Rainfall forecasting model using data Global Circular Model (GCM) with Statistical Downscaling technique has a fairly high accuracy. However, missing local climate information poses a constraint in data analysis and forecasting. Missing value imputation is one solution that can be used. Kalman Filter Imputation and State Space Model Arima are imputation methods that operate recursively where there is an update of prediction values when data updates occur. This study aimed to find the best model to use for missing value imputation with small imputation errors. The results of the missing value imputation were used to obtain the best statistical downscaling model on a 3 × 3 to 12 × 12 grid. The research was conducted on the daily rainfall data of Kupang City with 17% missing values and 8% unusual data at the Eltari Meteorological observation station, Kupang city. The average daily rainfall data in East Nusa Tenggara Province were utilized as a reference for the characteristics of rainfall data at the Kupang City observation station. The best missing value imputation was obtained by using the Arima State-Space Model (2,1,1) with a Root Mean Square Error (RMSE) of 0.930 and the model was statistical downscaling best obtained on a grid 6 × 6 with a Mean Absolute Percentage Error (MAPE) of 1.3 % and the number of PCs 11.

Banyuwangi is the largest district in East Java with an area of 5,782.50 km2. It has a long coastline of about 175.8 km which stretches along the southern eastern boundary of Banyuwangi Regency, and there are 10 islands. The BMKG estimates that the dry season in the Banyuwangi area is due to the appearance of the beach having hot weather and rarely rains. Banyuwangi also predicts that the dry season is due to the slight influence of cloud growth. Rainfall is a factor of the rainy season which has a big influence on life such as aviation, plantations and agriculture. Agriculture and plantations in Banyuwangi are mostly located in remote areas. Remote areas are likely to lack weather and climate data information. climate elements of a region cannot be ignored, especially rainfall. Based on data from BMKG (Meteorology, Climatology and Geophysics), the weather data used needs to be classified. Rainfall classification can be categorized into three, namely, light, normal and heavy. There are quite a lot of classification methods, there are several new methods that are quite good such as Naive Bayes (NB). Naive Bayes Classifier (NBC) is an algorithm in data mining techniques that is used to determine the probability of a member of a group. Large and irrelevant datasets can be solved using the Naive Bayes Classifier (NBC) method. The rainfall data used is known first, observed then identified to form a training dataset. Determining the accuracy of rainfall with the Naive Bayes Classifier (NBC) can use several parameters that have a physical relationship between the atmosphere and rainfall. The parameters used to determine rainfall are humidity, rainfall and precipitation. From this study, from 49 data testing, 47 data were predicted correctly with an accuracy of 96%.

Government Standard (GOST) is a 64-bit block cipher algorithm with 32 round, use a 256-bit key. The weakness of this algorithm is the keys so simple, than make cryptanalyst easy to break this algorithm. Least Significant Bit (LSB)  use to insert message into another form without changing the form of the cover after insertion. This research does by hiding encrypted ciphertext to image and hiding image into audio. This research use grayscale and RBG image with BMP and PNG format. Audio using music with wav format. Security analysis using differential analysis NPCR and UACI. Security analysis aims to calculate percentage from cover after hiding the message. The smaller the NPCR and UACI values, the higher the level of security the message is hidden. The results of the analysis of concealment in the image obtained by the average values of NPCR and UACI were 99.98% and 3.46% respectively. While the results of the analysis of hiding in audio obtained the average value of NPCR and UACI were 83.78% and 12.66% respectively.

Prostate cancer has long been a concern of expert’s human genetics in health research. However, an explanation of the main causes of prostate cancer cannot be obtained metabolically-biologic, except the most common one of which is heredity. Explanation of the risk of contracting prostate cancer is sought through genetic explanation of prostate cancer cells and healthy prostate cells from DNA sequencing in the form of micro arrays data or in the form of Gleason values. Cancer cell genetic data is high dimensional where the number of variables observed were far more than the individual observed. It’s make ordinary multivariate classification techniques fail to handle this data because of the singularity matrix. In addition, the observations number of cancer patients are small since they are rarely found. With these two facts, then in this paper we will use a machine learning approach to study the classification, namely SVM. SVM will be compared with the Naive Bayes Classifier and Discriminant Analysis method to determine the accurate division in distinguishing prostate cancer cells from healthy prostate cells. The sample data used consisted of 102 people with 2135 genetic variables which were then divided into training data and testing data. Based on the results of the study, the classification by the SVM method has an accuracy value of 96% with a precision error in the tumor class of 7%. The Naive Bayes classification has a precision error of 23.5% with a classification accuracy of 84%. While the Discriminant Analysis method produces an accuracy of 92% with a precision error of 13.33%.

<p>Cash flow is a form of financial report that is used as a measure of the company success in the investment world. So that companies need to forecast the cash flow to manage their finances. Statistics can be applied for the forecasting of cash flow using the <em>Support Vector Machine </em>(SVM) method on the time series data. The aim of this research is to determine the optimal parameter pair model of the <em>Radial Basic Function</em> kernel and to obtain the forecasting results of cash flow using the SVM method on the time series data. The independent variable is needed the data on cash flow from operating income, expenditure and investment expenditure, sum of all cash flow. While the dependent variable is the financial condition based on the <em>Free Cash Flow</em>. The result of this research is a model with the best parameter pairs of the SVM tuning results with the greatest accuracy that is 75%, 82%, 88%, 64% and the forecasting financial condition of PT Cakrawala for the next 16 months.</p><p><strong>Keywords: </strong>cash flow, forecasting, time series, support vector machine.</p>