Judul: Kinerja Sistem Manajemen Lingkungan IS0 14001  Pusat Metalurgi Mentok PT. Tambang Timah, Bangka
Abstrak: IS0 14001 adalah standar internasional Sistem Manajemen Lingkungan (SML) yang dikembangkan oleh International Organization for Standardization (ISO). IS0 14601 merupakan alat untuk mencapai dan mengontrol secara sistematis tingkat kepedulian organisasi dalam mencegah pencemaran, pentaatan terhadap peraturan perundang-undangan dan perbaikan terus menerus sejalan tlengan kebijakan lingkungan organisasi Tujuan penelitian ini adalah mengkaji perubahan kinerja lingkungan dan efisiensi usaha Pusat Metalurgi (Pusmet) Mentok PT. Tambang Timah setelah menerapkan SML IS0 14001 dan menelaah sejauh mana SML IS0 14001 yang diadopsi oleh Pusmet Mentok PT. Tambang Timah dapat digunakan untuk menghadapi perubahan situasi internal dan eksternal organisasi.
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Judul: Manfaat lmplementasi Sistem Manajemen Lingkungan IS0 14001 pada PT Pupuk Kujang, Cikampek, Jawa Barat.
Abstrak: Pengenalan dan penerapan IS0 9000 di lndonesia bisa dikatakan terlambat dan tertinggal dibandingkan dengan negara-negara lain di dunia. Untuk mencegah ha1 yang sama terjadi pada penerapan IS0 14000 maka sedini mungkin pengenalan tentang manajemen lingkungan ini hams dilakukan. Salah satu penyebab ketertinggalan lndonesia dalam penerapan sen standar internasional IS0 14000 adalah kurang memasyarakatnya pengetahuan mengenai sistem standar lingkungan ini. Kalangan industri juga belum begitu merasakan dan menyadari pentingnya penerapan sistem standar ini disebabkan kurangnya informasi mengenai implikasi, baik berupa manfaat maupun konsekuensi, sertifikasi standar internasional IS0 14000. Sebagian besar masih beranggapan konsekuensi berupa biaya dan kerja keras akan lebih besar dari pada manfaat yang diterima baik untuk jangka pendek maupun jangka panjang.
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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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