Judul: Perbandingan Pendugaan Parameter Regresi Distribusi Normal dan Distribusi Eksponensial dengan Maximum Likelihood Estimation (MLE)
Abstrak: Analisis regresi merupakan salah satu analisis statistika yang mempelajari hubungan antara variabel dependen dan variabel independen. Variabel dependen tidak selalu berdistribusi normal, melainkan termasuk ke dalam sifat distribusi yang lebih luas yang disebut dengan distribusi dari famili eksponensial. Untuk variabel dependen yang termasuk ke dalam famili eksponensial, maka dapat dimodelkan dengan Generalized Linear Models (GLM). Metode yang dapat digunakan untuk menduga parameter pada GLM pada regresi eksponensial adalah Maximum Likelihood Estimation (MLE) dengan menggunakan konsep Iterative Re-Weighted Least Square (IRLS). Pada data yang menyebar eksponensial, jika dilakukan pendugaan parameter dengan regresi normal akan menghasilkan pendugaan parameter yang kurang baik. Oleh karena itu, data harus dimodelkan sesuai dengan asumsi sebarannya yaitu dengan menggunakan GLM., Regression analysis is a statistical analysis that studies the relationship between the dependent variable and the independent variable. The dependent variable is not always normally distributed but belongs to a wider distribution called the distribution of the exponential family. For the dependent variable that belongs to the exponential family, it can be modeled using Generalized Linear Models (GLM). The method that can be used to estimate the parameters of GLM in exponential regression is Maximum Likelihood Estimation (MLE) using the Iterative Re-Weighted Least Square (IRLS) concept. In exponentially distributed data, if the parameter estimation is carried out with normal regression, it will result in an unfavorable parameter estimate. Therefore, the data must be modeled according to the distribution assumption, namely by using GLM.
Keyword: exponential family, Generalized Linear Model (GLM), Iterative Re- Weighted Least Square (IRLS), Maximum Likelihood Estimation (MLE), regression analysis

Judul: Perbandingan Metode MKT, LTS, WIN, dan Theil pada Pendugaan Parameter Regresi Apabila Galatnya Menyebar Eksponensial
Abstrak: Parameter estimation in simple linear regression analysis can be done with Ordinary Least Square (OLS). This method requires the assumption that error must be Normally distributed. However, in reality error is not always Normally distributed, but has another distribution, for example Exponential. Concequently, parameter estimation by MKT is not optimal. This problem can be solved by applying robust regression. This research applied Least Trimmed Square (LTS) method, Winsorized Least Square (WIN) method, and Theil’s method as a solution. The data used in this study was simulation data. Explanatory variables (x) is generated from the Normal distribution, while the error generated from the Exponential distribution. The results of estimation are evaluated with MSE and Relative MSE. The results showed that LTS was the best method when the error is Exponentially distributed.
Keyword: Bogor Agricultural University (IPB), WIN, Theil’s Method, OLS, LTS

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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