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.

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