Key Factors in Implementing Knowledge Management System based on Project Management (Case Study Pusilkom UI)

Transformasi digital dalam sektor publik (E-government) telah dilakukan dalam beberapa tahun terakhir. Namun, dari banyaknya proyek TIK yang telah berjalan, hanya 15% proyek TIK ini dapat dikatakan sukses. Banyak proyek yang gagal dikarenakan buruknya strategi dan perencanaan, buruknya manajemen SDM, kurang siapnya pemanfaatan TIK yang akan digunakan, serta tergesa-gesanya implementasi TIK tanpa ada perencanaan dan pengujian yang memadai. Proyek TIK ini sendiri sangatlah memakan banyak biaya, sehingga diperlukan suatu penanganan yang baik dalam pengelolaan proyeknya. Salah satu cara untuk dapat menangani proyek ini dengan baik adalah dengan menggunakan sistem pengelolaan proyek yang dapat mengelola pengetahuan (knowledge) dalam pengerjaan proyek tersebut. Menggunakan pendekatan post-positivism dan menganalisis data primer dari responden dengan aplikasi SEM-PLS, peneliti ingin mencari faktor apa saja yang dapat digunakan untuk dapat meningkatkan pemanfaatan pemakaian knowledge management system yang berbasiskan proyek. Hasil dari penelitian ini menunjukkan faktor Kualitas Sistem, Kualitas Konten, Kualitas Konteks dan Hubungannya, serta Keberkesinambungan Sistem; dapat meningkatkan pemanfaatan knowledge management system berbasiskan proyek yang baik. Dari hasil penelitian ini, didapatkan aplikasi Phabricator adalah Knowledge Management System yang berbasiskan proyek yang cocok untuk dapat diterapkan pada organisasi. Kata kunci: E-government, Manajemen Pengetahuan, Manajemen Proyek, Proyek TIK


INTRODUCTION
Digital transformation in the public sector (Egovernment) has begun to occur in recent years, especially after the emergence of Presidential Instruction No.3 of 2003. However, in the course of time the E-government project experienced many problems in the process. According to data from the United Nations (UNDPEPA & ASPA, 2002), this ICT project for the public sector has little success value, which is only 15% of all existing projects, the remaining 85% have total failure and partial failure. From the report, obtained information the main factors that caused this failure came from: 1) Lack of understanding of the government in the public administration system, 2) Lack of strategic plans, 3) HR problems, 4) Minimal ICT investment and budgeting plans, 5) ICT Vendors who are few and do not accept high risk, 6) Immaturity of technology planning, and 7) ICT Implementation that is forced so that preparation and testing is lacking.
Seeing from the problems found, previously, the main problem that is often encountered is regarding poor project management. From this problem, the best solution is to implement good knowledge management in existing project management ). Here, Liu believes that there needs to be a balance in the implementation of project management with the implementation of good knowledge management. In project management the things that must be considered are project planning, organizational management, tools and techniques, and operational management. In knowledge management, what must be considered is the time pressure, the impact of organizational culture, differences between information management and knowledge management, and performance evaluation on knowledge management. Liu also believes the project management will be more effective if it can use the system, namely the Project Management System (PMS), which has been integrated with the elements of Knowledge Management System (KMS). Figure 1 Conceptual Framework PMS based on KM  For information, currently physical resources are no longer the main asset for the organization, knowledge is the main asset (Stacey, 2001). Knowledge management is vital to the success of an organization. Knowledge management using systems or better-known Knowledge Management Systems are systems that facilitate methods, tools and techniques used to manage knowledge more effectively (Green, Liu, & Qi, 2009). By including elements of KMS in PMS, it can make the success rate of a project increase (Alawneh & Aouf, 2016). Seeing this, it is necessary to review what elements of KMS can increase user satisfaction that can increase the value of the benefits of project success.
This study uses the main sample data from Pusilkom UI employees who have used KMS. Pusilkom UI itself is a Fasilkom UI UKK which is engaged in ICT consulting with its main work is to provide ICT-related solutions for the public, private, or LMS sectors. Pusilkom has a total of 50 employees who make it a business unit of the type of MSME. The KMS platform that has been used by Pusilkom until 2016 is JIRA. Investment for the implementation of JIRA is very expensive, for the installation of a new JIRA with a maximum of 50 users requires a fee of $ 2,200 (Atlassian, 2013). Large investments must be balanced with benefits. Therefore, this study was conducted with the aim of getting factors that can increase user satisfaction and benefits in the use of KMS. By obtaining these factors, it is hoped that Pusilkom will be more effective in providing this KMbased PMS implementation to be able to have significant value in increasing user satisfaction and the benefits of using the success of ICT projects.
Research Problems. Which factor can be the best critical factor in using Knowledge Management System based on Project Management?
Research Goals. 1) To identify the key factors that can be use in using Knowledge Management System based on Project Management. 2) To find the best Knowledge Management System based on Project that can comply with key factors that has been found in this research.
Research Benefit. For academic purpose, this research can be used as reference material to find the key factors that can be used in choosing the best Knowledge Man-agement System for Project purpose. For organization, this research can be used as reference material for choosing the best Knowledge Management System for their projects.

LITERATURE REVIEW
Knowledge Mangement System (KMS). Knowledge Management System (KMS) is an integration of technology and a mechanism built to support 4 KM processes, namely discovery, capture, sharing, and application. Based on the supported KM process, KMS can be categorized into four which can be seen in the following This type of KMS supports the process of developing new knowledge both tacit and explicit from data and information or the synthesis of existing knowledge. This system supports 2 KM subprocesses that are related to knowledge discovery which is a combination (allows discovery of new explicit knowledge) and socialization (allows the discovery of new tacit knowledge). Knowledge Capture System This type of KMS supports the process of storing explicit and tacit knowledge that exists in individuals, artifacts, or organizations. This system helps the storage of existing knowledge inside and outside the organization including the knowledge that exists in consultants, competitors, customers, suppliers, and companies where new employees work before. Knowledge Capture System relies on mechanisms and technologies that support sub-processes of externalization and internalization. Knowledge Sharing System This type of KMS supports the process of communicating/distributing explicit and tacit knowledge to other individuals. This system supports 2 KM subprocesses, namely exchange (for example: explicit sharing of knowledge) and socialization (sharing tacit knowledge).

Knowledge Application System
This type of KMS supports the process of knowledge application by enabling an individual to use knowledge possessed by other individuals without actually learning the knowledge. Mechanisms and technology support this process by facilitating routine and direction subprocesses.
JIRA. It is one of the project-based Knowledge Management Systems. JIRA has three main features, such as: bug tracking, issue tracking and project management. The following is a brief feature explanation about JIRA which can be seen in the following

RESEARCH METHOD
This is a quantitative research with the technique of analyzing using Structured Equation Model-Partial Least Square (SEM-PLS). With the Likert (1-5) scale in the survey, this study will use the main sample data from 31 Pusilkom UI employees who have used the JIRA KMS system. This research approach itself is confirmatory research, where the researcher will confirm the main factors in the implementation of KMS that are good from the results of previous research to check the truth in one of the UMKM business units in Indonesia that is engaged in ICT.

Validity Test 1. Convergent Validity Test
Convergent validity test is intended to describe the relationship between instruments that measure the same attributes. Convergent validity is indicated by a single instrument correlation score with other instruments that measure the same attributes, whose value is expected to have a higher correlation score than the instrument's correlation score with other instruments that measure different attributes. The measurement of convergent validity can be done in three ways, namely: Loading factor, Average Variance Extracted (AVE), Communality. Based on these three methods, identification of data that needs to be removed is shown as follows: 1.A. Loading Factor Only a few questions that satisfied the calculation (had a value higher than 0.7), we need to remove those questions for a better calculation result later. (Can be seen from the Table 3 below) 1.B. Average Variance Extracted (AVE) Rejected result in H1, H2, and H4 for this calculation (had a value below than 0.5). Still all of those hypothesizes have a good value and can be used as a sample to calculate correlation.

1.C. Communality
Same as AVE value, H1, H2, and H4 rejected (had a value below than 0.5). But all of them can be used for correlation calculation later.

Discriminant Validity Test
Discriminant validity test is intended to describe the relationship between instruments that measure different attributes. Discriminant validity is indicated by a correlation score between one instrument and another instrument that measures different attributes, whose value is expected to be smaller than the instrument's correlation score with other instruments that measure the same attributes. Measurement of discriminant validity can be done in 2 ways, namely: Cross Loading and Square Root AVE (Correlation between latent constructs AVE shows the total variance construct that can be explained by the measurements made).

2.A. Cross Loading
Using standard value higher than 0.7; all the hypothesizes accepted.

2.B. Reliability
To measure the consistency of the model • Composite Reliability: This estimate takes into account the contribution of each latent factor to each item (loading factor) and each variance error that the item has. This calculation is based on the proportion of variance and can be used in situations where hierarchical structures exist in the data.
• Cronbach's Alpha: measures internal consistency, which is the relationship between components and the total variance studied and the component variance of each sample. Only H1 had a value below than 0.7; and by doing this we can remove all the indicators that had loading factor value, below than 0.7.  Goodness of fit By calculating the square root between the average R2 value and the average communality value, a goodness of fit value of 0.7195 is obtained. From this value it can be seen that the sample data with the model has matched. Q2 predictive relevance By checking the repetition level of a variable that has a R2 value, the Q2 value is obtained which is useful for predictive relevance. From the resulting table, it can be seen that the KP and M variables have a strong predictive relevance level. 4. Hypothesis checking By sticking to the t-statistic value and significant level, the acceptance of the results of the hypothesis can be known whether the hypothesis is acceptable or not. Following are the results of the calculations performed.

Research Conclusion
o The factors that influence the satisfaction of KMS users in Pusilkom UI are the quality of the system, the quality of the content, the quality of context and relationships, and the subsequent use. o The factors that influence the benefits felt by KMS users in Pusilkom UI are user satisfaction, where by fulfilling user satisfaction, indirectly the perceived benefits will be felt more. o Personalization, community and service quality factors are not very influential in increasing user satisfaction which indirectly does not affect the perceived benefits.
Suggestion o System quality: Provides good support regarding data backup and system security. Current conditions, Pusilkom UI does not yet have data backup procedures for JIRA and system security procedures are still not done well, such as in the case of providing passwords for employees, Pusilkom UI still provides default passwords for all employees at first and is not asked to change into a password only when first logged in.
o Content quality: Provides a "promote" feature from expert answers, to provide a ranking on the quality of knowledge provided. o Quality of context and relationships: Required to build a good knowledge repository. Currently Pusilkom UI still uses one repository on the server and has not been linked to JIRA. So that there is a possibility of duplication, out of context, or even difficult to find knowledge information in this repository. o Subsequent use: Integrating with other applications, such as Google. Because Google has integrated with smartphone owned by employees, so that if JIRA provides a deadline for project completion, Google calendar will automatically save the deadline date and employees can find out via smartphone.