分类拍卖摩托车和汽车在南雅加达地区检察官使用Naïve贝叶斯

Fransisca Kristina Mega, Y. Yaddarabullah, S. Permana
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摘要

检察官办公室作为一个机构,在执法方面发挥作用,特别是在起诉方面。犯罪所用或者造成的财物,由国家没收。由于犯罪行为而获得的物品,该物品将被拍卖。根据财政部长第27 / PMK.06 / 2016号关于拍卖实施说明的条例,拍卖是以书面或口头出价最高的方式向公众公开出售物品,可以在线上或线下进行。南雅加达地区检察官办公室必须确定被拍卖物品的等级。然而,确定要分类的质量花了一个月的时间。因此,需要对被拍卖物品的质量进行分类研究。需要对拍卖进行分类,特别是摩托车和汽车,以减少对车辆进行分类和分类所花费的时间。标准条件用于对拍卖物品进行分类。该等级适用于发动机、外部和内部汽车、车辆年份、SNTK、BPKB形式的字母和车辆钥匙等类别。本研究使用朴素贝叶斯方法对南雅加达地区检察官办公室的拍卖项目进行分类。机动车的准确率值为74%,准确率为44%,召回率为50%,f1-score为44%;汽车的准确率值为57%,准确率为44%,召回率为50%,f1-score为44%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification Auction Motorcycle and Car In South Jakarta District Attorney Using Naïve Bayes
The Prosecutor’s Office, as an institution, plays a role in enforcing the law, particularly in the field of prosecution. Goods used or resulting from a criminal act will be confiscated and then confiscated by the state. As a result of goods obtained as a result of a criminal act, said goods are to be auctioned off. According to the Minister of Finance Regulation Number 27 / PMK.06 / 2016 regarding auction implementation instructions, an auction is an open sale of goods to the public with the highest price bid in writing or orally, and it can be done online or offline. The South Jakarta District Prosecutor’s Office must determine the grade of the items being auctioned. However, determining the quality to be categorized took a month. As a result, research is required to categorize the quality of the items being auctioned. The need to classify auctions, particularly motorcycles and cars, to reduce the time spent classifying and classifying vehicles. The standard condition is used to classify auction items. This grade applies to the categories of an engine, exterior, and interior car, vehicle year, letter in the form of SNTK, BPKB, and vehicle keys. The Naive Bayes method is used in this study to classify auction items at the South Jakarta District Prosecutor’s Office. The accuracy value of this study is 74%, 44% precision, 50% recall, and 44% f1-score for motor vehicles and 57%, 44% precision, 50% recall, and 44% f1-score for car vehicles.
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