There is no doubt that knowledge management (KM)
has came to play an important role in enterprises. KM refers
to the set of processes or practice of developing the ability
to create, acquire, capture, store, maintain and disseminate
the enterprise’s knowledge. Even though managers knew
how important KM, it was very difficult to implement it
successfully[1]. One of the main important things that will
be faced by manager in enterprises before implementing the
knowledge management system is evaluation an appropriate
knowledge management tools (KMT). An appropriate
choice of KMT is significant expected in establishing KMS
to facilitate KM activities[2] besides the other factors such
as human aspect, and organizational aspect. Therefore, it is
necessary to selecting the right tool that suitable with the
enterprise circumstances to support KMS implementation.
The prior researches of KMT evaluation have created
versatile methods which can effectively deal with the KMT
evaluation problem. Ngai [3] applied an analytic hierarchy
process (AHP), Erensal [4] used fuzzy linear programming,
Yu-Rong [5] integrated modified Delphi, fuzzy
comprehensive evaluation and grey relational analysis.
Those models can handle both qualitative and quantitative
multi-criteria problems. However, the problem is not as
simple as it seems. This is due to the fact that uncertainty
character always present in decision making and enterprises
have different business circumstances each one; it means
that enterprise always different in IT purchasing policy.
Furthermore, the knowledge management tools evaluation is
time consuming task and filled with the multiple conflicting
criteria that must be interrelation among each factor. Hence,
the purpose of this paper is to improve conceptual model of
KMT evaluation by applying fuzzy inference system.
Over the years, Artificial Intelligence (AI) techniques
such as Artificial Neural Network (ANN), Genetic
Algorithm (GA), and Fuzzy Logic (FL) have been studied
and employed in decision making. FL has been widely used
because of its obvious advantages of effectively dealing
with uncertainty and capturing experts’ knowledge on a
specific problem and using this knowledge to make
decisions. In this paper, FL has been applied to deal with the
knowledge management tools evaluation problem. The
evaluation decision can be effectively made based on the
criteria and knowledge base which have been constructed by
experts of a specific domain. Additionally, the knowledge
management tools evaluation criteria and rules used in
making decision can be adapted to the changing
environment of enterprise. The rest of this paper is
organized as follows; we begin with the literature KM
system and tools, and KMT evaluation criteria in section 2.
Section 3 discusses about FIS, section 4 presents the details
of the proposed FIS for the KMT evaluation and to
complete the FIS work, we present the simulation through
section 5. Finally, we discuss the conclusion in section 6.