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.

ABSTRACT. This paper elaborates a research of the cancer patients after receiving a treatment in cencored data using Bayesian estimation under Linex Loss function for Survival Model which is assumed as an exponential distribution. By giving Gamma distribution as prior and likelihood function produces a gamma distribution as posterior distribution. The posterior distribution is used to find estimatior ̂ by using Linex approximation. After getting ̂, the estimators of hazard function ĥ and survival function ̂ can be found. Finally, we compare the result of Maximum Likelihood Estimation (MLE) and Linex approximation to find the best method for this observation by finding smaller MSE. The result shows that MSE of hazard and survival under MLE are 2.91728E-07 and 0.000309004 and by using Bayesian Linex worths 2.8727E-07 and 0.000304131, respectively. It concludes that the Bayesian Linex is better than MLE.

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.

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Investor menghadapi risiko dalam kegiatan investasi saham,. Salah satu ukuran risiko yang dapat digunakan adalah Value at Risk (VaR). VaR dapat mengukur peluang kerugian terburuk yang terjadi pada tingkat kepercayaan tertentu. Terdapat beberapa metode yang dapat dilakukan untuk menghitung VaR , namun metode - metode tersebut harus dievaluasi dengan backtesting agar penggunaannya tepat dalam memprediksi risiko. Backtesting dilakukan dengan menggunakan uji kejadian Bernoulli. Metode yang digunakan dalam perhitungan VaR pada penelitian ini adalah Simulasi Historis dan metode transformasi Johnson Su pada data PT. AALI, Tbk (AALI.JK) periode 2 Februari 2015 sampai dengan 1 Februari 2018. VaR relatif dalam menduga risiko pada saham AALI.JK menggunakan tingkat kepercayaan 95%. Berdasarkan hasil penelitian, metode simulasi historis boleh digunakan dengan probabilitas pelanggaran yang diharapkan sebesar 0,0075 ≤  ≤ 0,0440, sedangkan dengan menggunakan metode pendekatan transformasi Johnson  boleh digunakan dengan probabilitas pelanggaran yang diharapkan sebesar 0,0154 ≤  ≤ 0,0609. VaR absolut dalam menduga risiko pada saham AALI.JK menggunakan tingkat kepercayaan 95%. Pada VaR absolut, metode simulasi historis boleh digunakan dengan probabilitas pelanggaran yang diharapkan sebesar 0,0032 ≤  ≤ 0,0319, sedangkan dengan menggunakan metode pendekatan transformasi Johnson  boleh digunakan apabila probabilitas pelanggaran yang diharapkan sebesar 0,0075 ≤  ≤ 0,0440.  Kata kunci: Value at Risk, Simulasi Historis, Transformasi Johnson, Backtesting, uji kejadian Bernoulli

Model Vector Autoregressive Exogenous (VARX) dapat digunakan untuk memprediksi data deret waktu lebih dari satu variabel yang menggunakan variabel eksogen dalam sistem persamaannya. Model VARX menggunakan variabel endogen yang saling berhubungan dipengaruhi waktu sebelumnya dan terdapat variabel eksogen yang mempengaruhi variabel endogen tersebut. Penelitian ini menggunakan data sekunder, yaitu data bulanan deret waktu hasil produksi karet kering dan karet basah dalam periode tahun 2016 sampai tahun 2018 pada kebun Sintang di Kalimantan Barat serta data deret waktu curah hujan pada kebun . Tujuan penelitian ini adalah melakukan pemodelan dan memprediksi hasil produksi karet kering dan karet basah PTPN XIII pada periode Juli sampai Desember 2018 dengan model VARX. Hasil analisis menunjukkan bahwa model VARX yang dapat diterapkan terhadap data hasil produksi karet kering dan karet basah adalah VARX(1,1). Nilai MAPE untuk model VARX(1,1) pada masing-masing variabel yaitu 14,73% dan 16,06% sehingga ketepatan hasil prediksi model dapat dikatakan baik. Kata Kunci: karet basah, karet kering, curah hujan, MAPE

Kredit Tanpa Anggunan (KTA) are bank loans given to debtors without asking for guarantees. Some debtors who have made KTA but still need additional loan funds can top up. However, offering this facility to the public cannot be separated from the risk that the debtor and/or other parties fail to fulfill their obligations to the bank. In an effort to assess the feasibility of prospective debtors, banks need decision-making tools so that they can easily and quickly estimate which debtors are able to pay off credit on time (good credit). The tool that is often used is classification. In this study, we will compare 3 classification methods, namely k-nearest neighbor, binary logistic regression, and classification tree, to obtain the best method for analyzing the feasibility of giving KTA top-up. Based on the accuracy value in each method, the classification tree produces the highest accuracy value compared to the other two methods. Thus, for this study, the classification tree is the best method, with an accuracy value of 87.68%. The variables used in the classification tree are DBR, length of service of a debtor, credit limit, type of debtor's occupation, the total income of the debtor, the area where the debtor lives, and the credit period of the debtor is 1 month.