Judul: Kajian Sistem Pengendalian Suhu Pada Proses Pematangan Buatan Buah Pisang Susu (Musa sativa L.)
Abstrak: Buah-buahan tropika termasuk didalamnya pisang merupakan produk hortikultura Indonesia yang sudah selayaknya mendapat tempat khusus untuk dikembangkan. Teknik penanganan pasca panen yang kurang baik akan mengakibatkan produk kurang dapat diserap oleh pasar. Oleh karena itu diperlukan teknik penanganan pasca panen yang dapat mengendalikan tingkat kematangan dan lama penyimpanan serta dapat menekan kerusakan yang terjadi pada buah tersebut, salah satunya adalah dengan pengendalian suhu pada saat pemeraman dan penyimpanan.
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Judul: Pengarus Suhu dan Penambahan Gas Etilen pada Kelembaban Tinggi terhadap Kecepatan Proses Pemeraman dan Mutu Buah Pisang (Musa paradisiaca) cv. Ambon Putih
Abstrak: Buah pisang disamping sebagai salah satu komoditas hortikultura yang bernilai ekonomi tinggi, juga merupakan bahan pangan penting bagi segala lapisan masyarakat, karena banyak mengandung karbohidrat, cukup mengandung protein, lemak, vitamin A, B, C serta mineral. Masalah yang sering timbul dalam pemasaran pisang segar adalah pematangan yang tidak sesuai dengan tipe pasar yang akan dimasuki. Pisang sering terlalu cepat matang sehingga menjadi busuk atau belum matang penuh (masih mentah) ketika sampai pada pasar tujuan. Penelitian ini bertujuan untuk mengetahui pengaruh kombinasi perlakuan konsentrasi etilen dan suhu pada kelembaban tinggi terhadap mutu dan kecepatan pemeraman buah pisang ambon putih. Penelitian dibagi ke dalam dua tahap penelitian yaitu penelitian pendahuluan dan penelitian utama. Penelitian pendahuluan dibagi lagi menjadi 2 tahap penelitian pendahuluan yaitu tahap pertama bertujuan untuk menentukan tahap- tahap deskripsi kematangan buah pisang ambon putih tanpa perlakuan pada suhu kamar dan suhu 15'C, tahap kedua
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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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