东龙目岛渔民福利分析使用随机森林算法

Arnila Sandi, K. Kusrini, Kusnawi Kusnawi
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引用次数: 0

摘要

沿海地区人民的经济生活,特别是渔民的经济生活,非常依赖周围的自然资源,例如海洋资源,海洋资源在渔业社区的生存中仍然占据首要地位,被广泛使用,也被列为可再生自然资源。作为本研究材料的一个例子是西努沙登加拉东龙目岛的渔业社区。渔民社区可以被理解为以渔民为主要生计的一群人。这个社区的生活特点不同于一般社会。自然因素对他们的生活影响很大,从他们的生活方式到他们的经济和福利水平,这与其他社区不同。本研究的目的是利用分类方法和随机森林算法预测东龙目岛、西努沙登加拉渔业社区的福利水平。使用的数据集是私有数据,数据取自钓鱼应用程序。对数据进行处理,使算法的结果或性能达到预测的最佳结果。从现有的数据集中,我们使用了五个支持变量,包括教育、家庭成员、水井(与清洁水有关)、就业和住房。这种数据处理的结果或目标是处于繁荣和不繁荣状态的渔业社区的福利水平。本研究的最终结果是使用混淆矩阵,其中最终结果是精度值。随机森林的准确率最高,为93.37%,AUC为0.735%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analisa Prediksi Kesejahteraan Masyarakat Nelayan Lombok Timur Menggunakan Algoritma Random Forest
The economic life of the people on the coast, especially fishermen, is very dependent on the natural resources that are around, for example, marine resources, which still get the top position in the survival of fishing communities which are widely used and are also included as renewable natural resources. One example used as material for this research is the fishing community in East Lombok, West Nusa Tenggara. The Fishermen's Community can be interpreted as a group of people whose main livelihood is fishermen. The characteristics of the life of this community are different from society in general. Natural factors influence their lives a lot, from their lifestyle to the level of their economy and welfare, which is different from other communities. The purpose of this study is to predict the level of welfare of fishing communities in East Lombok, West Nusa Tenggara by using the classification method and the Random Forest algorithm. The dataset used is private data, the data is taken from fishing applications. Data processing is done to get the result or performance of the algorithm as the best result in predicting. From the existing dataset we use five supporting variables including, Education, family members, wells (related to clean water), employment and housing. The results or targets of this data processing are the level of welfare of fishing communities with prosperous and non-prosperous statuses. The final results of this study are seen using the Confusion Matrix, where the end result is the accuracy value. Random Forest has the highest accuracy value with a value of 93.37% and an AUC value of 0.735%.
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