IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI PEMESANAN DRIVER GO-JEK ONLINE DENGAN MENGGUNAKAN METODE NAIVE BAYES (STUDI KASUS: PT. GO-JEK INDONESIA)

Delisman Laia, Efori Buulolo, M. J. Sirait
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引用次数: 3

Abstract

PT. Go-Jek Indonesia is a service company. Go-jek online is a technology-based motorcycle taxi service that leads the transportation industry revolution. Predictions on ordering go-jek drivers using data mining algorithms are used to solve problems faced by the company PT. Go-Jek Indonesia to predict the level of ordering of online go-to drivers. In determining the crowded and lonely time. The proposed method is Naive Bayes. Naive Bayes algorithm aims to classify data in certain classes. The purpose of this study is to look at the prediction patterns of each of the attributes contained in the data set by using the naive algorithm and testing the training data on testing data to see whether the data pattern is good or not. what will be predicted is to collect the data of the previous driver ordering, which is based on the day, time for one month. The Naive Bayes algorithm is used to predict the ordering of online go-to-go drivers that will be experienced every day by seeing each order such as morning, afternoon and evening. The results of this study are to make it easier for the company to analyze the data of each go-jek driver booking in taking policies to ensure that both drivers and consumers or customers.Keywords: Go-jek Driver, Data Mining, Naive Bayes
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数据挖掘实现,使用NAIVE BAYES(案例研究:PT. GO-JEK印度尼西亚)预测在线司机GO-JEK预订
PT. Go-Jek Indonesia是一家服务公司。Go-jek是引领交通产业革命的技术型摩托出租车服务。使用数据挖掘算法预测go-jek司机的订购情况,以解决PT. go-jek Indonesia公司面临的问题,预测在线go-jek司机的订购水平。在决定拥挤和寂寞的时候。所提出的方法是朴素贝叶斯。朴素贝叶斯算法的目的是对数据进行分类。本研究的目的是通过使用朴素算法来查看数据集中包含的每个属性的预测模式,并在测试数据上测试训练数据,以查看数据模式是否良好。预测的内容是收集之前司机的订单数据,这是基于一天,一个月的时间。朴素贝叶斯算法通过查看上午、下午、晚上等每一个订单,预测每天都会经历的在线出行司机的订单情况。本研究的结果是为了使公司更容易分析每个go-jek司机预订的数据,以采取政策,以确保司机和消费者或客户。关键词:Go-jek Driver,数据挖掘,朴素贝叶斯
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