基于三指数平滑和层次分析法的优先级时间巡逻预测

N. Nurhaeni, Azhari Sn, Agus Byna
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引用次数: 0

摘要

预测是一个系统的过程,它根据当前的历史数据确定未来会发生什么,从而减少不准确性。本研究的目的是预测未来的罪案数量,以作为决策支持的基础,以确定警察执行巡逻任务的时间优先次序。研究人员将层次分析法(AHP)作为决策支持的方法之一,将三指数平滑法作为预测犯罪数量的方法。研究结果采用三指数平滑法,选取犯罪发生的脆弱时间为黎明、下午、傍晚和夜间,平均准确率为63.60%。在AHP方法中,每个层次的两两比较评估始终给出小于0.1的CR值。因此,两种方法产生的巡逻时间优先级是可以接受的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Priority Time Patrol using Triple Exponential Smoothing and AHP
Prediction is a systematic process of determining what will happen in the future based on current historical data, which reduces inaccuracies. The purpose of this study is to predict the number of crimes in the future so that it can be used as a basis for decision support to determine time priorities for the police to carry out patrol duties. Researchers use Analytical Hierarchy Process (AHP) as one of the methods used to make decision support and Triple Exponential Smoothing as a method used to predict the number of crimes. The study's results used the Triple Exponential Smoothing method with a vulnerable time when the crime occurred at dawn, afternoon, evening, and night with an average accuracy of 63.60%. In the AHP method, the pairwise comparison assessment for each hierarchy consistently gives a CR value of less than 0.1. Thus, the priority of patrol time generated by the two methods is acceptable.
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