Judul: Penerapan Adaboost dan SMOTE untuk Penyelesaian Ketidakseimbangan Kelas pada Kasus Kredit Macet Bank XYZ.
Abstrak: Ketidakseimbangan kelas merupakan suatu kondisi terjadinya ketimpangan proporsi antara satu kelas dengan kelas lainnya. Ketidakseimbangan kelas akan mempengaruhi prediksi pada model. Jika ketimpangan yang terjadi sangatlah ekstrem, prediksi model akan lebih mengarah pada kelas mayor, sedangkan kelas minor akan menghasilkan ketepatan prediksi yang rendah. Data yang digunakan merupakan data penggunaan jasa kredit Bank XYZ dengan proporsi yang tidak seimbang. Hanya sebanyak 4% nasabah yang berada pada kategori kredit macet. Pengklasifikasian dengan metode regresi logistik biner menghasilkan ketepatan prediksi yang rendah pada kelas minor (sensitivitas) yaitu 0.02%. Ketidaktepatan prediksi akan berdampak pada kebijakan Bank sehingga hal tersebut perlu ditangani. Adaboost dan Synthetic Minority Oversampling Technique (SMOTE) digunakan dalam menangani permasalahan buruknya klasifikasi kelas minor pada kelas tidak seimbang. Hasil penelitian menunjukkan bahwa kinerja klasifikasi dengan menggunakan metode SMOTE menghasilkan ketepatan prediksi pada kelas minor yang lebih besar dibandingkan metode Adaboost. Kenaikan nilai sensitivitas yang dihasilkan pada metode SMOTE mencapai 61.73%. Nilai akurasi yang dihasilkan untuk memprediksi amatan secara keseluruhan sebesar 76.45% dan ketepatan prediksi pada kelas mayor (spesifisitas) sebesar 77.05%.
Keyword: Adaboost, ketidakseimbangan kelas, regresi logistik biner, risiko kredit, SMOTE

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: Phylogeography and Species Limits in the Papuan White Snake (Micropechis ikaheka) (Reptilia: Serpentes: Elapidae)
Abstrak: Papua white snake, Micropechis ikaheka, has been distingushed into two subspecies M.i. ikaheka and M.i. fasciatus. However, due to lack of data on their morphology, distribution and ecology, both subspecies often ignored in scintific discussions and refers only byas , Micropechis ikaheka. This study was designed to document the differences between M.i. ikaheka and M.i fasciatus by using scales characteristics and reproductive organ or hemipenes. The study also examine, zoogeography, habitat, diet and cannibalism of both subspecies to better understanding the the ecology of one of the most venomous snakes in the world. This study consists of three topics with method and also different in the size samples, respectively. The first study is the histology and microtopography of the skin were studied using paraffin method and Scanning Electron Microscope (SEM). The second is the hemipenis were examined by following Myers and Cadle (2003), and Zaher and Prudente (2003). The third is the abdominal surgery was carried out to document diet type and the investigate phenomenon of cannibalism in the snake. Specimens for this study came from the collection from Museum Zoologicum Bogoriense (MZB) and Universitas Negeri Papua’s Laboratory (LZU). The research result also indicated that the markers of skin and hemipenis described the same structure and microornamentation in all M. ikaheka groups being compared. Based specimens examined morphological descriptions do not s support the further differentiation in Micropechis. In short, two markers that used in this study could not sufficiently indicate the differences in the level of species and subspecies of M. ikaheka. The study, however, were able to delineate zoogeography and skin color patterns, which fall into four groups: M. i. ikaheka; M. i. fasciatus. M. i. ssp. “intermediate”; and M. i. ssp “black”. This study also revealed that color variations did not overlap in their geographic distribution. It is therefore, it suggested that environmental factors may contribute to the skin color variations. It also assumed that habitat discontinuous and biogeography factors may also cause the loss of the connection between the original populations (vicarians) or follow colonization events (dispersal). The examined of 22 stomach contents of M. ikaheka shows this species is carnivorous, and prey on vertebrates such as amphibians, fish, reptiles, and Mammals. These snakes are opportunistic predators since they prey on all that can be captured, but mainly reptiles (61.5%), followed by mammals and fish (both 15.4%), and amphibians (7.7%). Although categorized as terrestrial animal, the diet study showed that M. ikaheka snakes often go into the water to hunt for fish (the sample from Yapen island), including eel (the sample from Aru island). Based on prey items indentified, there is evidence that M. ikaheka is cannibal. At this time, there still no record on reason for cannibalism, but it is assumed that cannibalism was due to competition between populations and means to control population.
Keyword: geographic variation, white snake, papua