设计与实施 利用 K-Nearest Neighbor (KNN) 学习印尼香料香薰的分类方法

Maulia Wijiyanti Hidayah, M. Ashar, I. M. Wirawan
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引用次数: 0

摘要

印度尼西亚拥有多种对人类有益的植物。有超过 31 种药用植物是传统医药和香料行业所需的材料之一。传统香料药物来自印尼香料植物。这些香料可以产生芳香疗法,包括精油。芳香疗法有助于保持人体健康。因此,有必要使用 K-Nearest Neighbor 对印尼香料中的芳香疗法进行分类。K-Nearest Neighbor 算法的准确率为 97.5%,使用混淆矩阵对准确率数据进行测试,然后进行前端和后端测试,结果显示应用程序设计有效,使用 weka 应用程序的准确率为 97.5%。这项研究将产生可供安卓用户访问的安卓应用程序等产品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design and Implementation The Learning of Classification Aromatherapy made from Indonesian Spices using K-Nearest Neighbor (KNN)
Indonesia is country that has various plants which have many benefits for human. There are more than 31 types of medicinal plants as one of material that needed by industry as traditional medicine and spices. Traditional spiceal medicine comes from spiceal plants that used from Indonesian spices. This spices can produce aromatherapy include essential oils. Aromatherapy can help in maintaning the healthy of human body. This is necessary that aromatherapy can be classsified using K-Nearest Neighbor to classify aromatherapy from Indonesian spices. The accuracy result which shown by K-Nearest Neighbor algorithm is 97.5 percent, the accuracy data testing using confusion matrix which will be followed by front end and back end testing that shown the valid result for application design and valid using weka application with an accuracy result of 97.5 percent. This research will produce product such as android application that can be accessed by android users.
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