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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Rizki, Setyo Wira
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Invertibility of Generalized Space-Time Autoregressive Model with Random Weight Yundari, Yundari; Rizki, Setyo Wira
CAUCHY Vol 6, No 4 (2021): CAUCHY: Jurnal Matematika Murni dan Aplikasi
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v6i4.11254

Abstract

The generalized linear process accomplishes stationarity and invertibility properties. The invertibility property must be having a series of convergence conditions of the process parameter. The generalized Space-Time Autoregressive (GSTAR) model is one of the stationary linear models therefore it is necessary to reveal the invertibility through the convergence of the parameter series. This article studies the invertibility of model GSTAR(1;1) with kernel random weight. The result shows that the model GSTAR(1;1) under kernel random weight fulfills the invertibility property and obtains a finite order of Generalized Space-Time Moving Average (GSTMA) process. The other result obtained is the time order of the finite orde  . On the Triangular kernel resulted in the relatively great value n, so that it does not apply to the kernel with a finite value n.
Analisis Kelayakan Kredit Menggunakan Classification Tree dengan Teknik Random Oversampling Vebriyanti, Lo Mei Ly; Martha, Shantika; Andani, Wirda; Rizki, Setyo Wira
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 12 Issue 1 June 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i1.24182

Abstract

Credit is providing money or bills based on the agreement between a bank and another party. Lending is inseparable from bad credit risk, so credit analysis must be conducted on prospective debtors before approving a proposed loan. This research aims to analyze creditworthiness using a Classification Tree as a classification method with Random Oversampling to overcome imbalanced data. This study uses secondary data on the status of debtors from a bank in West Kalimantan. Research data amounted to 800 data samples consisting of collectability variables as target variables and 10 independent variables, namely limit, rate, tenor, total installments, age, salary, premium and admin, agency, type credit, and type need. The Classification Tree method with Random Oversampling is used to overcome imbalanced data. Classification begins with data preprocessing, then the data is divided into training and test data with proportions of 70:30, 80:20 and 90:10 for each treatment without Random Oversampling and with Random Oversampling. Next, a classification model is formed using training data, and the classification model is validated using test data. After that, an overall evaluation of the model is carried out to determine the best model used in the classification process. Based on the research results, the best model is the model Classification Tree with Random Oversampling in proportion 70:30, with an accuracy value of 89.17%, specificity of 75.00%, and recall of 89.66%. The model can be used to classify current and non-current debtor data. The most influential variable in classifying debtor status is the total installment variable.
PENERAPAN METODE GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) PADA KASUS KEMISKINAN DI INDONESIA Martha, Shantika; Yundari, Yundari; Rizki, Setyo Wira; Tamtama, Ray
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 2 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (392.215 KB) | DOI: 10.30598/barekengvol15iss2pp241-248

Abstract

To analyze the factor affecting poverty during several periods by considering some geographical factors, we can use a geographically weighted panel regression (GWPR) method. GWPR is a combination of the geographically weighted regression (GWR) model and the panel regression model. The research conducts to identify the factors affecting the percentage of poor people in 34 provinces in Indonesia during 2015-2019. The results show that a suitable GWPR model is a fixed-effect model (FEM) with an exponential adaptive kernel function. Referring to the model, the province is divided into four groups based on variables having a significant effect on the percentage of poor people. That factors causing the poor people percentage in Indonesia are the poor people percentage aged above 15 years old and unemployment, the people percentage aged above 15 years old and employed in the agricultural sector, the literacy rate of the poor aged between 15 to 55 years old, and the life expectancy rate. Keywords: fixed effect model, exponential adaptive kernel.
Co-Authors Andani, Wirda Martha, Shantika Tamtama, Ray Vebriyanti, Lo Mei Ly Yundari, Yundari