The digital signature image is a digital pattern with highly variable features. The pattern recognition of digital signature images aims to build a specific characteristic capable of representing a considerable pattern variation while maintaining the boundary conditions of authentication. The feature as an attribute that describes the characteristics of a pattern becomes a determinant factor of reliability of a method of recognizing digital signature image pattern for Handwritten Signature Verification (HSV). To construct HSV required two types of signature samples that are the original signature samples used as training samples and the guess signature samples (consist of valid and imposter signature) which are used as test samples. This study proposes two unique features of 16-Bits Binary Chain to Decimal (16BCD) and Virtual Center of Gravity (VCG). The 16BCD feature obtained from image segmentation with a 4x4 pixel region. All pixels in each region of the segmentation result rearranged into a 16-bit binary chain. The VCG feature is a virtual representation of the Original Signature Pattern (OSP) gravity center against Pattern Space and Background. The verification mechanism uses criteria: the percent of acceptable correlation coefficients for the acceptable feature of 16BCD feature, Mean Absolute Error (MAE) against 16BCD, and the percent deviation of acceptable distance to the VCG feature prototype. Verification test results obtained Acceptance Rate (AR) 80% (which states the percentage of HSV success based on a number of original signature samples) with an efficiency of 90% (which states the percentage of success of HSV in distinguishing valid or forgery signature based on a sample of guessing signatures).

Clustering is one of the primary functions in data mining explorations and statistical data analysis which widely used in various fields. There are two types of the clustering algorithms which try to optimize certain objective function, i.e. the hierarchical and partitional clustering. This study focuses on the achievement of the best cluster results of the hard and soft clustering (K-Mean, FCM, and SOM clustering). The validation index called GOS (Global Optimum Solution) used to evaluate the cluster results. GOS index defined as a ratio of the distance variance within a cluster to the distance variance between clusters. The aim of this study is to produce the best GOS index through the use of the proposed method called the scattered averaging technique based on datasets for the cluster center initialization. The cluster results of each algorithm are also compared to determine the best GOS index between them. By using the annual rainfall data as the dataset, the results of this study showed that the proposed method significantly improved K-Mean clustering ability to achieve the global optimum solution with a performance ratio of 69.05% of the total performance of the three algorithms. The next best clustering algorithm is SOM clustering (24.65%) followed by FCM clustering (6.30%). In addition, the results of this study also showed that the three clustering algorithms achieve their best global optimum solution at the number of even clusters.