分析雅加达降雨量分类个案支持机的内核和成本差异的准确性

Novia Pratiwi, Yudi Setyawan
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引用次数: 0

摘要

抽象。这项研究集中在一些内核功能比较,成本和精度的训练数据比例支持向量机对雅加达的降雨研究人员说。内核,线性高斯和polynomial用于修饰功能支持向量机方法,以解决非线性问题经常发生的真实情况。这项研究中使用的变量包括太阳浴温度、湿度和风速。分析结果表明,支持向量最小值没有给每人最高的准确度内核的功能。此外,数据集(训练比例:90%大小的测试):10%相比,精度更高、更准确给每人80%比例:20%内核的功能。总的来说,准确性最高90%的比例:10%由线性内核功能,同时为成本1和1000 polinom即78,38%。关键词:成本线性高斯、内核、polynomial抽象。这个研究focuses on the不那么可怜的好几个内核functions,一次和不成比例的数据训练支持向量机》到《classifying评比rainfall在雅加达。《线性高斯,与polynomial functions是应用到内核修改的《支持向量机方法去解决非线性的案子,以至于经常occur in实际条件。variables used in this study comprised》风humidity,阳光的温度和速度。《smallest支持向量了价值分析disclosed那不是。《最高为每评比价值内核。在加法,数据集(proportion》训练”,90%的测试):10% provided a有点高评比compared to the proportion》评比为80%为每内核功能:20%。工作服,《最高评比attained at the proportion of 90%的线性偏:10% (polynomial内核functions for成本1和78 1000 simultaneously,哪种是38%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ANALISIS AKURASI DARI PERBEDAAN FUNGSI KERNEL DAN COST PADA SUPPORT VECTOR MACHINE STUDI KASUS KLASIFIKASI CURAH HUJAN DI JAKARTA
Abstrak. Penelitian ini difokuskan pada perbandingan beberapa fungsi kernel, cost dan proporsi data training pada Support Vector Machine terhadap akurasi pengklasifikasian curah hujan di Jakarta. Fungsi-fungsi kernel linier, Gauss dan polynomial digunakan untuk memodifikasi metode Support Vector Machine guna menyelesaikan kasus nonlinier yang sering terjadi pada kondisi real.  Variabel yang digunakan dalam penelitian ini meliputi temperatur, kelembaban, penyinaran matahari dan kecepatan angin. Hasil analisis menunjukkan bahwa nilai support vector terkecil tidak memberikan akurasi yang tertinggi pada masing-masing fungsi kernel. Selain itu, proporsi dataset (training:testing) sebesar  90%:10% memberikan akurasi sedikit lebih tinggi dibandingkan dengan akurasi untuk proporsi 80%:20% untuk masing-masing fungsi kernel. Secara keseluruhan, akurasi tertinggi diperoleh pada proporsi 90%:10% oleh fungsi kernel linier dan polinom untuk cost 1 dan 1000 secara bersamaan yaitu 78,38%.Kata Kunci : Cost, Gauss, Kernel, linear, polynomial, Abstract. This research focuses on the comparison of several kernel functions, costs and proportions of data training on the Support Vector Machine to the accuracy of classifying rainfall in Jakarta. The linear, Gaussian and polynomial kernel functions were applied to modify the Support Vector Machine method to solve non-linear cases that often occur in actual conditions. The variables used in this study comprised of temperature, humidity, sunlight and wind speed. The analysis disclosed that the smallest support vector value did not provide the highest accuracy value for each kernel. In addition, the proportion of the dataset (training:testing) of 90%:10% provided a slightly higher accuracy compared to the accuracy for the proportion of 80%:20% for each kernel function. Overall, the highest accuracy attained at the proportion of 90%:10% by linear and polynomial kernel functions for cost 1 and 1000 simultaneously, which was 78.38%.
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