Post on 22-Jan-2021
E-TENDER
MUHAMMAD ZHARIF BIN ZAKARIA
BACHELOR OF COMPUTER SCIENCES (SOFTWARE DEVELOPMENT)
WITH HONOURS
FACULTY OF INFORMATICS AND COMPUTING UNIVERSITI SULTAN
ZAINAL ABIDIN
DECLARATION
I declared that titled E-TENDER is based on the result of my investigations from the
gathered information previously. All the sections text and results which have been
obtained from several sources are fully referenced.
Signature :
Name : Muhammad Zharif Bin Zakaria
Date : 21 May 2018
SUPERVISOR ENDORSEMENT
This project report with title Expenditure Management System (EMS) was prepared and
submitted by Muhammad Zharif Bin Zakaria, matric number BTAL15041754 and has been found
satisfactory in terms of scope, quality and presentation as partial fulfillment of the requirement
of Bachelor of Computer Science (Software Development) with Honours in Universiti Sultan
Zainal Abidin (UniSZA).
Name : Madam Norlina Udin
Date : 21 May 2018
ABSTRACT
'E- Tender Assessment System' is a system developed for the Public Works Department and the
tender organization operates more systematically and regularly. This system works in the
handling and evaluation of tender forms from users or companies that have submitted tender
documents. Organizational staff will manage or handle all the data required for a tender
application for a construction project. The purpose of this system was developed to develop an
electronic based construction evaluation Tender form. Application of tender document
evaluation process will be carried out through a computerized system and user information will
be stored in the database. Additionally, the system is also accessible anywhere and anytime
through network or internet connection.
Through the use of this system, there are two types of users who are identified to access
the system ie the company who signed up to view all the projects, buy and fill the construction
tender form. Administrators will also register for system usage. The use of this developed
system includes the use of login for all users, registration for users and staff, updating user and
staff information for their personal information, filling out a tender form for users who have
purchased tender documents, staff will assess the filling of information or data whether or not
they conform information on tender documents, managing tender document data for
administrators viewing and reviewing all data on tender and company projects, finding
information for all users that allow users to search important data using keywords or IDs and
generate reports to help staff to generate reports standard and documentation
is to ensure the flow of system effectiveness in handling the process of evaluation of the tender
document for construction tender.
ABSTRAK
'E- Sistem Penilaian Tender ' merupakan suatu sistem yang dibangunkan untuk Jabatan Kerja
Raya dan organisasi tender beroperasi dengan lebih sistematik dan teratur. Sistem ini berfungsi
dalam urusan pengendalian dan penilaian borang tender daripada pihak pengguna atau syarikat
yang telah menghantar dokumen tender. Kakitangan organisasi akan menguruskan atau
mengendalikan semua data yang diperlukan untuk permohonan tender sesuatu projek
pembinaan. Tujuan sistem ini dibangunkan adalah untuk membangunkan sistem penilaian
borang Tender pembinaan berasaskan elektronik. Aplikasi proses penilaian dokumen tender
akan dilakukan melalui sistem berkomputer dan maklumat pengguna akan disimpan dalam
pangkalan data. Selain itu, sistem ini juga boleh diakses di mana-mana dan pada bila-bila masa
sahaja melalui jaringan atau sambungan internet.
Melalui penggunaan sistem ini, terdapat dua jenis pengguna yang dikenalpasti untuk
mengakses sistem iaitu syarikat yang mendaftar untuk melihat semua projek, membeli dan
mengisi borang tender pembinaan. Manakala pentadbir pula akan mendaftar untuk
penggunaan sistem. Penggunaan sistem yang dibangunkan ini meliputi penggunaan login untuk
semua pengguna, pendaftaran untuk pengguna dan kakitangan, mengemaskini maklumat
pengguna dan kakitangan untuk maklumat peribadi mereka, mengisi borang tender untuk
pengguna yang telah membeli dokumen tender, kakitangan akan menilai pengisian maklumat
atau data samaada menepati atau tidak maklumat berkaitan dokumen-dokumen tender,
menguruskan data dokumen tender untuk pentadbir melihat dan menyemak semua data
mengenai projek tender dan syarikat, mencari maklumat untuk semua pengguna yang
membolehkan pengguna untuk mencari data penting menggunakan kata kunci atau ID dan
menjana laporan untuk membantu kakitangan untuk tmenghasilkan laporan standard dan
dokumentasi
adalah untuk memastikan aliran keberkesanan sistem dalam mengendalikan pemerosesan
penilaian borang dokumen tender pembinaan.
CHAPTER I : INTRODUCTION
1.1 Introduction 1
1.2 Background 1
1.3 Introduction on the background research 2
1.4 Problem statement 3
1.5 Objectives 4
1.6 Scopes 5
1.7 Project Structure 5
CHAPTER II : LETERATURE REVIEW
2.1 Introduction 7
2.2 Decision Support System 8
2.3 Tender Evaluation 8
2.4 AHP 10
2.4.1 Forming a hierarchy 12
2.4.2 Pair-wise Comparison 13
2.4.3 Solving Eigenvector 15
2.5 Summary 17
CHAPTER III: METHODOLOGY
3.1 Introduction 18
3.2 AHP Calculation 18
3.2.1 Criteria 19
3.2.2 Alternatives 20
3.2.2.1 Financial Capability 20
3.2.2.2 Current Contract 23
3.2.2.3 Price 25
3.2.2.4 Past Performance 28
3.2.2.5 Technical Capability 30
3.2.3 Matrix Criteria vs Alternatives 32
3.3 Framework 34
3.4 Design 35
3.4.1 context Diagram 35
3.4.2 DFD 36
3.4.3 ERD 41
3.5 Summary 41
CHAPTER IV: IMPLEMENTATION
4.1 Introduction 42
4.2 Admin 43
4.3 Contractor/Company 52
4.4 Summary 59
REFERENCES
APPENDIX A
CHAPTER I
PROJECT INTRODUCTION
1.1 Introduction
`This chapter will discuss about background, problem statement, objective, and scope for the
Jabatan Kerja Raya Tender Suggestion System. Generally, this chapter focus more on the
problem occurring in tender evaluation. Thus, in this chapter, a new idea is proposed as a first
step to solve the problem.
1.2 Background
Tendering is a process of making offer or can also be referred as proposals or bid. The
process is when the interested contractors are invited to make bids to carry out specific packages
of construction work. The agency having a hard time in choosing the most suitable or deserve
company in giving the offer contract since all the interested contractor have high expectations in
having the tender.
Moreover, each and every one of the company have their own qualities that required by the
agency. Furthermore nowadays, there are always biased in giving the offer tender even though
there still companies that even more deserve to get the offer tender rather than the chosen one.
Thus, this system is designed to ease the work of the agency by using the implementation of
Analytic Hierarchy Process (AHP) method in choosing the most suitable and deserve company
to give the offer tender clear without any bias. This decision support is used based on the details,
history and performance of the company`s previous contact. This system will serve the agency
and also to the contractor in giving a better and smooth work to be done.
1.3 Introduction on the background research
Jabatan Kerja Raya Tender Suggestion System using AHP DSS method will be able to help
the agency which is JKR to make decision in choosing the suitable contractor`s company for the
offered tender by analyzing by calculating points based on the criteria of the company and also
the filled tender form. This tender suggestion system will help the agency to make decision on
giving the award tender to which company according to their rank. This system meant to solve
problems traditional tendering method or manual tendering method such as difficulty to evaluate
the contractor`s company, applying biased in tendering.
In this research, the contractor`s company will have to buy the offered tender form from the
agency which is JKR and complete all the term and condition such as attending the project
briefing and site visit. Then, the company need to register or log in into the JKR tendering
System to manage profile based on current company`s achievements. The system will make
analysis based on the company`s tender form and the company`s tender form and the company`s
detail and will calculated using the AHP computational calculations. The system will produce the
three most suitable company to have the offered tender as suggestion to the agency. In
conclusion, the Jabatan Kerja Raya Tender(JKR) Suggestion System will be a really big help for
the Jabatan Kerja Raya Tender(JKR) to solve the tendering problems.
1.4 Problem statement
The manual practice of tendering system gives holes for agency and also the contractor`s
company. Three of them are:
a) The agency have a hard time in choosing the most suitable and deserve company to
award the offer tender
There always have a tough decision to make in choosing the most suitable
company to award the tender since there are so many companies with a very
tendency to be chosen.
b) The accuracy of decision made by the decision maker is highly dependent on the
expertise.
In manual tender evaluation, the selection requires the involvement many
expertise from various field who have in-depth knowledge in a specific domain to
analyze the document tenders to take into account the objectives and criteria of
the tender before decision making process is executed.
c) There is always a biased in giving the offer tender
Tendering bodies in organizations that go through tenders manually embark on
decisions using their own discretion and thus yielding improper decisions due to
biasness involved in the selection. The bias selection is often being practice in real
life for so many reasons such as political influences or also the family issues.
1.5 Objectives
There are three main objectives. These objectives are derived from the problems stated
before:
To study and analyze the AHP technique in DSS method and the criteria needed in
evaluating tender
a) To model and design framework of tender suggestion system
b) To develop a tender suggestion system that can ease agency in finding the
suitable contractor to award the offer tender without any biased.
1.6 Scopes
a) Administrator
Administrator in this system is given to the staff of JKR in ‘ Jabatan Bendahari
JKR’. The admin can control the system which he/she may update the list of
available tenders in the system. He/she also responsible to approve the request
registration companies into the system. Besides that, the result of the companies’
priorities generated by AHP will be sent to this admin to be evaluated and decide
in order to award the tender to suitable company.
b) Contractor’s company
These companies are the user of this system. The company only approved after
the tender from of that company is received at ‘Jabatan Bendahari’. The
contractor of that company need to key in all the details of the company into the
system. These companies also can be alerted on the submission date of tender
form.
1.7 Project Structure
Chapter one of this thesis concentrate on background study and problem that being faced
when evaluating tender in real life manually. Each possibilities that may become limitation to the
current system is discussed.
Chapter two discuss about literature review collected from other thesis as guideline in
developing this project. The research in this chapter was collected from reviewing research
document, articles and websites. A comparison between the research and this project is made for
a better result and solution.
Chapter three discussing about methodology. AHP technique is chosen to be implemented
into this JKR Tender Suggestion System. The manual calculation of tender using AHP technique
is also included in this chapter.
Chapter for is all about implementation made into this system. This chapter discussing on
user manual of this system.
Chapter five is testing. Every function in the system is tasted to prove the functionality. Each
process have its own test cases.
Lastly, conclusion in chapter six wrap the whole system process from research,
development, implementation, testing and documentation.
CHAPTER II
LETERATURE REVIEW
2.1 Introduction
This chapter discuss on the research made by other people that have similar development with
the Jabatan Kerja Raya (JKR) Tender Suggestion System. This research paper is reviewed as the
guidelines and references in developing this tendering system. Each research will be reviewed
and analyze in objectives, scopes, and technique used.
A literature review surveys scholarly articles, book, dissertations, conference
proceedings and other resources which are relevant to a particular issue, area of research or
theory and provides context for a dissertation by identifying past research. In this chapter, there
contain about how important sensitive data need to be protect, existing tendering system with
their advantage and disadvantage, method for used in real life and techniques used, especially for
data masking and filtering techniques. Sensitive data encompasses a wide range of information
and can include information that relates to human as a consumer, client or employee.
2.2 Decision Support System
Decision Support System is also known as knowledge-based system which refers to
their attempt to formalize domain knowledge so that it is amenable to mechanized reasoning. An
extremely broad concept to explore and the definition of Decision Support System are varies
depending on the point of view of the authors. To avoid exclusion of any of the existing types,
Decision Support System is roughly defined as an interactive computer-based system that aid
users in judgment and choice activities (Marek J. Druzdzel,2002).
2.3 Tender Evaluation
Ros Haslinda Alias and Noor Maizura Mohamad Noor stated the ultimate goal is to
increase the transparency and integrity in the construction industry in their research titled
“Agent-Based JKR Tender Suggestion System in Tendering Process” which provides the
proposal in identifying and applied the use of Intelligent Multi Agents (IMA) in Open Tender
Evaluation System according to standard provided.
The research intends to develop intelligent multi agent in tendering process that will
assist decision maker to evaluate contractors involved by choosing the most qualified tenderer
for carrying the project (Ros Haslinda Alias & Noor Maizura Mohamad Noor,2002). The
methodology uses in this system still need more and more contribution and modification to
utilize the agent-based practice.
Eranjan and Rehan proposed AHP integrated with weighted score model in order to
achieve their objective in the research proposal which is to increase the efficiency rate accuracy
of the final tender decision. The proposed application for tender evaluation would be in a web
based environment to increased advantages that it would offer to both suppliers and organization
from the own convenience.
The framework proposed consist of three parts:
• Model Base: consist of the integrated AHP which is used to calculate weights with
linear weighting model which is used to calculate scores for each tender
• User Interface: The repository use to store various information and data related to
the tender process
The process starts with the weights identification, where DM is given the option to
identify the criteria which is used to evaluate a particular tender. This step is immediately
followed by the comparison of the identified criteria. AHP is used for this process where it
would allow the user to compare criteria with each other. Depending on the comparison a rank or
rather weights are being identified (Eranjan&Rehan,2009).
The web based JKR Tender Suggestion System proposed for the tendering process
provided information that would suggest that it would be able to improve the efficiency of the
process as well as reduction in costs.
iWDSS-Tender is proposed by Noor Maizura Mohamad Noor and Mustafa Man to
implement a precise and transparent tender evaluation for DMs which may reduce the mistakes
and increase the efficiency of the decisions. This iWDSS-Tender is expected to produce a
complete electronic tendering systems which will helps users to do tender evaluation in a fast,
efficient and accurate way. The tender evaluation proposed in the system has two phases. In the
first evaluation phase, all quantified tenders will be going through basic completeness document,
financial, and capital analysis. Tender documents that fail to fulfill all this basic analysis will
then be discarded for second evaluation phase and system will send them a failure notification.
The successful tenders will then be listed for the next evaluation phase (Noor Maizura &
Mustafa Man,2010).
Then in the second phase of evaluation, tenders will be reexamined for calculating the
criteria. Each criteria will be given points for each DMs scales. After that, the total points of each
successful tenders will be compared to the Minimum Evaluation Marks (MEM). Those tenders
that failed to reach the MEM are considered failed while those that are successful are listed down
based on the number of alternatives that the DMs want. The intelligent model of MCDM
analysis is implemented in this phase to help the DMs decide which tender to be selected based
on the list.
2.4 AHP
Analysis Hierarchy Process (AHP) is one of the Multi Criteria decision making method
tat introduced by Prof. Thomas L. Saaty in 1980. This AHP method is an effective tool for
dealing with complex decision making which aid the decision maker to set priorities and make
the best precise decision. AHP can evaluate decision alternatives by pair-wise comparison,
leading to more accurate judgments that the simple weighted product model (Saaty cited Ishizaka
& Lusti,2002).
AHP is a simple method because there is no need of building a complex expert system
with the decision maker`s knowledge. Furthermore, the AHP is very flexible and powerful tool
because the score, and therefore the final ranking are obtained on the basis of the pairwise
relative evaluations of bot the criteria and the opinions provided by the users. AHP is consider as
a tool that be able to translate the evaluation both quantitative and qualitative made by the
decision make into a multicriteria ranking because the computations made by AHP I always
guide by decision maker.
constructing hierachies
• indentification of hierarchy of crieterions needed for the evaluation
comparative judgment
•comparing the crieterions with each other by using the pair-wise comparison
syhthesis of priorities
•depending the result of the pair-wise comparition, identify the priorities for the criterion
2.4.1 Forming a hierarchy
The AHP consider a set of evaluation of criteria and a set of alternative options among
which the best decision is to be made. Thus, the very first step out of all is the problem are
decomposed into a hierarchy of criteria and alternatives. An important part of these processes is
to accomplish there steps:
Problem
Criterion1 Criterion2 Criterion3
Criterion1.1
Alternative3 Alternative2 Alternative1
Figure 2.3: Steps in AHP
The information is then synthesized to determine relative rankings of alternatives. Both
qualitative and quantitative criteria can be compared using informed judgments to derive weights
priorities (Rainer Haas & Oliver Meixner).
2.4.2 Pair-wise comparison
According to the Dr. Rainer has and Dr. Oliver Meixner in the journal “An Illustrated
Guide to the Analytical Hierarchy Process”, the relative importance of one criterion over another
can be expressed by using pair wise comparison. In this step, we are using judgments to
determine the ranking of the criteria and make comparisons of the criteria in pairs. Each of these
judgments is assigned a number on a scale. The AHP amploys a scale with values from 1 to 9 to
designate the relative preference of one element over another.
State the objective
Define the criteria
Pick the alternatives
Table 2.1 shows using a pair wise comparison, the relative importance of one criterion
over another can be expressed. Dr. Rainer Haas and Dr. Oliver Meixner stated in their article that
Dr. Thomas L. Saaty, with the University of Pittsburgh, demonstrated mathematically that the
eigenvector solution was best approach. Eigenvector can help in getting ranking of priorities
from a pair wise matrix.
Table 2.1: Fair wise comparison
Criterion 1, 2, …n Alternative 1 Alternative 2 … Alternative n
Alternative 1 A1/A1 A1/A2 A1/An
Alternative 2 A2/A1 A2/A2 A2/An
…
Alternative n An/A1 An/A2 An/An
Table 2.2 show the scale of relative importance according to Thomas L. Saaty.
Intensity of
Importance
Definition
Explanation
1 Equal importance Two activities contribute equally
to the objective
3 Weak importance of one another Experience and judgment slightly
favor one activity over another
5 Essential or strong importance Experience and judgment strongly
favor one another
7 Demonstrated importance An activity is strongly favored and
its dominance demonstrated in
practice
9 Absolute importance The evidence favoring one activity
over another over another is of
highest possible order of
affirmation
2,4,6,8 Intermediate value between the two
adjacent judgments
When compromise is needed
Reciprocals of above
nonzero
If activity I has one of the above
nonzero numbers assigned to it
when compared with activity j, then
j has the reciprocal value when
compared with i.
2.4.3 Solving Eigenvector
In order to get a ranking of priorities from a pair-wise matrix, constructing the
eigenvector is the best solution. A short computational way to obtain this ranking is to raise the
pair-wise matrix to powers that are successively squared each time. Then, the row sums are
being calculated and normalized. When the difference between these sums in two consecutive
calculations is smaller than a prescribed value, the computer is instructed to stop.
Table 2.3 shows using get ranking priorities for criterion by using criteria versus criteria.
Criteria 1 Criteria 2 Criteria n
Criteria 1 C1/C1 C1/C2 C1/Cn
Criteria 2 C2/C1 C2/C2 C2/Cn
Criteria n Cn/C1 Cn/C2 Cn/Cn
The fractions then converted to decimals.
Table 2.4 shows Compute eigenvector by adding all value by row.
Criteria 1 Criteria 2 Criteria n Sum
Criteria 1 A b z a+b+…+z
Criteria 2 A b z a+b+…+z
…
Criteria n A b z a+b+…+z
The matrix being squared then the first eigenvector is computed to 4 decimal places after rows
are summed.
Table 2.5 explained to sum off the row totals from each criteria.
Criteria 1 Criteria 2 Criteria n Sum
Criteria 1 A b z a+b+…+z
Criteria 2 A b z a+b+…+z
…
Criteria n A b z a+b+…+z
a+b+…+z
+
a+b+…+z
+
…..
a+b+…+z
=
Total
Finally, we normalize by dividing the row sum by the row totals.
Table 2.6 shows how the value is normalized by dividing the row sum with the row total to get
eigenvector.
Criteria n Sum Eigenvector
Criteria 1 z a+b+…+z (a+b+…+z)/(Total)=T1
Criteria 2 z a+b+…+z (a+b+…+z)/(Total)=T2
Criteria n z a+b+…+z (a+b+…+z)/(Total)=Tn
=Total
Table 2.7 shows the computed eigenvector give the relatives rank. The highest eigenvector value
will be ranked first and so on.
Eigenvector
Criteria 1
T1
The 2 most important criterion
Criteria 2
T2
The least most important criterion
Criteria n
T n
The most important criterion
This process must be iterated until the eigenvector solution does not change from the
previous iteration. When there is not much difference, the computed eigenvectors gives us the
relative ranking of our criteria.
2.5 Summary
This chapter is all about the that have been conducted before the development of the
system. Every similar information collected from research is collected as guidelines in
developing the system.
CHAPTER III
METHODOLOGY
3.1 Introduction
Chapter 3 discuss about the technique used in this project and research. E-Tender is
implemented with Decision Support System, Analytical Hierarchy Process(AHP). The AHP
method is implemented as a decision making. The result get from calculation is take as a
suggestion to help the official staff in making decision in tender evaluation.
3.2 AHP Calculation
Diagram 3.1 shows the first before start calculating AHP. That are importance art of the process
which are:
State the objective: To select contractor
Define criteria: Financial Capability, current contract, price, past performance on
contract, technical capability.
Pick the alternatives: contractor A, contractor B, contractor C, contractor D, contractor E.
The information is then synthesized to determine relative rankings of alternatives. The criteria
are compare using informed judgments to drive weights and priority.
3.1.2 Criteria
The relative importance of one criterion over another can be expressed using pairwise
comparison in table 3.2(a)
FP CC P PP TC
FP 3/3 3/1 3/7 3/5 ¾
CC 1/3 1/1 1/7 1/5 ¼
P 7/3 7/1 7/7 7/5 7/4
PP 5/3 5/1 5/7 5/5 5/4
TC 4/3 4/1 4/7 4/5 5/4
Table 3.1(a): Pair-wise comparison for criteria
Table 3.1(b): shows that the fraction is converted to decimals
FP CC P PP TC
FP 1.0000 3.0000 0.4286 0.6000 0.7500
CC 0.3333 1.0000 0.1429 0.2000 0.2500
P 2.3333 7.0000 1.0000 1.4000 1.7500
PP 1.6667 5.0000 0.7143 1.0000 1.2500
TC 1.3333 4.0000 0.5714 0.8000 1.0000
Table 3.1(b): Pair-wise comparison in decimal for criteria
Table 3.1(c) is the starting of eigenvector solving. A short computational way to obtain this ranking is to
raise the pairwise matrix to powers that are successively squared each time.
FP CC P PP TC
FP 5.0000 15.0002 2.1430 3.0000 3.7501
CC 1.6667 5.0002 0.7144 1.0000 1.2501
P 11.6664 35.0000 5.0003 7.0000 8.7500
PP 7.3332 25.0000 3.5717 5.0000 6.2501
TC 6.6664 19.9997 2.8573 4.0000 5.0000
Table 3.1(c): SSquaring the matrix for criteria
5.0000 + 15.0002 + 2.1430 + 3.0000 + 3.7501 29.8933
1.6667 + 5.0002 + 0.7144 + 1.0000 + 1.2501 9.6314
11.6664 + 35.0000 + 5.0003 + 7.0000 + 8.7500 67.4167
7.3332 + 25.0000 + 3.5717 + 5.0000 + 6.2501 4701550
6.6664 + 19.9997 + 2.8573 + 4.0000 + 5.0000 38.5234
Total = 191.6198
Figure 3.2(b): Normalize the eigenvector for criteria
3.2.2 Alternatives
3.2.2.1 Financial Capability
Table 3.2(a) show the pairwise comparisons determine the preference of each alternative over
another in terms of financial capability.
NORMALIZE= 0.1508
0.0503 0.3518 0.2461 0.2010
Figure 3.2(a): pair-wise comparison for alternative (Financial Capability)
CA CB CC CD CE
CA 1/1 4/5 4/4 4/7 4/3
CB 5/4 1/1 5/4 5/7 5/3
CC 4/4 4/5 1/1 4/7 4/3
CD 7/4 7/5 7/4 1/1 7/3
CE ¾ 3/5 3/4 3/7 1/1
Table 3.2(b) show that the fractions is converted to decimals.
Table 3.2(b) pair-wise comparison in decimals for alternative (Financial Capability)
Table 3.2(c) is the starting of eigenvector solving for financial capability. A short computational
way to obtain this ranking is to raise the pairwise matrix to powers that are successively
squared each time.
Table 2.3(c): Squaring the matrix for alternative (Financial Capability)
CA CB CC CD CE
CA 5.0000 4.0000 5.0000 2.8571 6.6665
CB 6.2501 5.0000 6.2501 3.5714 8.3333
CC 5.0000 4.0000 3.2857 2.8571 6.6665
CD 8.7500 7.0000 8.7500 5.0000 11.6665
CE 3.0001 3.0000 3.7501 2.1429 5.0000
CA CB CC CD CE
CA 1.0000 0.8000 1.0000 0.5714 1.3333
CB 1.2500 1.0000 1.2500 0.714 1.6667
CC 1.0000 0.8000 1.0000 0.5714 1.3333
CD 1.7500 1.4000 1.7500 1.0000 2.3333
CE 0.7500 0.6000 0.7500 0.4286 1.0000
Figure 3.3(a): compute eigenvector for alternative (Financial Capability)
Figure 3.3(a) shows that computing the eigenvector determines the relative ranking of
alternatives under financial capability criteria
Figure 3.3(b): Normalize the eigenvector for alternative (Financial Capability)
The row sums of alternative for financial capability are normalized in Figure 3.3(b).
5.0000 + 4.0000 + 5.0000 + 2.8571 + 6.6665 23.5236
6.2501 + 5.0000 + 6.2501 + 3.5714 + 8.333 29.4049 =
5.0000 + 4.0000 + 3.2857 + 2.8571 + 6.6665 21.8093
8.7500 + 7.0000 + 8.7500 + 5.0000 + 11.6665 41.1665
3.0001 + 3.0000 + 3.7501 + 2.1429 + 5.0000 16.8931
TOTAL = 132.7974
Normalize = 0.1771 0.2214 0.1642 0.3100 0.1272
3.2.2.2 Current contract
Table 3.3(a) shows the pairwise comparisons determines the preference of each alternative
over another in terms of current contract.
Table 3.3(a): Pair-wise comparison for alternative (Current Contract)
Table 3.3(b) shows that the fractions is converted to decimals.
Table .3(b): Pair-wise comparison in decimals for alternative (Current Contract)
CA CB CC CD CE
CA 1.0000 1.4000 1.7500 1.667 2.3333
CB 0.7143 1.0000 1.2500 0.8333 1.6667
CC 0.5714 0.8000 1.0000 0.6667 1.3333
CD 0.8571 1.2000 1.5000 1.0000 2.0000
CE 0.4290 0.6000 0.7500 0.5000 1.0000
Table 3.3(c) is the starting of eigenvector solving for current contract. A short computational
way to obtain this ranking is to raise the pairwise matrix to powers that are successively
squared each time.
CA CB CC CD CE
CA 7/7 7/5 7/4 7/6 7/3
CB 5/7 5/5 5/4 5/6 5/3
CC 4/7 4/5 4/4 4/6 4/3
CD 6/7 6/5 6/4 6/6 6/3
CE 3/7 3/5 3/4 3/6 3/3
Table 3.3(c): Squaring the matrix for alternative (Current Contract)
Figure 3.4(a): compute eigenvector for alternative (Current Contract)
CA CB CC CD CE
CA 5.0001 7.0000 8.7500 6.3334 11.6667
CB 3.5721 5.0000 6.2500 4.1667 8.3333
CC 2.8577 4.0000 5.0000 3.3333 6.6667
CD 4.2865 6.0000 7.5000 5.0001 9.9999
CE 2.1437 3.0006 3.7508 2.5005 5.0010
5.0001 + 7.0000 + 8.7500 + 6.3334 + 11.6667 38.7502
3.5721 + 5.0000 + 6.2500 + 4.1667 + 8.3333 27.3221 =
2.8577 + 4.0000 + 5.0000 + 3.3333 + 6.6667 21.8577
4.2865 + 6.0000 + 7.5000 + 5.0001 + 9.9999 32.7865
2.1437 + 3.0006 + 3.7508 + 2.5005 + 5.0010 16.3966 TOTAL = 137.3966
Figure 3.4(a) shows that computing the eigenvector determines the relative ranking of
alternative under current contract criteria
Figure 3.4(b): Normalize the eigenvector for alternative (Current Contract)
The row sums of alternatives for current contract are normalized in Figure 3.4(b)
3.2.2.3 Price
Table 3.4(a) shows the pairwise comparisons determines the preference of each alternative
over another in items of price.
Normalize = 0.2826 0.1993 0.1594 0.2391 0.1196
Table 3.4(a): Pair-wise comparison for alternative (Price)
CA CB CC CD CE
CA 3/3 3/5 3/6 3/9 ¾
CB 5/3 5/5 5/6 5/9 5/4
CC 6/3 6/5 6/6 6/9 6/4
CD 9/3 9/5 9/6 9/9 9/4
CE 4/3 4/5 4/6 4/9 4/4
Table 3.4(b) shows that the fractions is converted to decimals.
Table 3.4(b): Squaring the matrix for alternative (Price)
Table 3.4 is the starting of eigenvector solving for price. A short computational way to obtain
this ranking is to raise the pairwise matrix to over that are successively squared each time.
Table 3.4(c): Squaring the matrix for alternative (Price)
CA CB CC CD CE
CA 1.0000 0.6000 0.5000 0.3333 0.7500
CB 1.6667 1.0000 0.8333 0.5556 1.2500
CC 2.0000 1.2000 1.0000 1.6666 1.5000
CD 3.0000 1.8000 1.5000 1.0000 2.2500
CE 1.3333 0.8000 0.6667 0.4444 1.0000
CA CB CC CD CE
CA 5.0000 4.0000 2.5000 1.6667 3.7599
CB 8.3334 5.0001 4.1667 2.7778 6.2501
CC 10.0001 6.0001 5.0001 3.3333 7.5001
CD 15.0001 9.0000 7.5000 4.9999 11.2500
CE 6.6666 3.9999 3.3333 2.2222 4.9999
Figure 3.5(a): compute eigenvector for alternative (Price)
Figure 3.5(b) shows that computing the eigenvector determines the relative ranking of
alternatives under price criteria
Figure 3.5(b): Normalize the eigenvector for alternative (Price)
The row sums of alternatives for price are normalized in Figure 3.5(b)
5.0000 + 4.0000 + 2.5000 + 1.6667 + 3.7599 16.9266
8.3334 + 5.0001 + 4.1667 + 2.7778 + 6.2501 26.5281 =
10.0001 + 6.0001 + 5.0001 + 3.3333 + 7.5001 31.8337
15.0001 + 9.0000 + 7.5000 + 4.9999 + 11.2500 47.7499
6.6666 + 3.9999 + 3.3333 + 2.2222 + 4.9999 21.2219 TOTAL= 144.2600
0.1173 Normalize = 0.1839 0.2207 0.3310 0.1471
3.2.2.4 Past Performance
Table 3.5(a) shows the pairwise comparisons determines the preference of each alternative
over another in terms of past performance.
Table 3.5(a): Pair-wise comparison for alternative (Part Performance)
Table 3.5(b) shows that the fractions is converted to decimals.
Table 3.5(b): Pair-wise comparison in decimals for alternative (Past Performance)
CA CB CC CD CE
CA 5/5 5/7 5/3 5/8 5/4
CB 7/5 7/7 7/3 7/8 7/4
CC 3/5 3/7 3/3 3/8 ¾
CD 8/5 8/7 8/3 8/8 8/4
CE 4/5 4/7 4/3 4/8 4/4
CA CB CC CD CE
CA 1.0000 0.7143 1.6667 0.6250 1.2500
CB 1.4000 1.0000 2.3333 0.8750 1.7500
CC 0.6000 0.4286 1.0000 0.3750 0.7500
CD 1.6000 1.1429 2.6667 1.0000 2.0000
CE 0.8000 0.5714 1.3333 0.5000 1.0000
Table 3.5 (c) is the starting of eigenvector solving for past performance. A short computation
way to obtain this ranking is to raise the pairwise matrix to powers that are successively
squared each time.
Table 3.5(c): Squaring the matrix for alternative (Past Performance)
Figure 3.6(a): compute eigenvector for alternative (Past Performance)
Figure 3.6(a) shows that computing the eigenvector determines the relative ranking of
alternatives under past performance criteria
CA CB CC CD CE
CA 5.0000 3.5715 8.3334 2.7500 6.2501
CB 7.0000 5.0001 11.6663 4.3747 8.7498
CC 3.0000 2.1429 5.0001 1.8750 3.7501
CD 8.0001 5.7144 12.3334 5.0001 10.0001
CE 3.9999 2.8571 6.6666 2.5000 5.0000
5.0000 + 3.5715 + 8.3334 + 2.7500 + 6.2501 25.9050
7.0000 + 5.0001 + 11.6663 + 4.3747 + 8.7498 36.7911
3.0000 + 2.1429 + 5.0001 + 1.8750 + 3.7501 15.7681
= 8.0001 + 5.7144 + 12.3334 + 5.0001 +10.0001 41.0481
3.9999 + 2.8571 + 6.6666 + 2.5000 +5.0000 21.0236
TOTAL= 140.5359
0.1843 0.2618 Normalize = 0.1122 0.2921 0.1496
Figure 3.6b): Normalize the eigenvector for alternative (Past Performance)
The row sums of alternative for past performance are normalize in Figure 3.6(b)
3.2.2.5 Technical Capability
Table 3.6(a) shows the pairwise comparisons determines the preference of each alternative
over another in terms of technical capability.
Table 3.6(a): Pair-wise comparison for alternative (Technical Capability)
Table 3.6(b) shows that the fractions is converted to decimals
CA CB CC CD CE
CA 6/6 6/5 6/7 6/8 6/6
CB 5/6 5/5 5/7 5/8 5/6
CC 7/6 7/5 7/7 7/8 7/6
CD 8/6 8/5 8/7 8/8 8/6
CE 6/6 6/5 6/7 6/8 6/6
Table 3.6(b): Pair-wise comparison in decimals for alternative (Technical Capability)
Table 3.6 (c) is the starting of eigenvector solving for technical capability. A short computational
way to obtain this ranking is to raise the pairwise matrix to powers that are successively
squared each time.
Table 3.6(c): Squaring the matrix for alternative (Technical Capability)
Figure 3.7(a): compute eigenvector for alternative (Technical Capability)
CA CB CC CD CE
CA 1.0000 1.2000 0.8571 0.7500 1.0000
CB 0.8333 1.0000 0.7143 0.6250 0.8333
CC 1.1667 1.4000 1.0000 0.8750 1.1667
CD 1.3333 1.6000 1.1429 1.0000 1.3333
CE 1.0000 1.2000 0.8571 0.7500 1.0000
CA CB CC CD CE
CA 4.9999 5.9999 4.2856 3.7500 4.9999
CB 4.1666 4.9999 4.1428 3.1250 4.1666
CC 5.8334 7.0001 5.0000 4.3751 5.8334
CD 6.6666 8.0000 5.7140 5.0000 6.6666
CE 4.9999 5.7999 4.2856 3.7500 3.7500
4.9999 + 5.9999 + 4.2856 + 3.7500 + 4.9999 24.0353
4.1666 + 4.9999 + 4.1428 + 3.1250 + 4.1666 20.6009 =
5.8334 + 7.0001 + 5.0000 + 4.3751 + 5.8334 28.0420
6.6666 + 8.0000 + 5.7140 + 5.0000 + 6.6666 32.0472
4.9999 + 5.7999 + 4.2856 + 3.7500 + 3.7500 23.8353
TOTAL = 128.5607
Figure 3.7(a) shows that computing the eigenvector determines the relative ranking of
alternatives under technical capability criteria.
Figure 3.7(b): Normalize the eigenvector for alternative (Technical Capability)
The row sums alternatives for technical capability are normalize in Figure 3.7(b)
3.2.3 Matrix Criteria vs. Alternatives
FP CC P PP TC CA 0.1771 0.2826 0.1173 0.1843 0.1870 0.1508 CA CB 0.2214 0.1993 0.1839 0.2618 0.1602 0.0503 CB X CC 0.1642 0.1594 0.2207 0.1122 0.2181 0.3518 CC CD 0.3100 0.2391 0.3310 0.2921 0.2493 0.2461 CD CE 0.1272 0.1196 0.1471 0.1496 0.1854 0.2010 CE
Figure 3.8(a): criteria vs. alternative
Normalize = 0.1870 0.1602 0.2181 0.2493 0.1854
Figure 3.8(a) shows the last step to get the solution. The normalize value of alternatives of each
criterion are multiplied to the normalize value of criterion. Each value of each row then total up
to get the sum.
0.1629 CA
0.2047 CB 3 0.1819 CC
0.2972 CD
0.1510 CE
Figure 3.8(b): Priority
Figure 3.8(b) is the last value get for the ah calculation. The most highest ranking goes to
Contractor D with value 0.2972. the second most highest goes to Contractor B with value
0.2047. and the third most highest to Contractor C with value 0.1819.
3.3 Framework
Figure 3.9 shows the framework design of JKR Tender Suggestion System. Firstly ,
contractor register their company details into the system and wait for an approval. The system
will give notification to the admin on new registration request. Admin will view and check the
details of the company and give a feedback whether approve or disapprove. The system then
send the feedback from admin to the contractor. Admin can manage tender such as view , add ,
delete , and update. Admin also need to key in some of details into system that filled by
registered contractor in their submitted tender form. This is a must which will be needed in
tender evaluation. AHP method is implemented in this system to calculate the which contractor
or company deserve the tender. The AHP will collect the details of contractor or company and
criteria needed from database then calculate to get the priorities of the contractor or compony.
Once the AHP calculation is done, system will send to the admin the top three priorities of the
compony for organization to decide.
3.4 Design
3.4.1 Context Diagram
Figure .10 shows the context diagram of this system. The diagram explained that:
1. Contractor makes registration into the system. System will give a notification to the
admin then system will give an approval feedback that sent by admin to the contractor.
2. Contractor can view the avaible system.
3. Contractor can apply tender
4. Contractor can view status of apply.
5. Admin manage the tender such as add, delete, or view the tenders.
6. Admin view report from the system.
3.4.2 DFD
Figure 3.11 shows an in-depth processes flow of the system based on Context Diagram that had
just being explained before. This where the details of functionality is being stretch out for a
better understanding of what the system do.
There are eight processes in this tender suggestion system which are Manage user,
manage contractor, Manage Tender, View Tender, count, view top contractor and Report. There
are also five data stored involved which are alternative File, contractor File, Admin File, criteria
File and Tender File.
Process 1.0: Manage user
• In this module, contractor user makes a registration of the company in this
process. The user need to fill in the form provided in this registration process to
complete the process. Then, the conductor need to wait for an approval from the
system in order to login into the system. Once the company’s registration is
approved, the conductor can manage the company’s profile in the system. All the
information and details of the conductor and company are stored in Contractor
file.
• This module allows administrator user and contractor users to login into system to
use the system. The system only has one login page. Thus, the system will check
for the status when there is a user login into the system to identify the admin and
the conductor. This login data will be held in database when admin finish the
registration all data from username and password entered by conductors.
Password and username for the two entities will be stored in the database.
Process 2.0: Manage Contractor
• This module allows contractor user makes a update profile of the company in this
process. All the information and details of the conductor and company are stored
in Contractor file
Process 3.0: Manage Tender
• This module can only be accessed by the admin. In this process, admin can
manage the tenders such as add new tender, update tenders and the details, delete
the expired tender and also view the current tender. This process is important, any
mistakes in managing the tender will cause misinterpretation of information.
Process 4.0: View Tender
• This module allows the conductors to view the available and active tenders in
JKR for that time. The list of the available of tender are called from database. The
contractor can view all the details of the tenders and if interested, all the details
and terms and condition also can viewed.
Process 5.0: Apply Tender
• This module allows the conductors to apply the available and active tender. The
contractor has to fill the criteria. All the information and details of the criteria are
stored in Criteria file.
Process 6.0: Count
• This module allows the count for the criteria. The list of the criteria of contractor
are called from database criteria. All the information and details of the count are
stored in Alternative file.
Process 7.0: View Top Contractor
• This module allows the conductors to view the top three of contractor. The list of
the top three of contractor are called from database.
Process 8.0: Report
• This module only can be accessed by the admin to get report the system. This is
where the system will give suggestion on which contractor which tender. The
admin then will give the report to the decision maker to make decision based on
the suggestion provided by the system.
3.4.3 ERD
Figure 3.12 shows the tables and their relationships one to another. All table are normalized to a
satisfactory level as they are no redundant fields except for look up table.
3.5 Summary
In this chapter, the focus is more to technical part of this project which are the sample of
manual AHP calculation, the explanation of context diagram, data flow diagram and entity
relationship diagram. This chapter is important as a reference when developing the system.
CHAPTER IV
IMPLEMENTATION
4.1 Introduction
In this chapter, discussed on the difference of interface between the user which
are admin and contractor. Screen shot for each interface and form were given.
Figure 4.1 shows the home page of the JKR Tender Suggestion System. This system involved
two actors or users which are the contractor and also the admin.
4.2 Admin
Admin have accesses to almost all section of the system. Figure 4.2 shows a login from for
admin and contractor. Admin need to login into the system using userID and Kata Laluan.
Admin will directly into the admin homepage once the admin is successfully logged into the
system which will allow admin to view and update the process in the system.
Figure 4.3 shows the registration approval page for admin. Admin go to the “syarikat” page
which admin will be viewed the list of company. That list is divided by three section which the
first section is the list of waiting registration approval company for the admin to whether approve
or reject the registration request. The other section is the disapproved company registration
request which admin may make the disapproved company be approved. The last section is the
list of the approved registration company which the admin can cancel the approval if finding
irrelevant information or else.
The figure 4.4 displayed by the system as an alert when admin press [Lulus] from the waiting
status registration list. Admin may press OK to finish the approval or Cancel to cancel the
approval of the particular registration request.
The figure 4.5 displayed by the system as an alert when admin press [Tidak Lulus] from the
waiting status registration list.
The same thing goes to the other sections of approval.
Figure 4.6 shows the tender details Form for admin to fill in to add new tender into the system.
Figure 4.7 shows an example of admin add new tender details into the system by filling up the
form.
The figure 4.8 shows that the tender successfully added into the system and redisplay the details
when admin press PRINT button.
Figure 4.9 shows the list of the tender with details. Admin will be viewed the list of tender in the
system. Admin may delete the tender by pressing the PADAM button. This KEMASKINI button
is for admin to update the details of the tender. While this … button is for admin to insert the
criteria and alternative for that particular tender to be evaluate.
Figure 4.10 shows the update of tender form. Once admin press the SUBMIT button on a
particular tender, admin will be directed to a page containing the details of that tender. Admin
may update the details.
Figure 4.11 shows the alert pop out on update. When admin make update to a particular tender,
an alert will pop out when admin press OK button to confirm the update made.
Figure 4.12 shows an alert of deletion. An alert will be displayed to the admin once admin press
the OK button on a particular tender to confirm the cancellation or deletion of the tender.
4.3 Contractor/company
This figure 4.15 is for registration of new contractor into the system. The contractor press the
Register here and will be directed to a details registration form.
The figure 4.16 shows the registration form need to be filled in by the contractor.
The figure 4.17 shows that the form been filled in bye contractor and the details will be saved
once the contractor press the … button.
The figure 4.18 shows that the registration is successfully saved. The contractor needs to wait
until registration being approved by the admin of the system.
Figure 4.19 shows the login form for admin and contractor. Once the admin approved the
registration. Contractor can login into the system and have access to the system.
Figure 4.20 shows the profile of a registered contractor from the system. The approved
contractor can view their profile and update their profile once logged in. the contractor must
accesses the ‘Profile’ page to vie their company profile.
Figure 4.21 shows the from that can be updated. The contractor will be redirected to this page
when contractor press … button at the bottom of the view company profile page. In this page the
contractor may update the information and details of the company.
The figure 4.22 shows an alert is given to the contractor to confirm to process updating
information. The contractor may select the … to proceed or … to cancel the update information.
This figure 4.26 shows the list of tender displayed to be view by the contractor.
4.4 Summary
As the whole, this chapter focused more on user manual in using this system. As
discussed in this chapter, admin involve in a lot of processes such as approve registration
request, add new tender, view, update and delete tender, add criteria and alternatives for tender
evaluation. While contractor can register, add update profile.
CHAPTER V
TESTING
5.1 Introduction
In this chapter, the processes in the JKR Tender Suggestion System is tested to prove that
every function is working properly. The suitable approach for this system is also selected.
5.2 Testing Approach
Proactive technique of testing approach is selected for this system. Proactive approach is
an approach in which the test design process is initiated as early as possible in order to find and
fix the defects before the builds is created. As for my documentation, I used test cases as to prove
my system testing.
5.3 Admin
Test Case #: 1 Test Case Name: Admin login
System: JKR Tender Suggestion
System
Subsystem: Admin
Designed By: Muhammad Zharif Design Date:
Executed by; Muhammad Zharif Execution Date:
Short description:
Pre-conditions:
1. The user must have not accessed into system yet
2. The user has their own Login ID and password
3. Admin is on the homepage
Step Action Expcted System
Response
Pass
/ail
Comment
1 Admin enter the username in
“Login” button
Highlight the password
field as it was empty
Pass
2 Admin enter the password Pass
3 Admin clicks on the
“Login” button
System will verify the
Username and password
entered
pass
Post-condition:
1. User will be able to log into the system if their Username and password matched.
2. User unable to log into the system as either Username or password invalid.
References
Saaty, T.L(2008). Decision making with the analytic hierarchy process
International journal of services sciences,1(1),83-98.
Padumadasa, E.U., Colombo, S.R.I., &Rehan, S.(2009,July)
Investigation in to decision support system and multiple criteria decision making to
develop a web based tender management system. In International Symposium on the
Analytic Hierarchy Process.
Triantaphllou, E., & Mann, S. H. (1995). Using the analytic hierarchy
Process for decision making in engineering application some challenger. International
Journal of industrial Engineering: Application and Practice, 2(1), 35-44.
Saaty, T. L. (1980) The analytic Hierarchy Process. McGraw-Hill, New York.
Coyle R G(2004) Practical Strategy. Open Access Material. AHP
Saaty, T. L. (1980) The analytic Hierarchy Process. McGraw-Hill International.
Alexander, M. (2012). Decision Making using the Analytic Hierarchy
Process (AHP) and SAS/IML’. SouthEast SAS Users Group.
Ros Haslinda Alias, Noor Maizura Muhammad Noor(2009)
Agent-Based Decision Support System in Tendering Process. Proceeding of APSEC2009
Workshop & Tutorial and SEPow2009.
Noraziah, A., Norhayati, R., Abdalla, A., Roslina, A. H, Noorlin, M. A., & Affendy, O,
M.(2008)
Novel database system model design for tender management system. Journal of
Computer Science, 4(6), 463.
Ahmad, F., Saman, M.Y.M, Mohammad, F. S, Mohammad, Z , & Awang, W.S.W.(2014).
Group decision support system enhanced AHP for tender evaluation: International
Journal of Digital Information and Wireless Communications.
Koodhar, M. Y., Rind, M., Hub. M., & Shaikh, H.
Semi-automated E-Tendering System with web Services. A case study on tendering
process in Pakistan.
Hosain, M. S., Chowdury, M.S.U., & Sarker, S. (2003)
An Intelligent Tender Evaluation System using Evidential Reasoning Approach.
System,1(2), 13.
Marek J Drudzel & Roser Flynn
Decision Support Systems. New York: Marcel Dekker, Inc, 2002.