One strategy of the Ministry of Finance in the institutional transformation is to strengthen the role of Financial Education and Training Agency (FETA) in the development of human resources is to become a corporate university. Furthermore, base on theory that one of the important elements that should exist in an organization to become a corporate university is knowledge management (KM). The problem which occurs in FETA is that the organization does not have specific mechanisms to manage the knowledge. Moreover, to overcome that problem, the organization need to build a KM system that allows each individual to share useful knowledge for the organization. As the first step, it needs a good preparation to lessen the failure in implementing it; which is by measuring the readiness level of knowledge management implementation. Therefore, this study aims to measure the readiness level of knowledge management implementation in FETA in order to provide recommendations for improving the readiness of it. The readiness level is measured based on the variables mapping including KM Infrastructure, KM Enabler, and KM Critical Success Factor and then mapped into the KM aspects. The data were collected by using a sample survey method which were gathered from the employees of FETA. The data are then analyzed descriptively and inferentially to get the description about the readiness level of FETA in implementing knowledge management. Based on this research, it can be concluded that FETA is at the receptive level of readiness in implementing knowledge management. It indicates that all the indicators in KM, have been very supportive to the implementation of KM in FETA.

Previous studies have been conducted in gene expression profiling to identify groups of genes that characterize the colorectal carcinoma disease. Despite the success of previous attempts to identify groups of genes in the progression of the colorectal carcinoma disease, their methods either require subjective interpretation of the number of clusters, or lack stability during different runs of the algorithms. All of which limits the usefulness of these methods. In this study, we propose an enhanced algorithm that provides stability and robustness in identifying differentially expressed genes in an expression profile analysis. Our proposed algorithm uses multiple clustering algorithms under the consensus clustering framework. The results of the experiment show that the robustness of our method provides a consistent structure of clusters, similar to the structure found in the previous study. Furthermore, our algorithm outperforms any single clustering algorithms in terms of the cluster quality score.