Judul: Credit risk modeling using logistic ridge regression
Abstrak: The growth of credit of national banking may cause a greater risk faced by banks. One thing we must highlight is a way to determine whether the new applicant will be good in loan repayments. A well known and widely used method for classifying the new applicant of credit is Logistic Regression. Multicollinearity is a problem that is frequently encountered in model building. Usually, variable selection method is used for handling this problem. But sometimes it creates a new problem when the important variable does not enter to the model. Logistic Ridge Regression could be an alternative in logistic regression when multicollinearity exists. The advantage of this method is that it can handle multicollinearity without deleting any predictor variables. This research compared the performance of logistic ridge regression and logistic regression with variable selection to predict the collectability status of new applicants of credit. There were 1000 observations of German Credit data set. The 740 observations were used for modeling and 260 observations were used for validation. Backward was the best among other selection variable methods which had the highest c statistic and the model was fit by Hosmer and Lemeshow Goodness-of-Fit Test. By using backward logistic regression, it showed that among 17 variables there were eight variables which were significant in the wald test. There were many significant correlations among the predictors but the highest correlation coefficient was 0.628 which exist between duration of credit (V1) and credit amount (V2).The ridge parameter or λ was 0.001. The optimal cut point of backward logistic regression was 0.680, while for logistic ridge regression was 0.677. By comparing the c statistic and the total correctly predicted cases, we can see that the logistic ridge regression was better than backward logistic regression in training data. However, with testing data (validation), backward logistic regression was better. To have a better understanding of the model with higher correlation values between V1 and V2, V2* was generated to replace V2 and logistic regression with variable selection and ridge were also built. The result pointed out that logistic ridge regression has a little higher capability to predict the new applicant’s collectability status than logistic regression with variable selection.
Keyword: 

Judul: Perbandingan Metode Diskretisasi Dalam Model Regresi Logistik (Studi Kasus : Pembentukan Model Penskoran Kredit Bank X)
Abstrak: The number of KPR customers of bank X decreased since February 2012 until August 2012. Logistic regression which is used in credit scoring model is not only able to identify the significance factors but also to know the scoring value in each category of explanatory variable. The data used in credit scoring model must be categorical data. Continuous data needs to be discretized in order to get categorical data. Discretization process can use chimerge method (model I) and equal with interval method (model II). Based on logistic regression, the factors that affect the respon are sex, employment status, education, occupation, age, and income. Correct classification table, shows that model I is better to classify than model II, therefore the scorecard is made based on model I.
Keyword: scorecard, logistic regression, equal with interval, credit scoring, chimerge

Judul: Perancangan Taman Pemakaman Umum Penggilingan, Jakarta
Abstrak: Taman Pemakaman Umum (TPU) Penggilingan merupakan bagian dari lanskap berupa areal yang disediakan untuk keperluan pemakaman jenazah khusus agama Islam. Berada di dalam kawasan pemukiman di DKI Jakarta. Luas TPU 5,7 ha dan dimanfaatkan sebagai Ruang terbuka Hijau (RTH) kota. TPU penggilingan dikelola oleh Pemerintah Daerah dalam hal ini adalah Dinas Tata Pemakaman Umum (DTPU) wilayah DKI Jakarta.
Keyword: