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The recruitment process has always been critical to the success or failure of organizations.
Organizations constantly seek better methods of recruiting staff that will require minimal
effort to seamlessly fit in with the organizations business processes and thus provide
recruitment agencies with the means with which to determine which universities provide the
best graduates in a particular field for recruitment.
This project work utilized a V-model software methodology, in the verification phase
requirements are gathered via observation and document analysis these requirements are then
analysed using use case models data flow diagrams and entity relationship diagrams. In the
Systems Design phase a software specification document is generated and it holds technical
diagrams such as, the interface design of the recruitment management system (R.M.S), its
activity diagram, its architecture diagram and the logic model of the Apriori Algorithm
implemented in the R.M.S. In the implementation phase various web technologies were used
to implement the R.M.S, PHP was used to implement the Apriori Algorithm used to make
assertions as to which higher institutions produce the best graduates in a field of study and
finally in the validation phase unit tests were carried to ensure the R.M.S is fault free.
The R.M.S developed is user friendly with interactive modules that enables organizations
generate various reports and make predictions about schools that produce the best recruits in
a particular field of study. It also enables organizations schedule various exams for various
candidates hence cutting out third party recruitment agencies.
Various organizations run different business processes. It is common knowledge that
organizations spend a lot of resources adapting new recruits to their business processes and
still get mixed results; With R.M.S organizations are guaranteed greater value for invested


Certification II
Acknowledgement IV
Abstract V
List of Tables XIII
List of Figures XIV
1.1 Background of Study 1
1.2 Problem Statement 2
1.3 Aim and Objectives of the Study 4
1.4 Methodology 4
1.5 Scope and Limitation of Study 5
1.6 Justification 5
2 1 P bl 7
2.1 Preamble 7
2.2 Theoretical Background of Recruitment 7
2.3 Information Systems 8
2.3.1 Types of Information Systems 9 Executive Information System (EIS) 10 Management Reporting System 10 Enterprise Resource Planning Systems 11 Business Intelligence Systems 12 Human Resource Management Systems 13 Recruitment Management Systems 14
2.4 What is Knowledge? 14
2.4.1 Measuring a Persons Knowledge 16
2.4.2 Knowledge Discovery in Databases 17
2.4.3 Data Mining 19
2.4.4 Data Mining and Machine Learning 21 Neural Networks (NN) 21 Case-Based Reasoning (CBR) 22 Genetic Algorithms (GA) 22 Decision Trees (DT) 23 Association Rules (AR) 23 Rough Set Theory 24
2.4.5 Data Mining and Statistics 24 Cluster Analysis 25 Correlation Analysis 25 Factor Analysis 25
vii Regression Analysis 26
2.4.6 Data Mining Analysis and Techniques 26
2.5 Data Mining in Human Resource Applications 27
2.5.1 Data Mining and Talent Management/Recruitment 29
2.6 Decision Support 31
2.6.1 So what is decision support? 31
2.6.2 Human vs. Machine Decision Making 32
2.6.3 Decision Support Disciplines 33 Operations Research (OR) 33 Decision Analysis Decision Analysis (DA) 34 Decision Support Systems (DSS) 35 Data Warehousing 35 Group Decision Support 36 Other Decision Support Disciplines 36
2.7 Decision Support Systems (DSS) 37
2.7.1 Components of a Decision Support System 39 Database Management System 39 Model Based Management System (MBMS) 40 Dialogue Generation Management System 41 DSS User 41
2.7.2 Taxonomy of Decision Support System 42
2.7.3 Utilising Decision Support Systems in Recruitment 44
2.8 Distributed Systems 45
2.8.1 Organization of Distributed Systems 47
2.8.2 Architecture of Distributed Systems 47 Client Server Architecture 48 N –tier Architecture 49 Distributed Object Architecture 49 Peer to Peer Architecture 50
2.9.1 Existing Applications 50 Arithon 51 ApplicantStack 51 Ascentis 51 BambooHR 52 HRM Direct 52 iCIMS Recruit 53 Jobvite 53 Recruiterbox 54 The Applicant Manager 54 The Resumator 55
2.9.2 Existing Projects 55 Application of Association Rule Mining To Learning Management Systems 55 Information Retrieval Using Fuzzy Logic 56
3.1 Preamble 58
3.2 Description of the Existing Recruitment Management System 58
3.2.1 Analysis of the existing system 59
3.2.2 Problems of the existing system 59
3.2.3 Benefits of the existing system 59
3.3 Analysis of the Recruitment Management System 59
3.3.1 System Requirements 60 Functional Requirements 60 №n-Functional Requirements 61
3.3.2 Use Case Model for the Recruitment Management System (R.M.S) 61
3.3.3 Data Flow Diagram for the Recruitment Management System 64 Context Data flow Diagram 65 Level-0 Data flow Diagram 65 Level-1 Data flow Diagram 68
3.3.4 E-R Model (ERM) 71 Database Design 72 Database Design Methodologies 72 Physical database design 72 Schema Diagram 76
3.4 System Design for R.M.S 77
3.4.1 Architecture Diagram of R.M.S 77
3.4.2 Function Hierarchical Diagram for the Recruitment Management System 78
3.4.3 Logical Modelling of the Recruitment Management System 79
3.4.4 Activity Diagram 81
3.4.5 Interface Design of the Recruitment Management System 84
4.1 Implementation Model 88
4.1.1 Component Diagram 89
4.1.2 Deployment Diagram 91
4.2 System Requirements 94
4.2.1 Hardware Requirements for the Server 94
4.2.2 Software Requirements 95
4.2.3 Choice of Programming Language Used 96
4.2.4 Implementation Tools 97
4.3 Testing 97
4.3.1 Pilot testing 98
4.3.2 Integration testing 99
4.3.3 Functional testing 101
4.3.4 Unit testing 102 Module Screenshots 104
4.4.1 Result of work VS Existing System 111
4.4.2 Result of work VS Reviewed work 113
4.4.3 Assessment of Result against other Works 114


1.1 Background of Study
Results of various research carried out over the years has shown that one of the greatest
challenges facing organizations in the area of employee performance is their inability to put
together techniques capable of recruiting competent employees and retaining them to achieve
the goals of the organization (Gberevbie, 2008). Most of these organizations contract out the
handling of staff recruitment to recruitment agencies that are subject to bias and favouritism
and this more often than not results in the recruitment of incompetent staff.
According to (Banjoko 2003) employee recruitment is the act of reaching out, looking for and
attracting a large number of people or a huge amount of interested candidates from which the
organization can select those it considers adequate enough or most qualified for the job.
“Studies have shown that the human resource is the most valuable asset in any organization”
(Adebayo et al, 2001). The human factor is one of the most important factors to be considered
in the achievement of the goals of an organization. As a result, the need to put together
techniques capable of recruiting competent employees and retaining them as part of an
organizations workforce cannot be overemphasized.
The nature of the job market has made it almost a requirement for recruitment agencies in
Nigeria to administer psychometric tests popularly called pre-employment tests or aptitude
tests during the recruitment phase. Over 70% of large companies use these tests to measure
how potential employees would differ in their ability to carry out tasks (Leeds University,

2010). Dragnet Solutions which is a leading Recruitment Agency in Nigeria uses a
centralized computer based solution which involves inviting prospective employees to
undergo these tests at specific locations and times in the country whilst ensuring the
applicants never have real time access to their results.
The centralized computer based solution used by most recruitment agencies can be made
more efficient by employing a distributed solution using the internet to network the various
computers involved in the system to facilitate the exchange of information and ensure that
results are generated and can be viewed in real time whilst eliminating the need for applicants
to converge at specific locations to partake in the aptitude test
Over the years the science behind personnel recruitment has become an important area of
research. Multi component systems called recruitment Management Systems have been
designed to facilitate and automate the process of assessing and hiring new employees.
(Recruitment management system, Techniques
such as Data mining and Knowledge Discovery in Databases (K.D.D) have been used over
the years in some systems to find and interpret patterns from available data with the repeated
application of data mining algorithms to help make decisions about problems that rapidly
change or are not specific. (Fayyad et al, 1996). The machine learning approach has also
provided a step in the right direction to infer new knowledge from existing ones and this is
usually expressed in the form of static data sometimes with the option of dynamic data or
1.2 Problem Statement
In order to paint a clear picture of the problem to be solved a simple scenario would be used.
Candidate A is a bright young graduate who just finished from the university with a first class
has completed his compulsory national youth service and is about to enter into the Nigerian
labour market. He has no ”God father” or ”friends in high places” while Candidate B an
average student also just graduated from the university has also completed his youth service
with the difference being that In his case he knows people in all the right places. Both
candidates apply for a position in a company that uses a recruitment agency to employ its
members of staff candidate A performs exceedingly well in the aptitude test while candidate
B performs poorly. Candidate B knowing he has performed informs his elite friends who just
happen to know the C.E.O of the recruitment agency in charge.
The report that is to be sent to the hiring organization is then doctored to favour candidate B
while putting candidate A at a great disadvantage thus hampering the growth of the hiring
organizations. More over the data generated from the use of the system cannot be accessed
easily by external organizations owing to the fact that the system is centralized and pertinent
information that can be accessed by external clients is not located in a central online
This bias and hoarding of information necessitates the need for a more efficient means of
staff recruitment hence the need to decentralize the powers of recruitment agencies with the
use of a distributed decision support system which ensures that the decisions made by the
system are tamper proof and not subject to the biased nature of any individual whilst ensuring
that pertinent information relating to the job applicants are stored in a single repository online
that can be easily accessed by other organizations who can utilise the said information to
make informed decisions.
1.3 Aim and Objectives of the Study
The aim of the project is to provide organizations and educational parastatals with the means
to determine which Higher Institution provide the best graduates in a particular field for
Below are the outlined objectives of the project:
1. To provide a platform for capturing profiles of applicants.
2. To create an online recruitment test based system based on organizational
3. Provide applicants with results upon completion of test.
4. Deduce patterns from applicant’s tests performance to predict likely institutions as to
who produce best candidates for specific disciplines.
1.4 Methodology
The software model to be used in the development of the recruitment management system is
the V-model. It consists of two main phases namely verification and validation.
The first step in the verification phase is requirement analysis here requirements of the
system are collected by analysing users’ needs. Requirement gathering in the course of the
development of this information system would be done via observation, document analysis.
These requirements would then be analysed using models such as the use case model, data
flow diagrams and entity relationship models.
In the System Design step the feasibility of requirement analysis step is determined in this
step a software specification document would be generated and it would hold technical
diagrams such as, the interface design of the recruitment management system, its activity
diagram, its architecture diagram and the logic model of the knowledge discovery technique
implemented in the recruitment management system.
In the Implementation step the front end of the proposed system would be written in H™L,
JavaScript, AJAX, CSS with the help of a web based adaptive framework called Bootstrap
while the backend would be written in PHP. In other for the system to make assertions as to
which higher institutions produce the best graduates in a field of study an association rule
mining technique would be used to predict outcomes based on available data.
The validation phase of the V-model consists of the various tests that would be carried out on
software applications to ensure proper functionality. Unit testing would be carried out during
the implementation step it would be executed to ensure that bugs are eliminated at the code
level. It ensures that the smallest entity can function correctly when isolated from the rest of
the code.
1.5 Scope and Limitation of Study
The scope of the project would be limited to staff recruitment and provision of insights about
candidates and their higher institutions to external organizations as opposed to standard
systems which are multipurpose and are also used for Scholarship exams, Post-utme exams,
Jamb exams etc. The system would also not take into consideration the interview phase
which may or may not be part of the recruitment process and in most cases would be carried
out by the hiring organization.
1.6 Justification
With the rising number of universities and polytechnics in Nigeria, Employers of labour have
a large pool of graduates to offer employment in various fields, people attend tertiary
i tit ti t l t l b t l ith th h th t th k l d i d t th i
institutions not only to learn, but also with the hope that the knowledge gained puts them in
competitive positions for employment (including self-employment). Hence if preemployment
tests have become a meritocratic method for objectively screening from these
large pools of job-seeking graduates, then performance in such tests become significant.


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