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.
Service Performance (SERVPERF) merupakan metode pengembangan dari Service Quality (SERVQUAL). SERVPERF menggunakan skala kinerja dan skala kepentingan untuk mengukur kualitas jasa, sedangkan Important Performance Analysis merupakan metode yang memiliki kemampuan untuk mengidentifikasi prioritas yang diperlukan untuk perbaikan. Penelitian ini bertujuan untuk menganalisis kualitas pelayanan akademik FMIPA Untan pada mahasiswa tahun 2017/2018. Data dalam penelitian ini menggunakan data primer yang diperoleh melalui penyebaran kuesioner kepada responden yang telah ditentukan. Hasil penelitian menunjukkan bahwa ada beberapa atribut yang memerlukan perbaikan seperti kemudahan memperoleh informasi bagi mahasiswa (t3), proses pelayanan cepat dan tidak berbelit terkait dengan kebutuhan mahasiswa (r2), kesabaran petugas dalam menanggapi keluhan mahasiswa (e1).Kata Kunci: Kualitas Pelayanan, SERVPERF, IPA
Pendekatan regresi nonparametrik dilakukan untuk memodelkan data yang tidak diketahui bentuk fungsinya. Salah satu regresi nonparametrik yang sering digunakan adalah regresi kernel. Tujuan penelitian ini adalah untuk mengestimasi model regresi nonparametrik menggunakan regresi kernel dengan estimator Nadaraya-Watson pada data indeks pembangunan manusia di Indonesia. Berdasarkan hasil analisis yang telah dilakukan, dapat disimpulkan bahwa untuk data indeks pembangunan manusia diperoleh bandwitdh optimal dengan estimator Nadaraya-Watson sebesar 1,384884. Hasil estimasi tersebut memperoleh nilai koefisien determinasi sebesar 63,2% dan menghasilkan nilai Mean Absolute Percentage Error (MAPE) sebesar 2,5% yang berarti bahwa kemampuan estimasi menggunakan regresi nonparametrik kernel sangat baik.Kata Kunci: regresi kernel, bandwidth, Gaussian.
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.
This research conducts a case of the cancer patients in censored data using Bayesian methodology. There are three types of loss function in Bayesian estimation method such as squared error loss function (self), linear exponential loss function (lelf) and general entropy loss function (gelf). Pareto survival model is selected as presentation data. To construct a posterior distribution, framing a likelihood function of Pareto and a prior, requires the prior distribution. An exponential distribution is chosen as a prior that describes parameter character of the Pareto. The posterior distribution is used to discover estimators in three loss functions of Bayesian methods. There are estimators held down by Bayesian self , Bayesian lelf and Bayesian gelf which substance 3.79, 3.78 and 3.90 correspondingly. After getting those estimators, the hazard functions , and and survival functions , and can be determined. The result shows that all of survival values under Bayesian approaches are lower than the real survival value. It means the result is more trusted because as a prior, the parameter is defined more precisely than before. The hazard function confirmations a same shape in all approaches. The rates of hazard are decreasing along with survival values which show the same behavior. The curves are strictly dropping after first data. This occurrence because due to a heavy-tailed character of Pareto. The result indicates that MSE of parameter estimation under the Bayesian self, lelf and gelf are 1.3x10-2, 1.2x10-2 and 0 respectively. The mse of survival estimation under the Bayesian self, lelf and gelf are 10-4, 1.1x10-4 and 3x10-5 individually. It concludes that the Bayesian gelf is the best approximation.
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