Judul: Identifikasi Radionuklida di Air di Luar Kawasan Puspiptek Dikaitkan dengan Kegiatan Pusat Penelitian Tenaga Atom Serpong
Abstrak: Radionuklida adalah isotop suatu unsur yang tidak stabil, untuk menjadi isotop unsur lain dengan melepaskan kelebihan energinya dalam bentuk radiasi nuklir. Radionuklida itu akan mengalami peluruhan sambil memancarkan radiasi berupa partikel alfa, beta, dan sinar gama. Radiasi ini akan berinteraksi dengan bahan biologis dan energi dilepaskan ke bahan tersebut. Pencemaran ekosistem oleh radionuklida dapat berasal dari sumber alami dan dari sumber buatan. Radionuklida pencemar yang masuk ke dalam ekosistem akan mengikuti lintas rantai makanan makhluk hidup dalam biosfer dan dapat berakhir pada jaringan tubuh manusia.
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Judul: Pengukuran Konsentrasi Radioaktivitas Udara Lingkungan Di Kawasan Pusat Penelitian Tenaga Nuklir, Pasar Jumat, Jakarta
Abstrak: Konsentrasi radioaktivitas udara merupakan sa1ah sam komponen yang eliulrur daIam pemantauan radioaktivitas lingkungan. Udara adaIah bagian yang sangal penting bagi keIUdupan manusia. Udara yang tercemar oleh zat radioaktif akan mudah masuk Ire dalam tubuh manusia me1alui pemafusan. Karena itu elilakukan peogukumn Konsenttasi Radioaktivitas Udara (KRU) untuk menjaga agar tingkal radioaktivitasnya tidal< mdebihi baku motu yang diizinkan (menurut SK Kopala No.02lKa-Bapeten/V-99) seItingga tidal< menimbuIkan bahaya bagi kesehatan manusia kbususnya melalui pemapasan. Penclitian ini bertujuan nntuk mengctahui dan menctapkan data efisiensi alaI pencacah dan konsentrasi radioaktivilas udara eli kawasan sckitar Pusat Pene1itian Tenaga Nuklir. Pasar Iumat. Jakarta. Data konsentrasi radioaktivitas udara yang diperoleh djbandingkan dengan nilai baku mutu radioaktivitas lingkungan menurut SK Kopala Bapeten No.02lKa-Bapeten/V-99. Pencacaban diJakukan dcngan menggnnakan sistern pencacah Geiger-Mueller (G-M) Model TGS-136 buaIan Aloka. Efisiensi sitem pencacab ditenhdcan dengan mcncacah sumber standar dan dih;mdjogkan dengan aktivitas yang telah diketahui. Pengambilan sampel udara dilakukan dengan air sampler model NR 2030 buatan Negn:tti Automation yang se\anjumya elicacah dcngan sistem pcncacah G-M dcngan se\ang waktu tertentu nntuk me1ibat laju pelurulum zal radioaklifnya. Kemudian elitentukan ni\ai Konsentrasi Radioaktivitas Udara (KRU). Nilai KRU (Knnsentrasi Radioaktivitas Udara) yang elipcroleh eli setiap tempol daIam kawasan Pusal Pene1itian Tenaga Nuklir. Pasar lumat diharapkan berada eli bawah ni\ai baku motu lingkungan menurnl SK Kepa\a Bupcten No.02lKa-BapctenlV-99. Namun dari basil yang pcroleh tcrnyata ada beberapa tempat yang berada
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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.
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