kariyamin, Kariyamin Laia, Ertin Mardiyanto, Silvia Marisa, Vania Mihardi, Hansen Muhammad Amirul Mu'min Muhammad Shafwannullah Abdul Aziz Muhammad Wahyu Illahi Muhitualdi Murni Murni Muzdhalifatul Ijfi, Inessthasia Nakib, Arman Mohammad Novi Tristanti Novi Tristanti Nurhikmayani, Nurhikmayani Pratama, Dzaky Medlin Pratama, Nanda Dean Putera, Ardi Maulia Putri, Jossie Mutiarani Putri, Tia Refviani Rafif Syahril Nugraha Ramadhan, Aziz Rizki Risnal Diansyah, Risnal Safuan, M. Chairil Sarohim, Nabil saskia, ananda Sharkawy, Abdel Nasser Sharkawy, Abdel-Nasser Silpandi, Dimas Sulistiani Sulistiani, Sulistiani sunardi sunardi Sunardi Sunardi Sunardi, Sunardi Suwarno, Iswanto Syahril Syaputri, Qori Monica Tri Stiyo Famuji Tristanti, Novi TSABITAH, NAYLA Wahyu Ningsih, Dwi Putri Wide Mulyana Wiyrabawa Kainna Putra Rachman Yana Safitri Yana Safitri Yana Safitri, Yana Yusuf Hendra Pratama Zahran Ramdani, Ibnu Ziad Iqbal, Muhammad