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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal technoscientia Journal of Fundamental Mathematics and Applications (JFMA)
Setyawan, Yudi
Institut Sains & Teknologi AKPRIND Yogyakarta
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering

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PENERAPAN PENEMPATAN NILAI EIGEN INFINITE SISTEM SINGULAR PADA PENYELESAIAN PERSAMAAN POLINOMIAL MATRIKS BERBENTUK [Es – A] X + B Y = U(s) Suryowati, Kris; Setyawan, Yudi
JURNAL TEKNOLOGI TECHNOSCIENTIA Technoscientia Vol 5 No 1 Agustus 2012
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (401.265 KB) | DOI: 10.34151/technoscientia.v5i1.512

Abstract

Problem of solvability of polynomial equations and matrix eigenvalue relation to the placement of an infinite state-feedback is important to learn because it deals with the properties of dynamic and static systems. In this case discussed the problem with putting the infinite eigenvalue decomposition of the standard, then the results are applied to problem solving matrix polynomial equations. On eigenvalue placement or placement of the poles, the problem is determining the state feedback matrix K such that det [Es - A + BK] = a ≠ 0, in a and s with each other independent. Singular linear system that has an infinite eigenvalue will be formed in such infinite eigenvalues ​​are placed so that the system has no eigenvalues ​​of infinite state by providing appropriate feedback. Problems on infinite eigenvalue assignment can be attributed to the determination of polynomial equation solution in the form of matrix [Es - A] X + BY = U(s) for a matrix U(s) with detU(s) = a, so that necessary and sufficient conditions of
PEMODELAN SPASIAL AREA PADA DATA COVID-19 PULAU JAWA BERBASIS R-SHINY WEB FRAMEWORK Rokhana Dwi Bekti; Yudi Setyawan; Enik Laksminiasih
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 3 (2021): May, 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v17i3.11743

Abstract

The Covid-19 in Indonesia has had an impact on almost all lives, especially at economic, social, education, and health.. Efforts to prevent and reduce the number of cases are still ongoing. Likewise, research on the causes of the emergence of the Covid-19 pandemic outbreak, drugs, vaccines, and the factors that influence it are still being carried out. This study analyzes the effect of Covid-19 on inflation and the effect of population density on Covid-19 in Java. The method used is area spatial modeling. To make it easier for researchers to analyze data, this study also developed a web application based on the R shiny framework. This application has displayed valid output from the results of its use and is in accordance with existing theories, and is able to make it easier for users to carry out Covid-19 analysis in Java using the area spatial model method. The estimation results of the Spatial Durbin Model (SDM) show that the variable that has a significant effect on inflation is the inflation lag in the model with cumulative positive cases (α = 10%). This shows that the inflation of a province tends to be influenced by other neighboring provinces. Meanwhile, population density is also significant for Covid-19 positive cases (α = 5%).
ANALISIS AKURASI DARI PERBEDAAN FUNGSI KERNEL DAN COST PADA SUPPORT VECTOR MACHINE STUDI KASUS KLASIFIKASI CURAH HUJAN DI JAKARTA Noviana Pratiwi; Yudi Setyawan
Journal of Fundamental Mathematics and Applications (JFMA) Vol 4, No 2 (2021)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1387.13 KB) | DOI: 10.14710/jfma.v4i2.11691

Abstract

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%.
Co-Authors Enik Laksminiasih Kris Suryowati, Kris Noviana Pratiwi Rokhana Dwi Bekti