MY PART TIME TUTOR SELECTION SYSTEM USING K-MEANS ... · This is to confirm that this project entitled My Part Time Tutor Selection System ... K-means is often called an unsupervised

MY PART TIME TUTOR SELECTION SYSTEM USING K-MEANS

ALGORITHM

NUR ZARITH AKILLA BINTI AMBOAKA

BACHELOR OF COMPUTER SCIENCE

(INTERNET COMPUTING) WITH HONOURS

UNIVERSITI SULTAN ZAINAL ABIDIN

2018

MY PART TIME TUTOR SELECTION SYSTEM USING K-MEANS

ALGORITHM

NUR ZARITH AKILLA BINTI AMBOAKA

Bachelor of Computer Science (Internet Computing)

Faculty of Informatics and Computing

Universiti Sultan Zainal Abidin, Terengganu, Malaysia

AUGUST 2018

i

DECLARATION

I hereby declare that this report is based on my original work except for quotations

and citations, which have been duly acknowledged. I also declare that it has not been

previously or concurrently submitted for any other degree at Universiti Sultan Zainal

Abidin or other institutions.

________________________________

Name : Nur Zarith Akilla Binti Amboaka

Date : ..................................................

ii

CONFIRMATION

This is to confirm that this project entitled My Part Time Tutor Selection System

Using K-Means Algorithm was prepared and submitted by Nur Zarith Akilla Binti

Amboaka (Matric Number: BTCL15039761) and has been satisfactory in terms of

scope, quality and presentation as partial fulfilment of the requirement for the

Bachelor of Computer Science (Internet Computing) with honours in Universiti Sultan

Zainal Abidin. The research conducted and the writing of the report was under my

supervision.

________________________________

Name : Dr Suhailan Dato’ Safei

Date : ..................................................

iii

DEDICATION

In the name of Allah, the Most Gracious and the Most Merciful, all praise is only for

Him the documentation and the system for the subject, CSB 35102, Projek Ilmiah

2018/2019 is finished due the time. I would like to take these opportunities to give a

big thanks to my kind supervisor, Dr. Suhailan Bin Dato’ Safei for the valuable idea,

time, support, advice, guidance, and ideas given through the development of research

until complete the part of the project in phase one. Besides that, I also want to dedicate

my appreciation to my beloved family that supports and motivates me during finishing

this project. And not forget I would to thank a lot to friends that willing to lend their

hand for finishing the project. Lastly, thank you everyone who directly or indirectly

involved in the process of making the system and documentation

iv

ABSTRACT

Nowadays some students need an extra pocket money to support their life in

university. One of the ways to get an extra pocket money is to be a part time tutor

either among their friends in university or among the school students outside the

university. Being a part time tutor is so good for them to build their self-esteem and

also to gain an experience for their future career. However, some of them are still

confused to teach since they don’t really know how to assess their abilities in the

specific subject. Moreover, they need to proof to their client or students that they are

capable to teach the subject. In the other side, there is a problem for the admin to

choose the right student for each subject since there is so many applications from

student to be a part timer tutor, plus the student need to pass the subject requirement.

This project was built to classify their abilities to teach a subject based on their

achievement in the courses that they take in university. The student will apply for the

tutor job and fill in the subject requirement. After that, they will be waiting for the

admin to update the result, since the admin will manage the subject requirement group

and classify the student based on their ranking in that subject. This project is important

to convince another student who need a tutor in a specific subject. To realize this

project, clustering technique will be apply using centroid based clustering algorithm,

K-means. K-means is often called an unsupervised learning, as we don’t have

prescribed labels in the data and no class values denoting a priori grouping of the data

instances are given.

v

ABSTRAK

Pada masa kini, terdapat sesetengah pelajar memerlukan wang tambahan untuk

menyara kehidupan mereka di universiti. Salah satu cara untuk mendapatkan wang

saku tambahan ialah dengan menjadi guru sambilan sama ada di kalangan sahabat

mereka di universiti atau di kalangan rakan sekelas mereka. Dengan menjadi guru

sambilan, adalah sangat baik bagi mereka untuk membina keyakinan diri dan juga

untuk mendapatkan pengalaman untuk kerjaya di masa hadapan. Walau

bagaimanapun, sesetengah daripada mereka masih keliru untuk mengajar sesuatu

subjek kerana mereka tidak tahu bagaimana menilai kebolehan mereka dalam

subjek tertentu. Lebih-lebih lagi, mereka perlu membuktikan kepada klien atau

pelajar bahawa mereka mampu mengajar mata pelajaran. Di sisi lain, terdapat

masalah untuk pentadbir memilih pelajar yang tepat untuk setiap mata pelajaran

kerana terdapat begitu banyak penyertaan dari pelajar yang ingin menjadi guru

sambilan. Tambahan pula, pelajar tersebut juga hendaklah memenuhi syarat untuk

jadi guru untuk subjek tertentu. Projek ini dibina untuk mengklasifikasikan

kebolehan mereka untuk mengajar mata pelajaran berdasarkan pencapaian mereka

dalam kursus yang mereka ambil di universiti. Pelajar akan memohon pekerjaan

tutor dan mengisi keperluan subjek. Selepas itu, mereka akan menunggu pentadbir

mengemas kini hasilnya, kerana pentadbir akan menguruskan kumpulan keperluan

subjek dan mengklasifikasikan pelajar berdasarkan ranking mereka dalam subjek

itu. Projek ini penting untuk meyakinkan pelajar lain yang memerlukan tutor

dalam subjek tertentu. Untuk merealisasikan projek ini, teknik clustering akan

digunakan menggunakan algoritma kluster berasaskan centroid, K-means. K-

means sering dipanggil pembelajaran tanpa pengawasan, kerana kami tidak

menetapkan label dalam data dan tidak ada nilai kelas yang menunjukkan

kumpulan priori dari contoh data yang diberikan.

vi

CONTENTS

PAGE

DECLARATION i

CONFIRMATION ii

DEDICATION iii

ABSTRACT iv

ABSTRAK v

CONTENTS vi

LIST OF TABLES vii

LIST OF FIGURES xvi

LIST OF ABBREVIATIONS xv

CHAPTER I INTRODUCTION

1.1 Background 1

1.2 Problem statement 1

1.3 Objectives 1

1.4

1.5

1.6

Scopes

1.4.1 Scope Admin

1.4.2 Scope Student

Limitation of Work

Expected Outcome

2

2

2

1.7 Report Structure 3

CHAPTER 2 LITERATURE REVIEW

2.1 Introduction 4

2.2 Similar System 4

2.3 K-Means Clustering Algorithm

2.3.1 What is Clustering Technique

2.3.2 Introduction to K-Means Clustering

2.3.3 K-Means Clustering Algorithm

6

vii

CHAPTER 3

METHODOLOGY

3.1 Introduction 9

3.2 Iterative Model 9

3.2.1 Requirement Phase 10

3.3 Analysis and System Design 11

3.3.1 Framework Design 11

3.3.2 System Design 12

3.3.3 Data Model 15

3.4

3.3.4 Technique

Summary

18

16

23

CHAPTER 4

IMPLEMENTATION PHASE

4.1

4.2

4.3

4.4

CHAPTER 5

Introduction

Implementation of My Tutor system

Design Interface

Summary

CONCLUSION

24

24

25

33

5.1

5.2

5.3

5.4

5.5

5.6

Introduction

Project Contribution

Result discussion

Project constraint and limitations

Future work

Summary

34

34

34

34

35

35

REFERENCES 38

viii

LIST OF TABLES

TABLE TITLE PAGE

3.1 List of software 10

3.2 List of hardware 11

3.3 Admin data model 15

3.4

3.5

3.6

3.7

3.8

3.9

3.10

3.11

3.12

4.1

4.3

4.4

4.5

Student data model

Subject data model

Subject mark data model

Subject group data model

Student group data model

K-means data model

Academic data model.

Define centroid example

Calculation of new k-means

Test Cases Success Admin Login

Test Cases Success Add Subject

Test Cases Success Update Subject

Test Cases Success Delete Subject

16

16

16

17

17

17

18

19

20

34

34

35

35

ix

LIST OF FIGURES

Figure TITLE PAGE

2.1

2.2

Part Time Post

E-Rezeki website

5

5

2.3 Nearest cluster assignment formula 7

2.4 Centroids update formula 7

3.1 Iterative Model 9

3.2

3.3

3.4

3.5

3.6

3.7

3.8

4.1

4.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

4.10

4.11

4.12

4.13

4.14

4.15.1

4.15.2

4.16

System Framework

Context diagram

Data flow diagram level-0 (Admin)

Data flow diagram level-0 (Student)

Entity Relationship diagram

Subject mark example

Subject mark example graph

Main interface

Register page

Dashboard page

Profile page

Academic page

Subject page

History page

Report page

Profile page (Admin)

Manage tutor page

Manage subject page

Manage group page

Manage history page

Calculation page

K-means clustering table page

K-means clustering graph page

Admin report page

11

12

13

14

15

18

19

25

25

26

26

27

27

28

28

29

29

30

30

31

31

32

32

33

x

LIST OF ABBREVIATIONS / TERMS / SYMBOLS

CD Context Diagram

DFD Data Flow Diagram

ERD Entity Relationship Diagram

FYP Final year project

xi

LIST OF APPENDICES

APPENDIX TITLE PAGE

A Appendix 1 39

1

CHAPTER I

INTRODUCTION

1.1 Background

My Part Time Tutor Selection System Using K-Means Algorithm is a web base

application system. This system is to help students who want to be a part-timer

teacher to teach subject that fit their skills in a particular subject. The problem

is how to classify tutor teacher among students according to certain subject

correctly. As example, if they wanted to be a tutor in Data Structure subject,

they must have a good result in basic programming subject and object-oriented

programming subject. The system will group the potential tutors that nearly

matched to the subject requirement. To realize the system, K-Means Clustering

Algorithm will be used. To apply a tutor jobs, they need to fill in subject grade

and the grade will be calculated based on the centroids to determine they are in

the right tutors group.

1.2 Problem Statement

To find the best tutor, we have to assign them in a group that fit their skills in

particular subject. The problem is how to classify tutor teacher among students

according to certain subject correctly

1.3 Objectives

There is three main objective that to develop this system such as:

1.3.1 To analyze a group recommendation for Tutor Selection

System.

1.3.2 To design a proposed system Tutor Selection System based on

Student’s Academic Achievement using K-Means technique.

2

1.3.3 To develop system of Tutor Selection System based on

Student’s Academic Achievement using K-Means technique.

1.4 Scope

There is two scope in this system :

1.4.1 Scope Admin

1.4.1.1 Admin can login to the system.

1.4.1.2 Admin can manage profile, which the part timer tutor

profile.

1.4.1.3 Admin can create, update, and delete user profile.

1.4.2 Scope Student

1.4.2.1 Student can register to the system.

1.4.2.2 Student can add, update and delete their details in the

system.

1.4.2.3 Student need to fill in profile form and educational form

in the system.

1.4.2.4 Student can view recommended subject to teach at the

system.

1.5 Limitation of Work

1.5.1 The subject marks are entered manually by the students. It is up

to the management to validate the data.

1.5.2 This system only can cluster the result and give

recommendation to the part timer tutor.

1.6 Expected Outcome

This system is expected to group part time tutors based on similar course

achievement and assign them with a suitable subject to teach that suit their

skill. Finally, students will be given a list of recommended subjects that is

suitable with their range group.

3

1.7 Report Structure

This report structure has six (6) chapters. In the Chapter 1, the content consists

of project background, problem statement of project, the objective and system

scope. Then, Chapter 2 is about the study of literature review. This chapter is

reviewing the previous systems. The next is Chapter 3, describes the

methodology of research. This research used iterative model. Chapter 4

explains the system’s framework and design. Then, Chapter 5 is all about

implementation, testing and result. Lastly, Chapter 6 is the conclusion of the

whole project.

4

Chapter 2

LITERATURE REVIEW

2.1 Introduction

This chapter describes and explains about the literature review about technique

used for the development of a My Part Time Tutor Selection System on

student’s subject achievement using K-Means Clustering Algorithm.

2.2 Similar System

2.2.1 Manual System

My Part Time Tutor Selection System Using K-Means Algorithm is a project

that built to help an organization to choose the best tutor teacher among

student. The system will choose a tutor will choose a tutor base on a subject

that there are good with, which is they will be choose based on their

achievement in particular subject by calculate their grade of the subject. This is

because not all of the student is good with every subject they take. Some of

them have a high understanding and good achievement in particular subject.

This is what we want so that they can teach the other who didn’t good at the

subject. Nowadays, a normal procedure for tutor or lecture or teacher selections

are based on CGPA and interview session. This method does not guarantee

completely that the selected tutor is good in the job scope given. There is a lack

of selection based on certain subject achievement.

5

2.2.2 Part Time Post

Figure 2.1 Part Time Post

Figure 2.1 above shows the Part Time Post system which is provide many parts

times job for the user based on the requirement that has been set. This system is

very helpful for those who are looking for the part time job including to be a

tutor teacher.

2.2.3 E-Rezeki

Figure 2.3 E rezeki website

Figure 2.3 above shows the e-rezeki system which is they integrate the Part

Time Post system so that the tutor is easy to find a job anywhere.

6

2.3 K-Means Clustering Algorithm

2.3.1 What is clustering technique

Clustering is a technique for finding similarity groups in a data, called clusters.

It is attempts to group individuals in a population together by similarity, but not

driven by a specific purpose. Clustering is often called an unsupervised

learning, as you don’t have prescribed labels in the data and no class values

denoting a priori grouping of the data instances are given (Manu Jeevan,2017).

This K-Means clustering is purposed by J.B. MacQueen (Zhang Yufang,2003).

2.3.2 Introduction to K-Means Clustering Algorithm

K-Means is a method of clustering observations into a specific number of

disjoint clusters. The ‘K’ refers to the number of clusters specified. Various

distance measures exist to determine which observation is to be appended to

which cluster. The algorithm aims at minimizing the measure between the

centroid of the cluster and the given observation by iteratively appending an

observation to any cluster and terminate when the lowest distance measure is

achieved.

2.3.3 K-Means Clustering Algorithm

K-Means defines a prototype in terms of a centroid, which is usually the mean

of a group of points and is typically applied to objects in a continuous n-

dimensional space. The K-Means clustering technique is simple and we begin

with a description of the basic algorithm.

2.3.3.1 Initial Centroids Selection

We first choose K initial centroids, centroid (k) is referring to a cluster centre

that is represented using the feature points for a group of the nearby assigned

objects. It is also used as a reference point in assigning objects into a cluster

based on their nearest distance to the centroid. In the beginning of the

assignment process, a number of K set of initial centroids need to be

7

predetermined so that the objects can be assigned accordingly. In basic K-

Means, these initial centroids are randomly selected among objects.

2.3.3.2 Nearest Cluster Assignment

Each point is then assigned to the closest centroid, and each collection of points

assigned to a centroid cluster. Clustering process begins by measuring each

object distance on each centroid (mk).

Figure 2.3 Nearest cluster assignment formula

where Sik is set of the object in cluster-k, k= 0 to K and d is a feature. The

objects will be assigned to a cluster where they have the closest distance to the

centroid. The distance measurement is using the Euclidean distance method; a

typical K-Means nearest object measurement.

2.3.3.3 Centroids Update

Then, the centroid of each cluster updated based on the points assign to the

cluster. We repeat the assignment and update steps until no point changes

clusters, or equivalently, until the centroids remain the same. This is the final

step where once the objects have been re-assigned, the centroid for each cluster

needs to be re-calculated.

Figure 2.4 Centroids update formula

where M is the total of objects in cluster-k, k = 0 to K and d=0 to D. This step

is to ensure that all objects that currently assigned to a cluster definitely belong

to that cluster (i.e. nearest to its new assigned centroid) and far away from

other clusters. If there is an object that turns out to be nearer to another

centroid, then this object needs to be reassigned to the nearest cluster. Thus,

iteratively, the whole process cycle starting from step (b) to (c) needs to be

repeated until there are no changes to the centroids in all clusters.

8

2.3.3.4 Basic K-Means Algorithm

1; Select K points as initial centroids.

2; repeat

3; Form K clusters by assigning each point to its closest centroid.

4; Recompute the centroid of each cluster.

5; until Centroids do not change.

9

Chapter 3

METHODOLOGY

3.1 Introduction

This chapter will discuss the methodology that has been used to develop the

system from the beginning until the system is completed. Methodology process

is very important in develop our system. It is because, it can describe step by

step about how to develop the system and also as a revision for the next

generation who will continue expand or to study the system. In addition, a

methodology is a formalized approach to implement Software Development

Life Cycle (SDLC). There are various models defined and designed for

software development process. The chosen SDLC model to develop this

system is Iterative Model Life Cycle. Details for every phase involved in this

system development will be explained in this chapter.

3.2 Iterative Model

Figure3.1 Iterative Model

In this model the process starts from the requirements and iteratively enhance

the requirements until the final software implemented. The development

begins by specifying and implementing just part of the software, which can

10

then be reviewed in order to identify further requirements. This process is then

repeated, producing a new version of the software for each cycle of the model.

This model works on four phases. The phases are, requirement phase, design

phase, implementation phase and evaluation phase. This model purposely used

because we can possibly do a better testing at each iteration. In addition, this

model does not require high complexity rate and the feedback is generated

quickly. However, this model requires planning of technical level and also it is

not easily understandable.

3.2.1 Requirement Phase

In this phase, the requirement for the software are gathered and analyzed.

Iteration should eventually result a requirements phase that produces a

complete and final specification of requirements.

3.2.1.1 Software Requirement

Software used to develop the My Part Time Tutor Selection.

Table 3.1 List of Software

11

3.2.1.2 Hardware Requirement

Hardware used to develop the My Part Time Tutor Selection System.

Software Description

Laptop

• HP 15-r236TX

Processor: Intel® Core™ i3-4005U CPU @

1.7 GHz

RAM: 8.00 GB

OS: Window 10

GPU: NVIDIA GeForce FT 820M

Table 3.2 List of Hardware

3.3 Analysis and Design Phase

In this phase, the software solution to meet the requirement is designed. The

diagram of system framework, Context Diagram (CD), Data Flow Diagram

(DFD) and Entity Relationship Diagram (ERD) is built to clarify about the

actual system.

3.3.1 Framework Design

Figure 3.2 System Framework

The figure above shows the overview of the system. Both admin and student

will register and login to the system. Admin will update the available tutor

subject to the system, and student can view and apply as many subjects they

12

want. During apply for the subject, they will enter the requirement subject

mark and the mark will be calculate using K-Means technique in the system.

Once the calculation is done, the result we be give to admin for evaluation and

update the result to student if he or she is success or not.

3.3.2 System Design

3.3.2.1 Context Diagram

A system context diagram (CD) is a diagram that defines the boundary

between the system, or part of a system, and its environment, showing the

entities that interact with it. This diagram is a high-level view of a system.

Figure 3.3 Context Diagram

Figure above show the overview flow of the whole system where there is 2

entities included which is Student and Admin.

3.3.2.2 Data Flow Diagram

A data flow diagram (DFD) is a graphical representation of the “flow” of data

through an information system, modeling its process aspects. A DFD is often

used as a preliminary step to create an overview of the system without going

into great detail, which can later be elaborated.

13

3.3.2.2.1 Data Flow Diagram Level – 0

Figure 3.4 Data Flow Diagram Level-0 [Admin]

Figure above show the DFD Level-0 for Admin where there are 6 processes

included in Admin process. First, the admin will register to the system and

directly go the admin site. In the admin site, the admin will update the

available subject to the system and view if there is an application from the

student. Finally, admin will make a report for the choosen student for each

subject.

14

Figure 3.5 Data Flow Diagram Level-0 [Student]

Figure above show the DFD Level-0 for Student where there are 6 processes

included in Student process. First, the student will register to the system and

view their dashboard. Next, the student will be able ti view the available

subject list and insert their subject mark. Then the student will be able to see

their subject history and wait for the admin to update their report for subject to

teach.

3.3.2.3 Entity Relationship Diagram

Entity relationship diagram (ERD) is a graphical representation of entities and

their relationships to each other, typically used in computing in regard to the

organization of data within databases or information systems.

15

Figure 3.6 Entity Relationship Diagram

Figure above show the ERD of the system, where there is 5 entity and 6

relations included.

3.3.3 Data Model

A data model (or data model) is an abstract model that organizes elements

of data and standardizes how they relate to one another and to properties of the

real-world entities.

3.3.3.1 Admin

# Name Type Pk/Fk Description

1 id int(11) Primary Key

2 Username varchar(255)

3 Password varchar(255)

4 AdminPhoto varchar(255)

Table 3.3 Admin Data Model

Table above shows the details of admin data.

16

3.3.3.2 Student

# Name Type Pk/Fk Dscription

1 TutorRegno varchar(255) Primary

Key

2 TutorPhoto varchar(255)

3 TutorName varchar(255)

4 TutorCgpa decimal(10,2)

5 TutorPwd varchar(255)

6 TutorRegdate timestamp CURRENT_TIMESTAMP

Table 3.4 Student Data Model

Table above shows the details of student data.

3.3.3.3 Subject


1 subcode varchar(255) Primary Key

2 subname varchar(255)

3 subcreate timestamp CURRENT_TIMESTAMP

Table 3.5 Subject Data Model

Table above shows the details of subject data.

3.3.3.4 Subject Enrollment


1 id int(255) Primary Key

2 subcode varchar(255) Foreign Key Table subject

3 subgrade decimal(10,2)

4 TutorRegno varchar(500) Foreign Key Table Student

5 subenroll timestamp CURRENT_TIMESTAMP

Table 3.6 Subject Mark Data Model

Table above shows the details of subject mark data where the subcode is taken

from table subject and TutorRegno is taken from table student.

17

3.3.3.5 Subject Group

T

Table 3.7 Subject Group Data Model

Table above shows the details of subject group data where is admin will update

the two requirement subject for each group which is subA and subB.

3.3.3.6 Student Group

Table 3.8 Student Group Data Model

Table above shows the detail of student group data where is the final student

who is choosen in the subject group and require to teach that subject.

3.3.3.7 Kmeans


1 kmeans_id int(255) Primary Key


3 subA float Foreign Key Table subject

4 subB float Foreign Key Table subject

5 cluster int(255)

Table 3.9 Kmeans Data Model

Table above shows the detail of kmeans group data where the requirement

subject will be counted and will be place in the specific cluster.


1 group_id varchar(255) Primary Key

2 groupname varchar(255)

3 subA varchar(255) Foreign Key Table Subject

4 subB varchar(255) Foreign Key Table Subject


1 tgId int(255) Primary Key


3 group_id varchar(255) Foreign Key Table subject Group

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3.3.3.8 Academic


1 a_id int(255) Primary Key

2 a_department varchar(255)

3 a_course varchar(255)

4 a_sem varchar(255)

5 TutorCgpa varchar(255)

6 TutorRegno varchar(255)

Table 3.10 Academic Data Model

Table above shows the details of academic group data.

3.3.4 Technique

3.3.4.1 K-Means Clustering

K-Means Clustering is the simplest unsupervised learning technique that can

solve clustering problem. The step follows a simple and easy way to classify a

given set of data set through a certain number of cluster (assume k clusters)

fixed a prior. In this project we will select two subject mark of students based

on their subject achievement. Below is the example of their subject mark that

has been listed in the record.

Figure 3.7 Subject Mark

19

Figure 3.7 Subject Mark Graph

There are three main process to calculate the K-Means Clustering: -

3.3.4.1.1 Define k centroids, one for each cluster.

First, we have to assume the initial centroid for each cluster randomly, for this

example the initial centroid for cluster one is (1.0,1.0) and the initial centroid

for cluster two is (3.0,4.0). This initial centroid will be use to calculate the

Euclidean Distance for each object to the nearest distance of centroid.

Table 3.11 Define Centroid Example

These centroids should be placed in a wily way because of different location

cause different result. So, is better to place them as much as possible far away

from each other.

3.3.4.1.2 Take each point belonging to a given data set and associated it to

a nearest centroid.

Clustering process begins by measuring each object distance on each centroid.

Calculation for Record 2: -

Cluster 1 = 1(10,1.0) Cluster 2 = 3(3.0,4.0)

20

Euclidean Distance Cluster 1 = √(𝟏. 𝟓 − 𝟏. 𝟎)𝟐 + (𝟐. 𝟎 − 𝟏. 𝟎)𝟐 = 1.12

Euclidean Distance Cluster 1 = √(𝟏. 𝟓 − 𝟑. 𝟎)𝟐 + (𝟐. 𝟎 − 𝟒. 𝟎)𝟐 = 2.5

Therefore, distance cluster 1 is less than cluster 2, so that Record 2 has been

listed in cluster 1. So, cluster 1 has record 1 and 2.

When no point is pending the first step is done. At this point, recalculated k

new centroids as center of the clusters resulting from the previous step is

needed.

3.3.4.1.3 After this k new centroids, a new binding has to be done between

the same data points and nearest new centroids.

This is the last step where once the objects have been re-assigned, the centroid

for each cluster needs to be re-calculated. So that after record 2 has re-assigned

in cluster

1. We need to calculate the new means.

CLUSTER 1 2

Record 1,2 3(no change)

Means (1.25,1.5) (3.0,4.0)

Table 3.12 Calculation of New Means

New Means for Cluster 1 =( 𝟏+𝟏.𝟓

𝟐 ,

𝟐+𝟏

𝟐 ) = (1.25,1.5)

Thus, A loop has been generated, until it notices that the k centroids

change their location step by step until no more changes are done. In the

simplest words, centroids do not move any more.

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3.3.4.2 Implementation K-means Clustering Algorithm in My Tutor

3.3.4.2.1 Declaration and set alternative function.

3.3.4.2.2 Initialized Centroid

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3.3.4.2.3 Assign Cluster

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3.3.4.2.4 Update Centroids

3.3.5 Summary

In conclusion, choosing the right development methodology is very important

because it will affect the whole development process. The right methodology

will help the project to be done perfectly and smoothly. In addition, design

and framework are also important for us to see the picture of our system so

that we can build it smoothly and create a good system flow.

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Chapter 4

IMPLEMENTATION AND RESULT

4.1 Introduction

Implementation and result are executed to ensure the system are developed

according to the main objective of the system and achieve user requirement.

This chapter will give the result of the My Part Time Tutor Selection System or

called My Tutor System that has been develop.

4.2 Implementation of My Tutor System

There are several language that has been used to develop My Tutor System.

For the template interface Bootstrap 3.0 and startbootstrap-agency-gh-pages

has been used. Next, for the server side PHP(Hypertext Pre-processor) has

been used as programming language. PHP is widely used because it is an open

source for general-purpose scripting language and can be embedded into

HTML and it suits for Web development.

For the validation, this system had been used HTML5, PHP and JavaScript.

Validation is very important to make sure user have a low rate to make a silly

mistake when they key in their data. For example, user is required to insert

their data in every insert form. When user skip the insert data then they can’t

submit their data. Finally, an Open Source database also has been used in this

system and the database is MySQL version 10.1.22-MariaDB. Apache use to

run the local host server and the version is 2.4.25. Visual Studio Code used for

writing the code.

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4.3 Design Interface

The design interface is divided into two which is Admin page and user page.

4.3.1 Main Interface

Figure 4.1 Main Interface

Figure 4.1 above shows the main of login interface for both Admin and User.

4.3.2 Register Page

Figure 4.2 Register Page

Figure 4.2 above shows a register page for user. User is required to fill in their

full name, Id nmber and password.

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4.3.3 Dashboard Page

Figure 4.3 Dahboard Page

Figure 4.3 above shows the dashboard page both for Admin and User.

4.3.4 Profile Page

Figure 4.4 Profile page

Figure 4.4 above shows the profile page for user. User can view their name and

matric number. User also can update their full name and profile picture.

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4.3.5 Academic Page

Figure 4.5 Academic Page

Figure 4.5 above shows the academic page. User can update their academic

details like cgpa, faculti, course and semester.

4.3.6 Subject Page

Figure 4.6 Subject Page

Figure 4.6 above show the subject page, where user need to choose the subject

and their subject mark.

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4.3.7 History Page

Figure 4.7 History Page

Figure 4.7 above show the enroll history page for user. User can view all of the

subject that they has been key in and update their subject mark or delete the

subject.

4.3.8 Report Pages

Figure 4.8 Report Pages

Figure 4.8 shows the report pages for user. User can view the full details of

their personal details and academic details. At the bottom of the report they can

view the recommendation group for the to teach.

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4.3.9 Profile Page

Figure 4.9 Profile Page

Figure 4.9 shows the profile page for admin which is admin can view and

update his personal information.

4.3.10 ManageTutor Pages

Figure 4.10 ManageTutor Pages

Figure 4.10 above shows the tutor pages for admin where admin can view all

of the tutor that already register to the system and admin also can delete the

tutor that not active already.

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4.3.11 Manage Subject Pages

Figure 4.11 Manage Subject pages

Figure 4.11 above shows the subject pages for admin to manage. In this section

admin can add a new subject that available, edit the subject name and delete

the unavailable subject.

4.3.12 Manage Group Page

Figure 4.12 Manage Group Page

Figure 4.12 above shows the group pages for admin to manage. In this section

admin need to add two subject that fit the group cirteria. Then, admin also can

edit the subject criteria and delete the gorup.

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4.3.13 Manage History Page

Figure 4.13 Manage History Page

Figure 4.13 above shows the history pages for admin to manage. In this section

admin can view all of the subject that user enroll and admin also can delete the

history for user.

4.14 Calculation page

Figure 4.14 Calculation Page

Figure 4.14 above shows the calculation pages for admin to manage. In this

section admin has to select the group names and number of cluster to start the

calculation.

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4.4.15 Kmeans Page

Figure 4.15.1 Kmeans clustering table

Figure 4.15.1 above shows the clustering result for three cluster in a table. In

this section, admin can add the choosen student to the group and admin also

can delete the student from the cluster if the student is already assign to

another group.

Figure 4.15.2 Kmeans clustering graph

Figure 4.15.2 above shows the clustering result for three cluster in a table. In

this section, admin can view the cluster visually which is can ease the admin to

choose the right student by looking for the highest cluster on top of the graph.

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4.3.16 Report

Figure 4.16 Report Page

Figure 4.16 above shows the report of the clustering result. Admin can view

the student who has been choosen to the recommendation group.

4.4 Testing Analysis

After the development of the system are complete, this system will be test

using two techniques of software testing which are black box testing and white

box testing in order to examine the functionality of the system.

4.4.1 Black Box Testing

Module involve in this testing are:

I. Login

II. Create, retrive, update and delete subject

4.4.2 White Box Testing

Module involve in this testing are:

I. Generate Tutor’s Clustering Result.

II. Generate K-Means Clustering Graph

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4.5 Test Cases

A test case is a set of condition or variables under which tester will determinate

wheatear a system works correctly or under test satisfies requirement. Process

of developing test case may help to find problem in the requirement or design

of an application. It simple define that test cases is a set of condition or

combination of variables under which tester or engineer will identify wheatear

the application under test is working correctly or not. Below shows the cases

for several process in the My Tutor System.

4.5.1 Login

Step Procedure Expected Result Pass/Fail

1. Go to login page Preview page

loaded

Pass

2. Enter the following detail:

Admin Id : Admin

Password : admin17

Message”successfull

login”

Pass

3. Click “Login” Button Pass

Table 4.1 Test Cases Success Admin Login

4.5.2 Admin


1. Click “subject” page Preview page loaded Pass

2. Click “add” button Pass

3. Enter following detail:

Subject Code : C001

Subject Name : English

pass

4. Click “yes” Button Message “New data

added”

Pass

Table 4.3 Test Case Add Subject

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1. Click “subject” page Preview page loaded Pass

2. Click “update” button Pass

3. Enter following detail:

Subject Code : C002

Subject Name : English and

communication

pass

4. Click “yes” Button Message “Data

Updated”

Pass

Table 4.4 Test Case Update Subject


1. Click subject page Preview page loaded Pass

2. Click “delete” button Message “are you

sure want to delete”

Pass

3. Click “yes” Button One row deleted

from table

Pass

Table 4.5 Test Case Delete Subject

4.6 Summary

As a conclusion, this chapter briefly discussed about the implementation of

code, interface design and testing the final result of the system. After all of the

element where tested, the system shows exellent result where everything is

work as planned.

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Chapter 5

CONCLUSION

5.1 Introduction

This chapter will discuss a conclusion oh this project and the content on this

chapter are summary for the whole project, project contribution, project

limitation and some suggestion for the future.

5.2 Project Contribution

My Tutor system has been developed for final year student in Faculty of

Informatics Computing in UniSZA. It has achieved the objectives and scope of

scope of this project. Below is the list of the achievements on this project:

5.2.1 Generate student’s group achievement group using K-Means Clustering

5.2.2 This system recommended Student a tutor group that suited their skill.

5.2.3 This system gave benefits to the company by getting good tutor position

at their classroom

5.3 Result Discussion

Generally, this project has been carried out and follow the objectives that has

been explained in Chapter 1. This project has introduced the two main criteria

that would be used to calculate the K-Means clustering result which is two

subjects as the criteria. In addition, this project provided a better way to

student to know their recommender group to teach and also helped the admin a

lot to assign the best student in a group.

5.4 Project Constraint and Limitations

There are a few problems and limitations that occur throughout the

development of this project. The problems and limitation in conducting this

study are;

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5.4.1 This system is set default for only two criteria which is Subject

Mark A and Subject Mark B.

5.4.2 The cluster number is set default only from 1 to 5 cluster.

5.4.3 The subject mark needs to insert manually by the student.

5.5 Future Work

There is some suggestion that can be made in order to upgrade the system

to be more efficient in the future. The suggestion are:

5.5.1 Upload a resume to be a tutor.

5.5.2 The criteria will be added nad the cluster are not limited only from

1 to 5.

5.5.3 Put the prices for tutoring lesson per hour.

5.6 Summary

My Part Time Tutor Selection System is the system that focuses of

recommendation of a subject to teach for the student. Based on the previous

study and discussion with supervisor the suitable approach that will be

implement in this project is K-MeansClustering techniques. This system

will help the admin to select the best student for the tutor subject by looking

for cluster ranking on the graph. Hopefully, this system will help the

student to be a tutor teacher that suit the subject requirement.

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REFERENCES

Ju, C., & Xu, C. (2013). A New Collaborative Recommendation Approach

Based on

Users Clustering Using Artificial Bee Colony Algorithm, 2013.

Kodinariya, T. M., & Makwana, P. R. (2013). Review on determining number

of

Cluster in K-Means Clustering. International Journal of Advance Research in

Computer Science and Management Studies, 1(6), 2321–7782.

Li, C. S. (2011). Cluster center initialization method for K-means algorithm

over data

sets with two clusters. Procedia Engineering, 24, 324–328.

https://doi.org/10.1016/j.proeng.2011.11.2650

Li, Y., & Wu, H. (2012). A Clustering Method Based on K-Means Algorithm.

Physics

Procedia, 25, 1104–1109. https://doi.org/10.1016/j.phpro.2012.03.206

Yadav, S., Bharadwaj, B., & Pal, S. (2012). Data mining applications: A

comparative

study for predicting student’s performance. International Journal of Innovative

Technology & Creative Engineering, 1(12), 13–19. Retrieved from

http://arxiv.org/abs/1202.4815

https://doi.org/10.1016/j.proeng.2011.11.2650https://doi.org/10.1016/j.phpro.2012.03.206http://arxiv.org/abs/1202.4815

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APPENDIX

Documents

MY PART TIME TUTOR SELECTION SYSTEM USING K-MEANS ... · This is to confirm that this project entitled My Part Time Tutor Selection System ... K-means is often called an unsupervised