Judul: Suatu Studi Perjanjian Indonesia - Jepang Tentang Penangkapan Ikan Tuna di Laut Banda (Banda Sea Agreement)
Abstrak: Penulisan Karya Ilmiah ini bertujuan untuk mengetahui latar belakanq beroperasinya kapal-kapal Jepang diperairan Indonesia sejak dahulu, sebelum terbentuknya negar Republik Indonesia. Denqan dalih sebagai nelayan tradisional, mereka berhasrat untuk tetap dapat beroperasi di perairan Indonesia, khususnya disekitar Laut Banda yang terkenal sebagai sumber tuna jenis bigeye didaerah Asia Tenggara.
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Judul: Studi tentang daerah penangkapan perikanan tuna longline dengan pendekatan hook rate di Perairan Indonesia
Abstrak: Besarnya potensi perikanan Indonesia, memberikan peluang untuk pengembangan industri perikanan di Indone- sia. Salah satu komoditi perikanan yang memiliki kontri- busi nyata terhadap pertumbuhan ekonomi nasional adalah ikan tuna. Prospek pengembangannya sangat besar mengingat keadaan permintaan pasar dunia akan komoditi ini masih terus meningkat. Mengingat luasnya daerah penyebaran tuna di perairan Indonesia, maka diperlukan adanya suatu pengkajian menge- nai fishing ground yang potensial sehingga pada saat operasi penangkapan tuna, dapat berlangsung seefisien mungkin dengan mengetahui fishing ground yang baik. Penelitian ini bertujuan (1) Mengkaji data hasil tangkapan Tuna Longline armada penangkapan Jepang di perairan Indonesia selama tahun 1976 1980 untuk diguna- kan sebagai bahan acuan dalam menentukan fishing ground dengan perpedoman pada nilai hook rate yang dihasilkan di setiap area penangkapan (2) Memperkirakan puncak musim penangkapan dan pola musim penangkapannya. Metode yang digunakan pada penelitian ini adalah metode analisis deskriptif. Data yang digunakan merupakan data sekunder yang diperoleh dari Annual Report of Effort and Catch Statistics by Area on Japanese Tuna Longline Fishery yang merupakan data hasil tangkapan tuna armada penangkapan Jepang yang melakukan operasi penangkapan di wilayah perairan Indonesia. Analisis data yang digunakan adalah analisis hook rate yaitu persentase jumlah hasil tangkapan tuna (ekor) per seratus mata pancing yang digu- nakan sebagai nilai CPUE, dan analisis pola musim penang- kapan dengan menggunakan metode Moving Avarage…dst
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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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