用Apriori算法验证印尼农村垃圾处理实践问题

Aa Zezen Zaenal Abidin, M. Othman, Aslinda Hassan, Yuli Murdianingsih, Usep Tatang Suryadi, Zulkiflee Muslim
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引用次数: 0

摘要

在引入使用新技术、过程和实践的实际解决方案之前,验证一组最常见的问题是必不可少的。本研究提出了一种验证这些问题集的方法。本文的主要贡献是使用Google Earth可视化和Apriori算法验证以前通过调查问卷确定的废物处理实践中最常见的一组问题的方法。谷歌地球被用来精确定位现有垃圾箱、非法垃圾填埋场和人们房屋的地理位置。垃圾箱与居民住宅之间的距离、焚烧处理垃圾的地点、倾倒处理垃圾的地点,然后作为废物处理实践问题的组合进行分析。然后计算支持度、置信度、支持度和置信度之间的乘法以及提升比值,以获得最常见问题集的组合。接下来,使用Rapidminer将Apriori算法中的支持值与FP-Growth方法进行比较。结果获得支持,从而验证先前从调查中获得的数据。对于2项集问题,最小支持值为0.1,获得33%的准确率,而3项集问题返回99%的准确率。我们表明,我们的方法在验证以前从其他来源获得的数据是有用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Verifying Waste Disposal Practice Problems of Rural Areas In Indonesia Using the Apriori Algorithm
Verifying a set of most frequent problems is essential before introducing practical solutions using new technology, processes, and practices. This study proposes a way to verify these problem sets. The main contribution of this paper is a method to verify a set of most frequent problems in waste disposal practices previously identified through a survey questionnaire, using Google Earth visualization and the Apriori algorithm. Google Earth is used to pinpoint the geographical locations of existing waste bins, illegal landfills, and people's houses. The distance between the waste bins and the residents' houses, sites of waste disposal by burning, and sites of waste disposal by dumping are then analyzed as a combination of the problems of waste disposal practices. Support, Confidence, multiplication between Support and Confidence, and lift ratio values are then calculated to obtain a combination of the most frequent problems sets. Next, the support value in the Apriori algorithm is compared with the FP-Growth method using Rapidminer. Results obtain support and thus verify data previously obtained from the survey. For a 2-itemset problem and a minimum support value of 0.1, 33% accuracy is obtained, while a 3-itemset problem returns 99% accuracy. We show that our method is useful in verifying data previously obtained from other sources.
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