Model Penilaian Kredit Menggunakan Analisis Diskriminan dengan Variabel Bebas Campuran Biner dan Kontinu

Credit scoring models is an important tools in the credit granting process. These models measure the credit risk of a prospective client. This study aims to applied a discriminant model with mixed predictor variables (binary and continuous) for credit assesment. Implementation of the model use debitur characteristics data from a bank in Lampung Province which the used binary variables involve sex and marital status. Whereas, the continuous variables that was considered appropriate in the model are age, net income, and length of work. By using the data training, it was known that the misclassification of the model is 0.1970 and the misclassification of the testing data reach to 0.3753

Pendugaan Data Hilang dengan Menggunakan Data Augmentation

Data augmentation is a method for estimating missing data. It is a special case of Gibbs sampling which has two important steps. The first step is imputation or I-step where the missing data is generated based on the conditional distributions for missing data if the observed data are known. The next step is posterior or P-step where the estimation process of parameter values ​​from the complete data is conducted. Imputation and posterior steps on the data augmentation will continue to run until the convergence is reached. The estimate of missing data is obtained through the average of simulated values

Model Prediksi Curah Hujan dengan Pendekatan Regresi Proses Gaussian (Studi Kasus di Kabupaten Grobogan)

Forecasting method of rainfall has developed rapidly, ranging from the deterministic approach to the stochastic one. Deterministic approach is done through an analysis based on physical laws expressed in mathematical form, which identify the relationships between rainfall and temperature, air pressure, humidity and the intensity of solar radiation. Similarly, there are some stochastic models for the prediction of rainfall that have been commonly used, for instances, the model Autoregressive Integrated Moving Average (ARIMA), Fourier analysis and Kalman filter analysis. Some researchers about climate and weather have also developed a predictive model of rainfall based on nonparametric models, especially models based on artificial neural networks. Above models are based on classical statistical approach where the estimation and inference of model parameters only pay attention to the information obtained from the sample and ignore the initial information (prior) of parameter model. In this research, prediction model with Gaussian process regression approach is used for predicting the monthly rainfall. Gaussian process regression uses a stochastic approach by assuming that the amount of rainfall is random. Based on the value of Root Mean Square Error Prediction (RMSEP), the best covariance function that can be used for prediction is Quadratic Exponential ARD (Automatic Relevance Determination) with RMSEP value 123,63. The highest prediction of the monthly rainfall is in January 2014 reached into 336,5 mm and the lowest in August 2014 with 36,94 mm

Distribusi Poisson dan Distribusi Eksponensial dalam Proses Stokastik

In the queueing system, the processes usually come from a Poisson process. In this system should be obtained an arrival distribution and a service distribution. This paper studies about the form of the number of arrival distribution, the number of service distribution, the interarrival distribution and the service time distribution. Futhermore it talks about the relation of them to a Poisson distribution and an exponential distribution

Penerapan Diagram Kontrol Multivariate Exponentially Weighted Moving Average (Mewma) Pada Pengendalian Karakteristik Kualitas Air (Studi Kasus: Instalasi Pengolahan Air III Pdam Tirta Moedal Kota Semarang)

Water treatment is intended to change the original water quality that does not fulfill the health requirements become a water for human consumption and must comply with the levels of certain parameters. Quality control can be done by forming a Multivariate Exponentially Weighted Moving Average (MEWMA) control chart. In the Multivariate Exponentially Weighted Moving Average (MEWMA) control charts with λ = 0.25 and UCL = 13.92658 seen that process controlled statistically. Once the process is under control, it can be done analysis of the ability of the process to determine whether the process fulfill the specifications or not. In the calculation process capability univariate each characteristics and multivariate process capability index values obtained more than 1 means that the process is going well

Analisis Pengendalian Persediaan Produk Oli Menggunakan Metode Economic Order Quantity Probabilistik Dengan Model (Q,r) (Studi Kasus Di Bengkel Maju Jaya Tuban)

Inventory has an important role for the continuity of the trading business. In the trading business, consumer demand for the product is usually random. Consumer demand opportunities are aspects that need to be considered in the process of inventory management. Economic Order Quantity (EOQ) probabilistic model (q,r) is the method used when consumer demand is random and the time between ordering until the product comes (lead time) is not equal to zero. This research aims to apply methods EOQ probabilistic model (q,r) in determining the total cost savings in the inventories of oil products in Maju Jaya Tuban workshop. The oil products analyzed were Top 1 and Yamalube oil products. These results indicate that the method EOQ probabilistic model (q,r) has a total inventory cost less than the policy Maju Jaya Tuban workshop. Total inventory cost savings when the ordering cost (10%) and holding cost (1%) is Rp 4.313,- for Top 1 oil products and Rp 3.086,- for Yamalube oil products

Perbandingan Metode Regresi Logistik Biner Dan Metode Backpropagation Dalam Menentukan Model Terbaik Untuk Klasifikasi Pengguna Program Keluarga Berencana

Indonesia is one of the highest population density in the world has high birth level. One of the regulation to get the population density lower than before that is used by Government is Family Planning Program. On the reality, not all of the productive age join this program. The method is Binary Logistic Regression and Backpropagation. The predictor variables that is researched are husband's age, wife's age, age of the last child, count of children, husband's education, wife's education, husband's job, wife's job and the level of family prosperity. The aim of the research is to compare the classification accuracy between Binary Logistic Regression and Backpropagation. The result of the research by binary logistic regression method, shows the variables that affect the status of KB user is age of the last child and wife's education with the classification accuracy are 66.98%, and the classification accuracy of Backpropagation are 67,30%. The conclution based on the research that is the Backpropagation is better than Binary Logistic Regression when classification the status of KB user in Semarang on March 2013 until Januari 2014