Judul: Kajian Metode Pohon Model Logistik (Logistic Model Tree) dengan Penanganan Ketakseimbangan Data
Abstrak: Logistic model tree (pohon model logistik) adalah metode pemodelan nonparametrik yang mengombinasikan metode pohon keputusan dengan regresi logistik linier. Pohon model logistik dapat menangani multikolinearitas dengan baik, tetapi tidak kebal terhadap masalah yang diakibatkan oleh ketakseimbangan data. Penelitian ini dilakukan untuk mengkaji performa metode penanganan ketakseimbangan data undersampling, SMOTE, dan ROSE pada pohon model logistik. Data yang digunakan adalah data simulasi yang diperoleh dengan membangkitkan bilangan acak berdasarkan sebaran Bernoulli sebagai peubah respon dan bilangan normal berganda dua peubah sebagai peubah penjelas. Kombinasi yang digunakan adalah tingkat ketakseimbangan data. Berdasarkan nilai AUC, diperoleh bahwa pohon model logistik yang dibangun dengan menerapkan metode penanganan ketakseimbangan data memiliki performa klasifikasi yang lebih baik dibandingkan dengan pohon model logistik yang dibangun tanpa menerapkan metode penanganan ketakseimbangan data pada seluruh tingkat ketakseimbangan yang dicobakan. Pohon model logistik dengan penerapan metode ROSE memiliki performa lebih baik dibandingkan dengan pohon model logistik dengan penerapan metode undersampling maupun SMOTE pada gugus data dengan tingkat ketakseimbangan data tinggi hingga sedang. Pada gugus data dengan tingkat ketakseimbangan data rendah, performa pohon model logistik dengan penerapan metode ROSE dan metode undersampling tidak berbeda signifikan., Logistic model tree is a nonparametric modelling method that combines decision tree with linear logistic regression. Logistic model tree handles multicollinearity well, but is not immune to problems that arise due to data imbalance. This study was carried to compare the performance of undersampling, SMOTE, and ROSE in handling imbalanced data when used in tandem with logistic model tree. The data used in the simulation was obtained by generating random numbers following the Bernoulli distribution as the response variable and the Bivariate Normal distribution as the explanatory variables, based on five different imbalance levels. Comparisons done on the AUC value showed that logistic model trees built with methods to handle imbalanced data performed better than logistic model trees built without applying any such method on every level of tested data imbalance in classifying objects. Among those, logistic model trees built with ROSE performed best. On datasets with low level of imbalance, the performance of logistic model trees built with ROSE and undersampling do not significantly differ.
Keyword: penanganan ketakseimbangan data, pohon model logistik, ROSE, SMOTE, undersampling

Judul: Perbandingan Performa Metode Pohon Model Logistik dan Random Forest pada Pengklasifikasian Data
Abstrak: Multikolinearitas dan missing data merupakan dua masalah yang sering ditemukan pada data besar. Keberadaan missing data dapat mengurangi akurasi hasil prediksi. Logistic model tree (LMT) dapat menangani multikolinearitas dengan baik karena multikolinearitas bukan suatu masalah dalam metode pohon keputusan. Random forest terbukti dapat menurunkan keragaman dalam kasus prediksi. Penelitian ini bertujuan untuk mengkaji perbandingan metode LMT dan random forest pada data yang mengandung multikolinearitas dan missing data menggunakan data simulasi dan data riil. Tolak ukur evaluasi model adalah nilai ketepatan klasifikasi dan nilai AUC. Hasil yang diperoleh menunjukkan random forest memiliki performa yang lebih baik dalam memodelkan data dengan tingkat multikolinearitas sedang, banyak amatan dan persentase missing data yang besar. Sedangkan untuk data dengan tingkat multikolinearitas tinggi, LMT dengan penghapusan missing data terbukti menghasilkan performa yang lebih baik. Analisis selanjutnya dilakukan pada data dengan dimensi data yang berbeda. Hasil yang diperoleh menunjukkan bahwa random forest menghasilkan performa yang lebih baik. Kemudian untuk data “breast cancer” yang memiliki missing data hanya 0,3% penghapusan missing data menghasilkan performa yang lebih baik., Multicollinearity and missing data are two common problems in big data. Missing data could decrease the prediction accuracy. Logistic model tree (LMT) is used to handle multicollinearity because multicollinearity does not affect the decision tree. Random forest can be used to decrease variance in prediction case. This study aimed to study the comparison of two methods, LMT and random forest, in multicollinearity and missing data in various cases using simulation study and real data as dataset. Evaluation model is based on classification accuracy and AUC measurement. The result stated that random forest had better performance if the multicollinearity level is moderate. LMT with omitted missing data is proven to have better performance for big data and when a high percentage of missing data occurred, and the multicollinearity level is severe. The next step is analysed real data with different sample size. The result stated that random forest have better performance. Omitted missing data have better performance in classification “breast cancer” data which consist 0,3 % missing data.
Keyword: Dimension of data, LMT, missing data, multicollinearity, random forest

Judul: Pemurnian dan karakterisasi protein inhibitor RNA helikase virus hepatitis C
Abstrak: Hepatitis C virus (HCV) infection is the second most common chronic viral infection in the world with global prevalence. This infection is contribution to chronic hepatitis and liver cirrhosis, often leading to hepatocellular carcinoma. There is no effective drug and vaccine available against HCV. The current standard treatment, a combination of pegylated interferon-α with ribavirin, is limited by its suboptimal response rate in a significant patient population, side effects, and affordability. Therefore, development of effective drug is needed. One approach to find out the candidate of antiviral drug is to discover the inhibitor of enzyme that essential for virus replication, such as RNA helicase. Streptomyces chartreusis 5-095 produce the inhibitor of HCV RNA helicase. The aim of this study was to purify the protein inhibitor which has inhibition activity against ATPase activity of RNA helicase HCV from S. chartreusis 5-095. The supernatant of culture S. chartreusis 5-095 exhibited high inhibiton activity against ATPase activity of RNA helicase HCV (approximately 72%) at 10 days of incubation. The extracellular protein inhibitor was partially purified by procedures of precipitation with ammonium sulfate to 65% saturation and gel filtration (Sephadex G50) using methanol water solution (4:6) as an eluen. The purity of protein inhibitor was monitored by SDS PAGE. The protein inhibitor showed a relative molecular mass were estimated to be 14 kDa and increased the purity 23,08 fold than those of the crude protein. The specific activity and IC50 after purification were 13,1338 U/mg and 0,0767 mg/mL, respectively. The activity of purified protein inhibitor showed its stability over pH range from 5-8 and optimum at pH 7,4. The inhibition activity showed its stability up to 70% at temperature 100 oC. The purified protein inhibitor relatively stable its activity at storage in the range of -20 oC to room temperature for 35 days.
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