Judul: Klasifikasi Nasabah Kartu Kredit Menggunakan Algoritme Fuzzy K-Nearest Neighbor pada Data Tidak Seimbang
Abstrak: Debtor classification is a crucial banking process in order to identify potential problematic credit card applicant. This information is to support decision making in accepting or rejecting the application. The classification method makes use of fuzzy k-nearest neighbor method. Due to the nature of credit card business, most likely, the data collected is imbalanced. In this case, the good debtors always significantly outnumber the bad one. Most of the existing classification systems work well on balanced data, which is not the case in this research. Thus, in order to improve the system, the data composition must be banced using oversampling and undersampling technique. The performance indicator used are accuracy, precision, recall, and f-measure. Replication oversampling improved the algorithm best at the number of nearest neighbors 1. The accuration, precision, recall, and f-measure is 91.93%, 86.12%, 100%, and 92.54%, while the least performance was achieved at the number of nearest neighbors 5. Random oversampling performed better as the number of the nearest neighbors increases. Undersampling performed more stable with the number of the nearest neighbors between 1 and 5.
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Judul: Klasifikasi Debitur Kartu Kredit Menggunakan Algoritme K-Nearest Neighbor untuk Kasus Imbalanced Data
Abstrak: Data is said to suffer the class imbalanced problem when the class distribution are highly imbalance. In this case, minority class is more difficult to predict then the majority class. Though the minority class sometime has important information. In this paper, classification analysis of credit card debtors is conducted by using k-nearest neighbor that can classify debtors into two categories, good or bad. Analysis of a prospective debtor is essential to minimize credit risk. One approach taken to overcome imbalanced data problems is to modify instance distribution using oversampling and undersampling method. The evaluation is conducted by comparing the value of parameter k, accuracy, precision, recall, and F-measure. The evaluation results show that oversampling technique gives the best result of 96.24% with k = 3, 99.23% recall with k = 2, 95.21% precision with k = 1, and 96.30% F-measure with k = 3.
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Judul: Konstruksi dan Ekspresi Gen Penyandi Merozoite Surface Protein-1 Plasmodium yoelii pada Vektor Ekspresi pET32a.
Abstrak: Merozoite surface protein-1 (MSP-1) merupakan protein yang terdapat dalam fase aseksual semua spesies Plasmodium dan berperan dalam invasi eritrosit oleh parasit malaria. Protein ini berpotensi sebagai kandidat vaksin malaria, dan penelitian ini bertujuan mengkonstruksi gen msp1 sintetik Plasmodium yoelii ke dalam vektor ekspresi pET32a serta mengekspresi protein MSP-1 dengan induksi IPTG. Gen msp1 sintetik yang diperbanyak melalui teknik PCR diligasikan ke dalam vektor pET32a dan dipropagasi di dalam inang E. coli TOP10. Transforman positif yang telah diverifikasi dimasukan ke E. coli BL21(DE3). Analisis pola restriksi terhadap plasmid hasil konstruksi menunjukkan adanya dua pita yang masing-masing berada di ukuran ~300 bp untuk gen msp1 dan 5900 bp untuk plasmid pET32a. Hasil sequencing menunjukkan adanya gen msp1, namun urutan basa vektor bersifat asing. Ekspresi protein dengan menggunakan IPTG konsentrasi 0.1 mM, 0.3 mM, dan 0.5 mM menghasilkan protein target, yakni MSP-1 yang terfusi dengan tioredoksin, yang berukuran total sekitar 28 kDa. Uji dot blot dengan antibodi monoklonal MSP-1 juga menunjukkan hasil yang positif.
Keyword: E. coli, ekspresi protein, malaria, MSP-1, pET32a