Judul: Interval Pemberian Air dengan Sistim Irigasi Curah sesuai dengan Tingkat Pertumbuhan Tanaman dan Sifat Fisik Tanah di Perkebunan Tembakau Sampali PTP IX Medan - Sumatera Utara
Abstrak: Pemberian air irigasi antara lain bertujuan untuk memenuhi kebutuhan tanai,~an akan a i r , pada waktu dan jumlah yang tepat agar diperoleh hasil dan kualitas yang baik. Beberapa parameter yang penting diperhatikan untuk menghasilkan suatu sistim pengelolaan irigasi adalah ketersedian surnber air yang ada, pola pertanaman, jumlah kebutuhan air , dan jenis tanaman yang diusahakan.
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Judul: Penentuan Waktu Pemberian Air Irigasi Berdasarkan Jumlah dan Jarak Penetes pada Sistem Irigasi Tetes untuk Tanaman Palawija
Abstrak: Air yang cukup sangat penting bagi pertumbuhan tanaman. Tanaman menyerap air dari dalam tanah untuk pertumbuhannya. Air yang dibutuhkan tanaman akan terus berkurang dan sulit diserap tanaman apabila tidak ada tambahan air hujan atau air tanah. Dalam keadaan ini pemberian air irigasi perlu dilakukan untuk menjarnin pertumbuhan tanaman yang baik dengan menambah kadar air tanah (Sosrodarsono dan Takeda, 1978).Jadwal irigasi, waktu pemberian air dan jumlah air yang diberikan adalah masalah yang sangat kompleks. Faktor utama yang mempengaruhi jadwal irigasi adalah kebutuhan air tanaman, sifat tanah yang menunjukkan kapasitas menyimpan air di daerah perakaran, pertumbuhan perakaran tanaman dan toleransi tana.man terhadap penurunan kelembaban. Dalam perencanaan skkdul irigasi, Eaktor tambahan yang perlu dipertirnbangkan adalah karakteristik hidrolik, metode dan praktek irigasi. kondisi daerah dan iklim, operasi lahan $ang mempengaruhi waktu iriqasi dan kondisi lahan yang berhubungan dengan usaha manusia dan proses produksi (Buras, N, et al., 1973) .
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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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