student grouping system based on academic … · 2018. 8. 12. · kepada kumpulan tugas. dengan...

STUDENT GROUPING SYSTEM BASED ON

ACADEMIC ACHIEVEMENT USING K-MEANS

CLUSTERING ALGORITHM

NIK AHMAD RIDHUAN BIN NIK IBRAHIM

BACHELOR OF COMPUTER SCIENCE

(INTERNET COMPUTING)

UNIVERSITI SULTAN ZAINAL ABIDIN

2018

STUDENT GROUPING SYSTEM BASED ON ACADEMIC ACHIEVEMENT

USING K-MEANS CLUSTERING ALGORITHM

NIK AHMAD RIDHUAN BIN NIK IBRAHIM

Bachelor of Computer Science (Internet Computing)

Faculty of Informatics and Computing

Universiti Sultan Zainal Abidin, Terengganu, Malaysia

AUGUST 2018

ii

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 : ..................................................

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

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CONFIRMATION

This is to confirm that this project entitled Student Grouping System Based on

Academic Achievement Using K-Means Clustering Algorithm was prepared and

submitted by Nik Ahmad Ridhuan Bin Nik Ibrahim (Matric Number: BTCL15039674)

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

this report was under my supervision.

________________________________

Name : Dr Suhailan Dato' Safei

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

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DEDICATION

First of all, I would like to take this opportunity to express my greatest gratitude

to my supervisor, Dr. Suhailan Dato' Safei for his teaching, patience and motivation

during development of this project. I was so proud to be supervised by him with

guidance, which has been most memorable experience.

Hence, I would like to thank my parents for giving me the facilitator to complete

this project. They have given me all the limitations and endless moral support that I

have successfully completed this task.

Not forgetting to Faculty of Informatics & Computing (FIK), special thanks for

given me such a valuable change to discover and reveal new things myself with this

project. Besides, I also would like to thank to all other lectures in Faculty of Informatics

& Computing (FIK) in order for me to complete this final year project. Last but not

least, special thanks my supportive friends whom help me to finish up this project.

Thank you.

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ABSTRACT

The student grouping system based on academic achievement using the developed k-

means algorithm. The system uses based on several subjects and then cluster them into

groups that represent similar scores among the subjects. In other words, the group will

represent good and weak students. The result of these groups can be used by the

lecturers to assign students into assignment's groups that consists of good and weak

students. K-Means clustering algorithm is used to cluster the students based on their

subjects' mark similarities. A lecturer can specify two subjects' mark that will be used

as the criteria to group similar students' achievement. Based on the result, the lecturer

can choose one student from each group to be assigned into an assignment group. By

doing this, each assignment's group will consists of good and weak students so that the

weak student can learn from their good peer. The system also notify the students which

group they have been assigned to by their lecturers.

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ABSTRAK

Sistem kumpulan pelajar berdasarkan pada pencapaian akademik menggunakan

algoritma K-Mean yang dibangunkan. Sistem ini berdasarkan beberapa subjek dan

kemudian kumpulan ke dalam kumpulan yang mewakili skor yang sama antara

subjek. Dalam erti kata lain, kumpulan itu akan mewakili pelajar yang baik dan

lemah. Hasil daripada kumpulan-kumpulan ini boleh digunakan oleh pensyarah

untuk memperuntukkan pelajar ke dalam kumpulan tugasan yang terdiri daripada

pelajar-pelajar yang baik dan lemah. Algoritma K-Mean digunakan untuk

mengumpulkan pelajar berdasarkan persamaan subjek mereka. Pensyarah boleh

menentukan tanda dua mata pelajaran yang akan digunakan sebagai kriteria untuk

kumpulan pelajar-pelajar yang sama pencapaian. Berdasarkan hasilnya,

pensyarah boleh memilih satu pelajar dari setiap kumpulan yang akan diberikan

kepada kumpulan tugas. Dengan berbuat demikian, tugasan setiap Kumpulan akan

terdiri daripada pelajar-pelajar yang baik dan lemah supaya pelajar yang lemah

boleh belajar daripada rakan sebaya mereka baik. Sistem ini juga memberitahu

pelajar kumpulan mana mereka yang telah telah diperuntukkan oleh pensyarah.

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TABLE OF CONTENTS

DECLARATION ......................................................................................................... ii

CONFIRMATION ..................................................................................................... iii

DEDICATION ........................................................................................................... iv

ABSTRACT ................................................................................................................. v

ABSTRAK ................................................................................................................. vi

TABLE OF CONTENT ............................................................................................. vii

LIST OF TABLES ...................................................................................................... xi

LIST OF FIGURES ................................................................................................... xii

LIST OF ABBREVIATIONS / TERMS / SYMBOLS .......................................... xiv

CHAPTER 1 ................................................................................................................. 1

INTRODUCTION ....................................................................................................... 1

1.1 Background .......................................................................................................... 1

1.2 Problem Statement ............................................................................................... 2

1.3 Objectives ............................................................................................................ 2

1.4 Scopes .................................................................................................................. 2

1.4.1 Scope of Admin ................................................................................................ 2

1.4.2 Scope of Student ............................................................................................... 3

1.4.3 Scope of Lecturer .............................................................................................. 3

1.5 Limitation of work ............................................................................................... 3

viii

1.6 Expected Results .................................................................................................. 3

CHAPTER 2 ................................................................................................................. 4

LITERATURE REVIEW ........................................................................................... 4

2.1 Related Research and Project ............................................................................... 4

2.1.1 Based on Research System ............................................................................ 4-5

2.2 K-Mean Clustering (Technique) ....................................................................... 5-6

2.2.1 Application That Use K-Mean ....................................................................... 6-7

CHAPTER 3 ................................................................................................................. 8

METHODOLOGY ...................................................................................................... 8

3.1 Introduction .......................................................................................................... 8

3.2 Planning Phase ..................................................................................................... 9

3.3 Requirement Analysis Phase ................................................................................ 9

3.3.1 Hardware Requirement ................................................................................... 10

3.4 Design Phase ...................................................................................................... 10

3.5 Process Model .................................................................................................... 11

3.5.1 Context Diagram ............................................................................................. 11

3.5.2 Data Flow Diagram (DFD) Level 0 Admin .................................................... 12

3.5.3 Data Flow Diagram (DFD) Level 0 Lecturer.................................................. 13

3.5.4 Data Flow Diagram (DFD) Level 0 Student ................................................... 14

3.5.5 Data Flow Diagram (DFD) Level 1 (Process) Register User ......................... 15

3.5.6 Data Flow Diagram (DFD) Level 1 (Process) Manage Subject List .............. 16

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3.5.7 Data Flow Diagram (DFD) Level 1 (Process) Cluster Subject Score............. 17

3.5.8 Framework ................................................................................................. 18-19

3.6 Data Model ......................................................................................................... 20

3.6.1 Entity Relationship Diagram (ERD) ............................................................... 20

3.7 GUI DESIGN ................................................................................................ 21-25

CHAPTER 4 ............................................................................................................... 26

IMPLEMENTATION AND RESULT ..................................................................... 26

4.1 Introduction ........................................................................................................ 26

4.2 Implementation and Output ............................................................................... 26

4.3 Design Interface ................................................................................................. 27

4.3.2 Main Interface ............................................................................................ 27-37

4.4 Testing Analysis ................................................................................................. 38

4.4.2 Black Box Testing ........................................................................................... 38

4.4.3 White Box Testing .......................................................................................... 38

4.5 Test Cases .......................................................................................................... 39

4.5.2 Login ............................................................................................................... 39

4.5.3 Admin ........................................................................................................ 40-42

4.5.4 Lecturer ...................................................................................................... 42-44

4.5.5 Student ............................................................................................................ 44

4.6 Summary ............................................................................................................ 45

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CHAPTER 5 ............................................................................................................... 46

CONCLUSION .......................................................................................................... 46

5.1 Introduction ........................................................................................................ 46

5.2 Project Contribution ........................................................................................... 46

5.3 Limitations ......................................................................................................... 47

5.4 Future Work ....................................................................................................... 47

5.5 Conclusion ......................................................................................................... 47

REFERENCES ........................................................................................................... 48

APPENDIX ................................................................................................................. 49

APPENDIX A : GANTT CHART ............................................................................ 50

xi

LIST OF TABLES

Table 3-1: List of Hardware ......................................................................................... 10

Table 4-1: Test Cases Success Admin Sign In ............................................................ 39

Table 4-2: Test Cases Success Lecturer Sign In .......................................................... 39

Table 4-3: Test Cases Success Student Sign In ........................................................... 39

Table 4-4: Test Cases Add New Student ..................................................................... 40

Table 4-5: Test Cases Update Student ......................................................................... 40

Table 4-6: Test Cases Add New Lecturer .................................................................... 40

Table 4-7: Test Cases Update Lecturer ........................................................................ 41

Table 4-8: Test Cases Delete Student .......................................................................... 41

Table 4-9: Test Cases Delete Lecturer ......................................................................... 41

Table 4-10: Test Cases Add Subject List ..................................................................... 42

Table 4-11: Test Cases Change Profile Picture lecturer .............................................. 42

Table 4-12: Test Cases Add Subject List ..................................................................... 42

Table 4-13: Test Cases Update Group List .................................................................. 43

Table 4-14: Test Cases Delete Group List ................................................................... 43

Table 4-15: Test Cases Generate Cluster ..................................................................... 43

Table 4-16: Test Cases Cluster add Student using Group Identification ..................... 44

Table 4-17: Test Cases Delete Student from Group .................................................... 44

Table 4-18: Test Cases Change Profile Picture Student .............................................. 44

Table 4-19: Test Cases Manage Subject Score ............................................................ 44

Table 4-20: Test Cases Update Subject Mark .............................................................. 45

Table 4-21: Test Cases View Group Details ................................................................ 45

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LIST OF FIGURES

Figure 3.1: Iterative Model ............................................................................................ 9

Figure 3.2: Context Diagram ....................................................................................... 11

Figure 3.3: Data Flow Diagram Level 0 Admin .......................................................... 12

Figure 3.4: Data Flow Diagram Level 0 Lecturer ........................................................ 13

Figure 3.5: Data Flow Diagram Level 0 Student ......................................................... 14

Figure 3.6: Data Flow Diagram Level 1 (Process) Register User ............................... 15

Figure 3.7: Data Flow Diagram Level 1 (Process) Manage Subject List .................... 16

Figure 3.8: Data Flow Diagram Level 1 (Process) Cluster Subject Score ................... 17

Figure 3.9: Framework ................................................................................................. 19

Figure 3.10: Entity Relationship Diagram ................................................................... 20

Figure 3.11: Admin Login .......................................................................................... 21

Figure 3.12: Admin Dashboard .................................................................................... 22

Figure 3.13: Lecturer Login ......................................................................................... 22

Figure 3.14: Lecturer Dashboard ................................................................................. 23

Figure 3.15: Lecturer Generate K-Mean ...................................................................... 23

Figure 3.16: Continues Generate K-Mean ................................................................... 24

Figure 3.17: Student Login .......................................................................................... 24

Figure 3.18: Student Dashboard .................................................................................. 25

Figure 4.1: Main Interface ........................................................................................... 27

Figure 4.2: Admin Login ............................................................................................. 27

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Figure 4.3: Admin Dashboard ...................................................................................... 28

Figure 4.4: Admin Profile Picture ................................................................................ 28

Figure 4.5: Admin Register for Student ....................................................................... 29

Figure 4.6: Admin Register for Lecturer ..................................................................... 29

Figure 4.7: Add Subject List (Admin) ......................................................................... 30

Figure 4.8: Detail of Student (ADMIN) ...................................................................... 30

Figure 4.9: Detail of Lecturer (ADMIN) ..................................................................... 31

Figure 4.10: Lecturer Dashboard ................................................................................. 31

Figure 4.11: Lecturer Profile Picture ........................................................................... 32

Figure 4.12: Manage Group List (Lecturer) ................................................................ 32

Figure 4.13: Cluster Student ....................................................................................... 33

Figure 4.14: List Clustering Result .............................................................................. 33

Figure 4.15: Graph for K-Mean Clustering ................................................................. 34

Figure 4.16: Group List ............................................................................................... 34

Figure 4.17: Recommend Group Details ..................................................................... 35

Figure 4.18: Student Dashboard .................................................................................. 35

Figure 4.19: Student Profile Picture ............................................................................. 36

Figure 4.20: Add Subject Score ................................................................................... 36

Figure 4.21: Update Subject Score .............................................................................. 37

Figure 4.22: Group Details .......................................................................................... 37

xiv

LIST OF ABBREVIATIONS / TERMS / SYMBOLS

CD Context Diagram

DFD Data Flow Diagram

ERD Entity Relationship Diagram

1

CHAPTER 1

INTRODUCTION

1.1 Background

Selecting and collecting students in groups is a complex and difficult task

especially among lecturers at a University. In addition, there are some students who

face problems in producing a group in the classroom.

Furthermore, lecturers also have difficulties to choose and compare students

score especially when considering more than two subjects mark with the highest score

and lowest.

Student Grouping Based on Academic Achievement System Using K-Means

Clustering will be implemented to assist students and lecturers in addressing this

problem. In addition, K-Mean is a cluster method used to represent a group of students.

Besides, K-Means is an algorithm of unorganized learning methods and attempts to be

collected based on their equations. Total irregular scores of students will be collected

based on the equation. In this system, the equations are based on the list of subjects as

well as the number of students scores.

Hence, the result of K-Means groups can help the lecturers to produce the

several groups of students for completing the group's assignments at the university

based on their abilities in the subject.

2

1.2 Problem Statement

a) Lecturers face difficulties in selecting multiple students into a group to produce

the several groups of assignment at the university.

b) Furthermore, there are some students who have difficulties in producing groups

in the classroom based on the assignments provided by the lecturers.

1.3 Objectives

a) To analyse the problem of selecting group students focusing on the subject list

and the subject scores.

b) To design a proposed system of Student Grouping Based on Academic

Achievement System Using K-Means Clustering.

c) To develop a Student Grouping Based on Academic Achievement System Using

K-Means Clustering.

1.4 Scopes

1.4.1 Admin

a) Administrators can act as lecturers, create current sessions, subject lists by

adding, updating and deleting subject details.

b) Administrators can also create, update, and delete users.

3

1.4.2 Student

a) Students can manage profiles, manage subject scores, choose subject code,

current session. Profile Modules consist of add and update student details.

b) In addition, students can view the whole report on their activities that they have

created.

c) Finally, students can access the system anytime and anywhere.

1.4.3 Lecturer

a) Lecturers can manage profiles, choose subject code and choose subject.

b) In addition, the batch will be generated based on clusters. In the Select Cluster

Module, the lecturer must choose the student and the number of groups to

produce the best cluster group of students.

c) Finally, lecturers can review the entire report on their previous activities.

1.5 Limitation of Work

1. It is limited to student selection because the system only focuses on every faculty

in university only to help students in academic achievement.

2. This system can run on web based only.

1.6 Expected Result

The system is expected to combine students based on subject scores and subject lists to

set them up with groups that fit their skills in knowledge of study.

4

CHAPTER 2

LITERATURE REVIEW

2.1 Related Research and Project

There are some research studies and explanations about the related project that has been

done to know the way to of developing this project.

2.1.1 Based on Research System

Based on literature review, there are several existing systems that are

found. The first system is named Extending Moodle grouping functionality using

artificial intelligent techniques. Moreover, this research paper is about how to extend

Moodle grouping functionality in discussion forums using an intelligent grouping

algorithm. This system may implement artificial intelligent that only supports random

group assignment method. Advantages of this system is to clusters are formed and

utilized to form heterogeneous groups which are automatically added in Moodle

Database. The difference of this system is to use artificial intelligent that only supports

random group assignment method and my project use k-mean algorithm to make group

(Elizaphan, Robert&Peter,2017)

The second is named as Evaluating the discussion boards on Blackboard as a

collaborative learning tool. Further, this research paper is to students think of their

5

experience in a junior level course that has a blackboard course presence where the

discussion boards are used extensively by the students. This system may implement

collaborative learning techniques. Advantages of this system is the results and the

participation were very interesting in terms of the feedback via open comments from

the students from the answers to the questions. The difference of this system is to use

discussion boards for get result and my project may use k-mean algorithm to get

recommendation result. (Abdel-Hameed, Michelle,2010).

The third system is named Docebo. Additionally, this research paper is to

Groups are useful whenever there is a set of users with characteristics that are different

than those determining the branches of the organization chart. This system may

implement based on user account ID & username and based on user additional field.

Advantages of this system is can create groups and auto-populate them based on user

additional fields. The difference of this system is to use user account ID to create group

and my project based on skill achievement of subject score and subject list.

(Jonathan,2018).

2.2 K-Mean Clustering Technique

a) K-Means Clustering is one of the methods that can be used to divide objects into

partitions by categories by viewing the given midpoint.

b) The cluster of objects is viewed from the nearest object to the nearest midpoint.

After finding out the nearest point, the object will be classified as a member of

that category.

c) Next is to classify the objects into the existing categories randomly.

6

d) The next step is to compare objects with the entire centroid that exists. Each

object searches the centroid closest to him.

e) After the whole object is compared, the object will be classified in a certain

category based on the nearest centroid. (Fadlika,2013)

2.2.1 Applications That Used K-Mean

The first application is named Dengue fever prediction using K-means clustering

algorithm. Dengue fever is a virus infection which is transmitted to humans by

mosquitoes that living in tropical and subtropical climates and carries the virus. The

functions are focused four stages namely pre-processing, attribute selection, clustering

and predicting the dengue fever. Then, the advantages to help the biotechnologists and

bioinformaticians to move one step forward to discover antibiotic for dengue. (P.

Manivannan, P. Isakki,2017).

The second application is named a smoke detection algorithm based on K-

means. Smoke is considered as main constituent of fire. Then, the functions of this

algorithm use colour feature of smoke and is comprised of following steps: reading the

image, pre-processing, classify colour pixels using k-means from video sequences.

After that, the advantages such as early detection in controlling this damage from danger

to people's lives. (Manish, Princy,2016).

The third application is named K-means algorithm for the road junction time

period analysis. Although, the traffic congestion is one of the important issues in

developed and developing countries. Then, the functions such as use the information

collected by the vehicle detector (VD) to analyse the causes of traffic congestion and

7

find a suitable road junction time period classification. For the advantages are according

to the more precise analysis, the traffic congestion problem can be solved by the

appropriate traffic signal lights cycle arrangements. (Hung-Chi, Chi-Kun,2017).

8

CHAPTER 3

METHODOLOGY

3.1 Introduction

In this chapter, will explain about the methodology that used to develop in this project.

In addition, Student Grouping System Based on Academic Achievement Using K-

Means Clustering Algorithm is developed using Iterative Model. Besides, there are

several phases in developing this project and some of the required system requirements.

However, in iterative model, having a repeat process starts with a simple

implementation of a small set of software requirements and iteratively increases so that

a complete system is executed and ready for use. In fact, the project is built and

enhanced by step by step. Each iteration focuses on a set of specific requirements. In

addition, iterative models can accommodate changes in the very general needs of most

projects.

9

Figure 3.1: Iterative Model

3.2 Planning Phase

In this planning phase, it determines about the problem will be occur in student group

distribution and how to settle it.

3.3 Requirement Analysis Phase

The requirement analysis of this system had been collected and identified. Besides, the

problem statement, objective, system scope and literature review had been defined.

Lastly, data related to this project had been collected by referring to journals, internets

and research papers.

10

3.3.1 Hardware Requirement

The list of hardware that used by this system is as shown below:

Table 3-1: List of Hardware

HARDWARE DESCRIPTION

Laptop

(Acer Aspire v5-471pg)

Processor: Intel Core i5-

[email protected] GHz

RAM: 8 GB

OS: Window 8.1 Pro

GPU: NIVIDIA GeForce

GT 620M

Printer Brothers DCP-J100

3.4 Design Phase

This phase is to identify the design of the system and developed the prototype based in

the functionalities that will be build. The data or requirement obtained during planning

and requirement phase was analyzed and transformed into the design that follow the

identified requirement. The design diagrams have been built covering Framework,

Context Diagram (CD), Data Flow Diagram (DFD) level 0,1 and Entity Relation

Diagram(ERD).

11

3.5 Process Model

3.5.1 Context Diagram

Figure 3.2 shows the Context Diagram for Student Grouping System (SSS) which

includes 3 entities which are Admin, Lecturer and Student. All entities are required to

login into the system before they can access into their interface. Once they are

successfully login, they will be directed to the specific dashboard and they can navigate

to the other processes on the system.

Figure 3.2: Context Diagram

12

3.5.2 DFD Level 0 Admin

Figure 3.3 shows the Data Flow Diagram Level 0 Admin which have an admin that who

uses this system to register users, manage subjects list, create reports.

Figure 3.3: Data Flow Diagram Level 0 Admin

13

3.5.3 DFD Level 0 Lecturer

Figure 3.4 shows the Data Flow Diagram Level 0 Lecturer who have a lecturer using

this system that manages profiles, choose subject list, generate cluster, add students and

also report.

Figure 3.4: Data Flow Diagram Level 0 Lecturer

14

3.5.4 DFD Level 0 Student

Figure 3.5 shows the Data Flow Diagram Level 0 Student who have student that using

this system to manage profile, manage subject score, view group and create report.

Figure 3.5: Data Flow Diagram Level 0 Student

15

3.5.5 DFD 1 (Process) Register User

Figure 3.6 shows the Data Flow Diagram Level 1 for manage registration. Admin can

add new student and delete student, new lecturer, delete lecturer. Besides, Student can

view profile and update their profile. Other than that, Lecturer can view their profile

and update profile.

Figure 3.6: Data Flow Diagram Level 1 (Process) Register User

16

3.5.6 DFD 1 (Process) Manage Subject List

Figure 3.7 shows the Data Flow Diagram Level 1 for manage subject list. Admin can

add new subject, view subject, update subject and delete subject. All subject list data

will be stored in subject score.

Figure 3.7: Data Flow Diagram Level 1 (Process) Manage Subject List

17

3.5.7 DFD 1 (Process) Cluster Subject Score

Figure 3.8 shows the Data Flow Diagram Level 1 for cluster subject score. Lecturer can

make a selection for student. To generate cluster group of student, this system will

collect data from group list, and subject score. Then, the system will update data in

group student list. Then, student can view their recommended group based on their

strength and skill in knowledge.

Figure 3.8: Data Flow Diagram Level 1 (Process) Cluster Subject Score

18

3.5.8 Framework

Figure 3.9 shows the framework for the Student Grouping System. To gain access to

the system, administrators need to log in themselves. Once the login is successful as a

valid user, the admin can take action to register the user (Student and Lecturer).

Then, after a user has been created, they can gain access to the Student Grouping

System by using the identification number and password provided by the admin.

Students can gain access to the system by signing in and viewing the user interface.

Therefore, students can have some of the processes here that add a score of subjects and

updated subject scores. Then, students can see the group based on the score of the

subjects.

Next, Lecturer need to first sign in and login. Lecturers also need to complete

several processes that manage the list of groups and generate lists. Lecturers need to

choose a group course and cluster group (by group). Then, they will be given a list of

cluster group of students. Here, the Lecturer needs to take action by selecting the best

student group results and adding the appropriate group.

19

Figure 3.9: Framework

20

3.6 Data Model

3.6.1 Entity Relationship Diagram

Figure 3.10 shows the relationship diagram of an entity that includes seven (7) entities

namely admin, student, lecturer, subject score, student group, subject list and group list

containing various attributes which is distinctive to ensure the fulfillment of each other's

characteristics.

Figure 3.10: Entity Relationship Diagram

21

3.7 GUI DESIGN

Figure 3.11 shows the GUI design for Admin Login. In this GUI design, administrators

need to login to the system by entering identification and password. Then, press the

SIGN IN button.

Figure 3.11: Admin Login

22

Figure 3.12 shows the Admin Dashboard. In this GUI design, it will display a

dashboard that has guidance for references to administrators in using this system.

Figure 3.12: Admin Dashboard

Figure 3.13 shows the GUI design for Lecturer Login. In this GUI design, lecturer

need to login to the system by entering identification and password. Then, press the

SIGN IN button.

Figure 3.13: Lecturer Login

23

Figure 3.14 shows the Lecturer Dashboard. In this GUI design, it will display a

dashboard that has guidance for references to lecturer in using this system.

Figure 3.14: Lecturer Dashboard

Figure 3.15 shows the Learner Generating K-Mean. In this GUI design, it will display

a list of information generated by generating clusters to students and sorted by matrix

numbers.

Figure 3.15: Lecturer Generate K-Mean

24

Figure 3.16 shows the K-Mean Clustering Graph. In this GUI design, it will display a

group of students grouped by the nearest centroid to create clusters.

Figure 3.16: K-Mean Clustering Graph

Figure 3.17 shows the GUI design for Student Login. In this GUI design, student need

to login to the system by entering identification and password. Then, press the SIGN IN

button.

Figure 3.17: Student Login

25

Figure 3.18 shows the Student Dashboard. In this GUI design, it will display a

dashboard that has guidance for references to student in using this system.

Figure 3.18: Student Dashboard

26

CHAPTER 4

IMPLEMENTATION AND RESULT

4.1 Introduction

Implementation and result will discuss on how to construct the system as

specific design that had been explained in previous phase and executed to ensure

the system are developed according to the main objective and achieve user

requirement of the system.

4.2 Implementation and Output

The process of the system should be built and ensure that the system should be

operational and can be used well. However, implementation should ensure that the

system meet quality standard by doing test on it.

27

4.3 Design Interface

4.3.2 Main Interface

STUDENT GROUPING SYSTEM ON WEB-BASED PLATFORM

Figure 4.1: Main Interface

Figure 4.1 above show the main interface for the web-based platform. User need to

choose their type of login either as Admin Login, Student Login or Lecturer Login.

Figure 4.2: Admin Login

28

Figure 4.2 show the login interface for Student Grouping System. User need to fill in

their details such as User ID or password. If wrong, the system will be in the same

place.

Figure 4.3 : Admin Dashboard

Figure 4.3 it show the Admin Dashboard as a guide to the user to use the Student

Grouping System.

Figure 4.4 : Admin Profile Picture

29

Figure 4.4 show the interface of Admin Profile Picture and Admin Personal

Information. Hence, Admin can update their picture and personal information at

button Edit.

Figure 4.5 : Admin Register for Student

Figure 4.5 it shows the interface Admin to register for Student

Figure 4.6 : Admin Register for Lecturer

30

Figure 4.6 shows the interface Admin to register for Lecturer.

Figure 4.7 : Add Subject List (Admin)

Figure 4.7 above shows the add subject list that need to do by Admin to send at the

interface of Lecturer.

Figure 4.8 : Detail of Student (ADMIN)

31

Figure 4.8 it shows the detail of Student that have been done register by Admin.

Meanwhile, Admin can update and delete Student.

Figure 4.9 : Detail of Lecturer (ADMIN)

Figure 4.9 above shows the detail of Lecturer that have been done register by Admin.

Meanwhile, Admin can update and delete Lecturer.

Figure 4.10 : Lecturer Dashboard

32

Figure 4.10 shows the Lecturer Dashboard as a guide to the user to use the Student

Grouping System.

Figure 4.11 : Lecturer Profile Picture

Figure 4.11 it shows the Lecturer Profile Picture. Then, Lecturer can update their

profile picture.

Figure 4.12 : Manage Group List (Lecturer)

33

Figure 4.12 above shows the Manage Group List (Lecturer) that need to fill in all the

information in the provided column and the dropdown menu to select the code

subject1 and code subject2 as the criteria to make a group.

Figure 4.13 : Cluster Student

Figure 4.13 shows cluster student to get a group of one course.

Figure 4.14 : List Clustering Result

34

Figure 4.14 it shows add student to the group. Then, Lecturer need to add student

based on the best clustering result. After Lecturer click add button, student will

enter into the group.

Figure 4.15 : Graph for K-Mean Clustering

Figure 4.15 this graph shows with different colour to differentiate between each

cluster. After the curser are move to point, the information about x-axis (Subject

Score 1) and y-axis (Subject Score 2) and the label (Student ID) are displayed.

Figure 4.16 : Group List

35

Figure 4.16 above shows the group list that has been created by Lecturer.

Figure 4.17 : Recommend Group Details

Figure 4.17 shows the Recommend Group Details. In this interface, Lecturer

needs to scroll down to see the list of students included in the group course.

Figure 4.18 : Student Dashboard

36

Figure 4.18 it shows the Student Dashboard as a guide to the user to use the

Student Grouping System.

Figure 4.19 : Student Profile Picture

Figure 4.19 above shows the Student Profile Picture. Then, Student can update

their profile picture.

Figure 4.20 : Add Subject Score

37

Figure 4.20 shows the Add Subject Score. In this interface, Student need to add

the subject score.

Figure 4.21 : Update Subject Score

Figure 4.21 it shows the Update Subject Score. Additionally, if the student wants

to update the subject scores, they need click at button update.

Figure 4.22 : Group Details

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Figure 4.22 above shows the Group Details. Indirectly, the student can identify

the group name by selecting the combo box above.

4.4 Testing Analysis

Testing analysis conducted to evaluate the system’s compliance with its

specified requirements. This system will be test using two techniques of

software testing which are black box testing and white box testing. Test cases

are also used in this project.

4.4.2 Black Box Testing

The process that involved in this testing such as:

i. Login

ii. Create, Retrieve, Update, and Delete Student iii. Create, Retrieve, Update, and Delete Lecturer

iv. Create, Retrieve, Update, and Delete Subject List

v. Create, Retrieve, Update, and Delete Group List

vi. Create, Retrieve and Update Subject Score

vii. Create, Retrieve and Delete Student Grouping

4.4.3 White Box Testing

The process that involved in this testing such as:

i. Generate Student’s Cluster Result

ii. Generate Graph for K-Means Cluster

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4.5 Test Cases

A test case is a set of condition or variables which will be determine the system

had fulfilled the requirement or not. It is also a step that to check the system

either it works correct or not. Each of the process that had been tested will be

shown at below.

4.5.2 Login

Table 4-1: Test Cases Success Admin Sign In

Step Procedure Expected Result Pass/Fail

1. Go to login page Preview page loaded Pass

2. Enter the following

detail:

Admin ID:1

Password: a

Pass

3. Click “SIGN IN” Sign in successful and

display the Dashboard

Admin Page

Pass

Table 4-2: Test Cases Success Lecturer Sign In




detail:

Lecturer ID:L002

Password: abcde1234

Pass



Lecturer Page

Pass

Table 4-3: Test Cases Success Student Sign In




detail:

Student ID: 039674

Password: abcde1234

Pass

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Student Page

Pass

4.5.3 Admin

Table 4-4: Test Cases Add New Student


1. Click “Add User” and

then “Student”

Form to add student Pass


detail:

Student ID : 040471

Student Name :

Mohamad Shahrul

Hanif Bin Osman

Student Course : ISM

(PEMBANGUNAN

PERISIAN)

Pass

3. Click “Submit” Pass

Table 4-5: Test Cases Update Student


1. Click “Report” and

then “Student

Form to add student Pass

2. Click “Edit” Button Form with student detail Pass


detail:

Student ID : 040471

Student Name :

Mohamad Shahrul

Student Course : ISM

(PEMBANGUNAN

PERISIAN)

Pass


Table 4-6: Test Cases Add New Lecturer



then “Lecturer”

Form to add lecturer Pass

41


detail:

Lecturer ID : L001

Student Name :

EN. AHMAD

FAISAL

AMRI BIN ABIDIN

@ BHARUN

Pass


Table 4-7: Test Cases Update Lecturer



then “Lecturer”

List of lecturer record Pass

2. Click “Edit” Button Form with lecturer detail Pass


detail:

Lecturer ID : L001

Student Name :

EN. AHMAD

FAISAL

AMRI

Pass


Table 4-8: Test Cases Delete Student



then “Student”

List of student record Pass

2. Click “Remove” Icon

which student you

want to remove

Message preview student

successful delete

Pass

Table 4-9: Test Cases Delete Lecturer



then “Lecturer”

List of lecturer record Pass


which lecturer you

want to remove

Message preview lecturer

successful delete

Pass

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Table 4-10: Test Cases Add Subject List


1. Click “Manage List”

and then “Subject list”

Form to add subject list Pass


detail:

Subject Code :

CSN23403

Subject Name:

Komputer Forensik

Pass

3. Click “Submit” button Pass

4.5.4 Lecturer

Table 4-11: Test Cases Change Profile Picture lecturer


1. Click “Profile” View profile detail Pass

2. Click “Browse”

Button

Select picture from

document

Pass

3. Click “Save” Icon Message preview your

profile has been updated!

Pass

Table 4-12: Test Cases Add Subject List


1. Click “Choose

Subject” and then

“Subject List”

Form to manage group list Pass


detail:

Group ID : 1

Group Name:

SUTRA

Group Course :

JAVA1

Code Subject1:

CSB21303

Code Subject2 :

CSN23403

Pass


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Table 4-13: Test Cases Update Group List


1. Click “Choose

Subject” and then

“Subject List”

Form to manage group list Pass

2. Click “Edit” Icon Form with group list detail Pass


detail:

Group ID : 1

Group Name:

GEMILANG

Group Course :

JAVA1

Code Subject1:

CSB21303

Code Subject2 :

CSN23403

Pass


Table 4-14: Test Cases Delete Group List



then “Group List”

List of group list Pass


which group list you

want to delete

Message preview group list

successful delete

Pass

Table 4-15: Test Cases Generate Cluster


1. Click “Generate Cluster”

and then “Generate List”

Form to add cluster

Student

Pass

2. Choose combo box at

group course

Pass

3. Insert number of cluster

from min 2 to 4 max and

click “Submit” Button

Pass

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Table 4-16: Test Cases Cluster add Student using Group

Identification


1. Choose combo box

“Group Identification”

List of cluster student for

group course

Pass

2. Click add “Icon” that

which one student would

you like insert to group

Pass

Table 4-17: Test Cases Delete Student from Group


1. Click delete “Icon” that

which one student would

you like to remove from

group

Preview page loaded Pass

4.5.5 Student

Table 4-18: Test Cases Change Profile Picture Student


1. Click “Profile” View profile detail Pass

2. Click “Browse”

Button

Select picture from

document

Pass

3. Click “Save” Icon Message preview your

profile has been updated!

Pass

Table 4-19: Test Cases Manage Subject Score


1. Click “Manage

Subject Score” and

then “Student”

List of subjects that student

need to be entered subject

score

Pass


45

Table 4-20: Test Cases Update Subject Mark



then “Subject Score”

List of Subject score Pass

2. Click “Update” button Preview page loaded Pass

3. Enter the subject

marks that students

need to modify

Pass


Table 4-21: Test Cases View Group Details



then “Group Details”

List of Group details Pass

2. Choose combo box

“Group Identification”

Result of Group Pass

4.6 Summary

As a conclusion, to develop this project is not as easy as ABC. Besides that, I

learn many things from this project. I hope this project will benefit to university

especially in the group of assignments. In the development of this project, all

objectives have been achieved to ensure that the project works and goes

smoothly and perfectly. Last but not least, I hope this system can help students

to get the best team with their skills in knowledge.

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CHAPTER 5

CONCLUSION

5.1 Introduction

In this chapter, it will explain the conclusions that will be concluded about this

project. Apart from that will be explained in this chapter explaining what the

project contribution, limits the development of this project and what proposals

can be added to this project in the future.

5.2 Project Contribution

Student Grouping System have been developed for final year student in Faculty

of Informatics Computing in UniSZA, Campus Besut. It has achieved the

objectives and scope of this project. Below is the list of the achievements on this

project:

This system give Student a group that suited their skills in knowledge.

This system facilitates students in producing a set of assignments that

given by lecturers.

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5.3 Limitations

There are some limitations that occur throughout the development of these

projects. The limitations in carrying out this study are:

The cluster group can be changed because of the number of clusters

that have been entered by the user and will make the user unclear.

Subject Score need to update manual by Student.

5.4 Future Work

Here are some suggestions over time:

Combine several methods such as gravity search with k-means clustering for

better satisfaction to complete cluster data sets.

Add more functions to the system. For example, Students can print copies of

group results in case of undesirable events.

5.5 Conclusion

Most of the current research uses k-means in conducting their research work. However,

there is lacking of research that cluster student’s skill achievement based on overall

subject mark. Thus, my project will cover on skill achievement using K-Means

Algorithm.

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REFERENCES

1. Elizaphan, Robert & Peter. Extending Moodle grouping functionality using

artificial intelligent techniques. Retrieved February 03, 2018, from

http://ieeexplore.ieee.org/document/8095455/

2. Abdel-Hameed, Michelle. Evaluating discussion boards on Blackboard as a

collaborative learning tool. Retrieved February 03, 2018, from


3. Jonathan. How to Manage Groups. Retrieved February 04, 2018, from

https://www.docebo.com/knowledge-base/elearning-how-to-manage-and-

create-group/

4. P. Manivannan&P. Isakki. Dengue fever prediction using K-means clustering

algorithm. Retrieved March 01, 2018, from


5. Manish&Princy. A smoke detection algorithm based on K-means. Retrieved

March 03, 2018, from http://ieeexplore.ieee.org/document/7846590/

6. Hung-Chi,Chi-Kun. K-means algorithm for the road junction time period

analysis. Retrieved March 04, 2018, from


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APPENDIX

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APPENDIX A : GANTT CHART

student grouping system based on academic … · 2018. 8. 12. · kepada kumpulan tugas. dengan...

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