Judul: Perbandingan Algoritme C4.5 dan CART pada Data Tidak Seimbang untuk Kasus Prediksi Risiko Kredit Debitur Kartu Kredit
Abstrak: Credit risk is the risk of customers inability regarding their debt payment obligations. Credit risk can lead to non-performing loans, thereby reducing bank earnings. Various techniques can model the revenue of a debtor. One of them is the decision tree. There are several decision tree algorithms that can be used as classifiers, for example: C4.5 and CART. However, the classification results may be inaccurate due to the imbalanced in the used data. One way that can be used to address this problem is to include methods of sampling strategies: for examples over-sampling and under-sampling. This research found that the implementation of sampling strategy on the use of C4.5 and CART can improve the average accuracy, precision, recall, and F-measure.
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Judul: Klasifikasi Naive Bayes pada Data Tidak Seimbang untuk Kasus Prediksi Resiko Kredit Debitur Kartu Kredit.
Abstrak: Banking business in Indonesia is still dominated by the credit business field. Most of the bank's revenue comes from this business field. Unfortunately, credit risk can cause problems in loans which can reduce the bank’s revenue. This research uses a Naive Bayes classification analysis on the imbalanced data for the predictions of debtor’s credit risk that are able to classify the future debtor into the following two categories: good or bad. Sampling strategy is used to overcome the problems of imbalanced data. Duplication oversampling, random oversampling, random undersampling, and cluster undersampling are chosen as the methods. It is found that the random oversampling method shows the best value after sampling strategy is conducted with an f-measure of 83.30%.
Keyword: undersampling., oversampling, naive bayes classification, imbalanced data, Credit risk

Judul: Evaluation of Influenza Antibody Titers in Serum and Egg Yolk of Layers Vaccinated with Influenza Vaccines.
Abstrak: Influenza belongs to the Orthomyxoviridae family which causes upper and lower respiratory tract infection. The aim of this study was to evaluate influenza-A specific antibody titers from serum and egg yolk of hens vaccinated with quadrivalent influenza vaccines. Three Hy-line Brown layers were vaccinated with quadrivalent influenza type A and B inactivated vaccines. Vaccination was carried out three times at a dose of 0.25 ml/chicken via the intramuscular route. The first vaccination was carried out at 24 weeks of age, then boosters were performed at 28 and 32 weeks of age. Serum and egg yolk were collected every week for 20 weeks since the first vaccination. Influenza A H3N2 and H1N1 antibody titers were determined using hemagglutination-inhibition (HI) test and agar-gel-precipitation (AGP) test. Influenza-A H3N2 and H1N1 specific antibodies were detected in both serum and egg yolk from the first to twenty weeks after the initial vaccination. Highest HI geometric mean titer (GMT) of H1N1 antibody reached 26.67 HIU at nine weeks post first vaccination (p.f.v), while H3N2 antibody reached 26 HIU at six weeks p.f.v. Our result showed that influenza antibodies can be produced in layers using quadrivalent vaccines and layer chickens are able to produce equivalent antibody levels for each influenza subtype.
Keyword: antibody, egg yolk, influenza, layer chickens, serum