Support Vector Machine (SVM) with Radial Basis Functions (RBF) kernel is one of the methods frequently applied to nonlinear multiclass image classification. To overcome some constraints in the form of a large number of image datasets divided into nonlinear multiclass, there three stages of SVM-RBF classification process carried out i.e. 1) Determining the algorithms of feature extraction and feature value dimensions used, 2) Determining the appropriate kernel and parameter values, and 3) Using correct multiclass method for the training and testing processes. The OaO, OaA, and DAGSVM multi-class methods were tested on a large dataset of batik motif images whose geometric motifs with a variety of patterns and colors in each class and containing similar patterns in the motifs between the classes. DAGSVM has the advantage in classification accuracy value, i.e. 91%, but it takes longer during the training and testing processes.

Classification in recognizing image of letters and numbers is useful to recognize vehicle license plates. This study aims to maximize classification accuracy value of feature extraction method using matrix segmentation. The dataset consists of 300 vehicle license plate images which have 36 classifications, 26 classes for A-Z letters image, and 10 classes for 0-9 numbers image. The research stages carried out to maximize the results of the classification using multiclass SVM-RBF nonlinear are: preparing region image of interest, image enhancement, image segmentation of letters and numbers, determining the best n value for n x n matrix segmentation, calculating total points of each segment as feature value, and determining the best value for C and γ as the value of RBF kernel parameter. The result of this study shows a maximum value of 92% classification accuracy using n = 5, γ = 0.8, and C = 15.

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Beaufort and Vigenere are fast and strong substitution encryption methods against brute force attacks if they have good key quality. But both of these methods by default can not produce a good avalanche effect. Avalanche effect is a measuring tool that can determine the strength of encryption from differential attacks. This research proposes hybrid techniques of Beaufort and Vigenere algorithms to optimize the security of text message encryption by modifying key generators. With this method, the encryption process can be done by input text messages and keys. The key is processed with a key generator to produce two new keys that are used for encryption using the Beaufort and Vigenere hybrid methods. Based on the test results, the Avalanche effect value rose significantly compared to the standard method, and the value of the avalanche effect was more stable and close to ideal compared to the previous method. The time required is also relatively very fast for the encryption or decryption process, experimental result shows it takes less than 0.1 seconds for more than 43 thousand of characters in the text message.