Judul: Klasifikasi Debitur Kartu Kredit Menggunakan Algoritme Support Vector Machine Linear Kernel untuk Kasus Imbalanced Data
Abstrak: In this research, classification analysis of credit card debtors is conducted by using support vector machine linear kernel that can classify debtors into two categories good or bad. The data used in this research is imbalanced because most data are from one class. Classification algorithms generally result in poor performance on imbalanced data because the minority class is more difficult to predict than the majority class. One way that can be used to solve this problem is by using a sampling method with oversampling and undersampling technique. This research compares the value of accuracy, recall, precision, and F-measure. The evaluation result shows a fairly high accuracy values in the original data is 83.59% but, the value of recall, precision, and F-measure are 0%. Random oversampling technique gives the best performance with 54.14% accuracy, 53.47% recall, 61.30% precision, and 54.51% F-measure.
Keyword: undersampling, support vector machine, oversampling, imbalanced data

Judul: Classification of imbalanced data using weighted k-nearest neighbor in data bank credit card debtors
Abstrak: Manajemen risiko kredit bertujuan untuk meminimalkan potensi kerugian dari kredit macet. Analisis data debitur bermasalah yang sudah ada dapat menjadi model dalam kualifikasi pemberian kredit selanjutnya. Data debitur bank termasuk kasus data tidak seimbang. Proses klasifikasi menjadi tidak optimal karena kelas dengan jumlah data lebih banyak memberikan pengaruh yang sangat besar dalam hasil klasifikasi. Penelitian ini bertujuan untuk mengembangkan model klasifikasi data debitur kartu kredit menggunakan algoritme weighted k-nearest neighbor dan metode sampling yang bertujuan meningkatkan kualitas klasifikasi pada data tidak seimbang. Metode sampling yang digunakan yaitu oversampling dan undersampling. Metode oversampling acak menghasilkan nilai f-measure terbaik sebesar 86.51%. Metode oversampling duplikasi menghasilkan nilai recall terbaik sebesar 100%., Credit risk management aims to minimize potential losses of non-performing loans. The classification results of existing data debtors can be referred for credit qualifications. The debtors data, most likely, are imbalanced due to the good debtors dominated the bad one. Classification process could not be optimum because of the class with more data had tremendous influence in the classification result. This research aims to develop a data classification model based on credit card debtors using weighted k-nearest neighbor and sampling method which aimed to improve the quality of classification on the imbalanced data. The sampling methods used are the oversampling and undersampling. The random oversampling method obtains the best performance with F-measure of 86.51%. Moreover, the duplication oversampling can obtain 100% recall. Keywords: imbalanced data, oversampling, undersampling, weighted k-nearest neighbor
Keyword: data tidak seimbang, oversampling, undersampling, weighted k-nearest neigbor

Judul: Methane Consumption (CH4) on Upland Rice Cultivation Under Micro Climate Cobdition With N Fertilizer Levels
Abstrak: Methane (CH4) is one of radiatively active trace gases or greenhouse gas, having strong absorbsivity to the infrared outgoing radiation emitted by the earth's surface, hence causing the warming of the atmosphere. Methane concentration in the atmosphere increases with a rate of about 12+1 ppbv/yr, or 36 Tg/yr. The increase of methane was predicted to have a significant impact on the earth's climate. The presence of 1.3 ppmv of methane in the atmosphere causes the globally average surface temperature 1.30C higher than it would be with no methane.
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