Judul: Classifier Learning For Imbalanced Dataset Using Modified SMOTEBoost Algorithm And Its Application On Credit Scorecard Modeling
Abstrak: In many real cases, imbalanced class problem are often founded. A dataset is called imbalanced if there is one or more classes that are dominating the whole dataset as majority classes, and other classes becoming minority. In statistical classification, imbalanced class is a serious problem. This could lead the model less sensitive in classifying minority class objects. In many cases, misclassifying the minority class objects could have bigger problem than misclassifying the majority class. For instance in credit scorecard, accepting “bad” applicant will be much risky than rejecting “good” applicant. This study will discuss a hybrid technique between SMOTE and Boosting, called SMOTEBoost to overcome imbalanced class issue. SMOTE generates synthetic samples based on the objects’ characteristics and the k-nearest neighbor. Synthetic samples generation has different procedure for each numerical and categorical variable. Euclidian distance is used for numerical variable, while the value’s mode can be simply used for categorical variable. “Boosting” is a general method for improving the performance of any learning algorithm. In theory, boosting can be used to significantly reduce the error of any “weak” learning algorithm that consistently generates classifiers which only a bit better than random guessing. The popular variant of boosting called “AdaBoost”, an abbreviation for adaptive boosting. SMOTEBoost is a combination of SMOTE and boosting algorithm. The purpose of this combination is to create a powerful model in classifying imbalanced class dataset without sacrificing the overall accuracy. Decision tree, with CART algorithm, will be used in each boosting iteration. From the analysis result, the combination of SMOTE and Boosting has proven that it gives significantly better performance than CART. While SMOTE let the classifier improves the performance on the minority class, boosting procedure improves the predictive accuracy of any classifier by focusing on difficult objects. On comparison, SMOTEBoost produce better separation between good and bad class which is represents by higher performance measures (KS-Statistics, Area under ROC, and Accuracy) than CART. It also turns out that SMOTEBoost produces stable percentage between sensitivity and specificity, and this is the difference between SMOTEBoost and CART. SMOTEBoost produces good performance in predicting minority class (bad) as well as maintaining good performance in predicting majority class (good). Meanwhile if CART used, it will only gives good performance on predicting majority class. Besides having better performance compare to CART, SMOTEBoost also maintains stable performance across different bad rate. The stability assessment result shows SMOTEBoost gives good and stable performance, even though the bad rate is set to be significantly lower than the good rate.
Keyword: Imbalanced class, SMOTE, Boosting, Credit Scorecard

Judul: Analisis Regresi Logistik dan CART untuk Credit Scoring dengan Penanganan Kelas Tak Seimbang
Abstrak: Tidak adanya jaminan pada suatu jenis kredit akan membuat risiko kredit (gagal bayar) yang dihadapi oleh pihak bank semakin tinggi. Penerapan prinsip kehati-hatian oleh bank diterapkan dengan mengelola portofolio kredit yang dimiliki, sehingga potensi risiko yang terjadi dapat diukur dan dikontrol dalam suatu model. Credit scoring menggambarkan seberapa besar kemungkinan debitur akan macet dengan pembayaran. Pada penelitian ini dibandingkan pemodelan credit scoring antara analisis regresi logistik dan Classification and Regression Trees (CART) dalam mengklasifikasikan debitur yang nantinya dapat digunakan oleh bank untuk membuat atau mengevaluasi kebijakan terkait kredit. Permasalahan klasifikasi pada ketidakseimbangan kelas salah satunya dapat ditangani dengan Synthetic Minority Oversampling Technique (SMOTE). Hasil penelitian ini menunjukkan bahwa analisis Regresi Logistik dengan penanganan SMOTE menghasilkan kinerja yang lebih baik karena memiliki nilai sensitifitas yang lebih tinggi daripada analisis CART dan tidak terdapat perbedaan nilai terlalu besar pada nilai Area Under Curve (AUC). Peubah yang berpengaruh signifikan dalam klasifikasi debitur (lancar, macet) adalah tingkat pendidikan, status kepemilikan rumah, dan pendapatan., The absence of collateral for a type of credit will increase the bank's credit risk (failed to pay). Banks apply the precautionary principle by managing their credit portfolios so that potential hazards that occur can be measured and controlled in a model. Credit scoring describes how likely a debtor will fail with payments. This study aimed to compare logistic regression analysis and Classification and Regression Trees (CART) in classifying debtors to evaluate credit policies. One of the problems in classification is unbalanced data. Synthetic Minority Oversampling Technique (SMOTE) is a technique to handle the unbalanced problem in classification. The results show that the logistic regression model with SMOTE has higher sensitivity than the CART model, and there was no difference in Area Under Curve (AUC). The variables that have significant effects on the classification of debtors (good, bad) are level of education, homeownership status, and income.
Keyword: CART, Credit Scoring, Logistic Regression, Credit Risk, SMOTE

Judul: Distribusi suhu, salinitas dan oksigen terlarut di Laut Arafura Selatan pada bulan November 1992
Abstrak: Tujuan penelitian ini adalah (1) mengetahui variabilitas sebaran suhu, salinitas dan oksigen terlarut secara mendatar dan menegak, (2) Mengidentifikasi massa air di Laut Arafura Selatan. Data yang digunakan dalam penelitian ini adalah data Ekspedisi MINA ZEE-06 1992 pada bulan November 1992. Daerah yang diteliti adalah perairan Laut Arafura bagian selatan yang termasuk Zona Ekonomi Eksklusif Indonesia (ΖΕΕΙ). Suhu dan konduktivitas air laut diukur dengan menggu- nakan CTD Guildline 6000. Nilai suhu ditentukan dengan persamaan salinitas dan konduktivitas. Pengukuran dilakukan di 18 stasiun oseanografi dari permukaan hingga dekat dasar. Sampel air laut untuk menentukan kadar oksigen terlarut diambil dengan botol Niskin pada kedalaman "stan- dar". Kadar oksigen terlarut dianalisa dengan menggunakan metode Winkler. Data menunjukkan bahwa suhu lapisan permukaan berkisar antara 27.72 28.47 °C dengan rata-rata 28.10 °C, dan nilai yang lebih tinggi terdapat di bagian barat. Sebaran suhu secara mendatar memperlihatkan adanya kecenderungan suhu lebih tinggi di daerah barat.
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