FURNITURE ONLINE ORDERING USING CHATBOT

INTAN NADIRAH BINTI AHMAD

BACHELOR OF COMPUTER SCIENCE

(SOFTWARE DEVELOPMENT) WITH HONOURS

UNIVERSITI SULTAN ZAINAL ABIDIN

2019

FURNITURE ONLINE ORDERING USING CHATBOT

INTAN NADIRAH BINTI AHMAD

BACHELOR OF COMPUTER SCIENCE

(SOFTWARE DEVELOPMENT) WITH HONOURS

FACULTY OF INFORMATICS AND COMPUTING

UNIVERSITI SULTAN ZAINAL ABIDIN

2019

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 : Intan Nadirah Binti Ahmad

Date : 7 May 2019

ii

CONFIRMATION

This is to confirm that this final year project entitled Furniture Online Ordering using

Chatbot has been prepared and submitted by Intan Nadirah Binti Ahmad , with matric

number BTAL17046608 and has found satisfactory in terms of scope, quality, and

presentation as a part of the requirement for the Bachelor of Computer Science in

Software Development in University Of Sultan Zainal Abidin (UniSZA). The research

conducted and the writing of this report was under my supervison.

Name : Prof. Madya Dr. Engku Fadzli

Hasan Bin Syed Abdullah

Date : 7 May 2019

iii

DEDICATION

I am using this opportunity to express my gratitude to everyone who has supported me

to complete my final year project Furniture Online Ordering using Chatbot

successfully. I am thankful for their aspiring guidance, invaluably constructive criticism

and friendly advice during this project work.

I express my greatest gratitude to my supervisor, Prof. Madya Dr. Engku Fadzli Hasan

Bin Syed Abdullah, who helps in guiding me throughout my journey in finishing this

project. Under his supervision with a lot of advices, I was able to complete this final

year project successfully. Then, a honorable respect I present to my family especially

my parents for their understanding with my conditions.

I would also love to thanks all my friends and my course mates for supporting me and

gave me an aspiration to improve this project. I would like to thank all the people for

their help whether it was directly or indirectly to complete this project.

iv

ABSTRACT

Furniture Online Ordering using Chatbot was developed to helps entrepreneurs to get

closer to customers. This chatbot may be able will help the seller to promote their

products, engage customers and boost revenue. Then, it also can assist the customer

throughout the purchase process to make purchases more easily. It is a demand

nowadays, where it can save time and efforts by automating customer support. The

RAD method is used in this project to ensures that the development of the system runs

smoothly and according to planning. This model have four main phases, which are

Analysis and Quick Design, Prototype Cycles, Testing and Deployment. The expected

result for the following proposed project is the user can choose any answer that has been

provided by the chatbot system and make a booking. Then the user will be able to

communicate with the chatbot anytime.

v

ABSTRAK

Pesanan perabot secara atas talian menggunakan Chatbot telah dibangunkan untuk

membantu para usahawan untuk lebih dekat dengan para pelanggan. Chatbot ini dapat

membantu penjual mempromosikan produk mereka, melibatkan pelanggan dan

meningkatkan pendapatan. Kemudian, ia juga dapat membantu pelanggan sepanjang

proses pembelian untuk membuat pembelian lebih mudah. Ini adalah permintaan pada

masa kini, di mana ia dapat menjimatkan masa dan usaha dengan mengautomasikan

sokongan pelanggan. Kaedah RAD digunakan dalam projek ini untuk memastikan

pembangunan sistem berjalan lancar dan mengikut perancangan. Model ini

mempunyai empat fasa utama, iaitu Analisis dan Rancang Pantas, Siklus Prototaip,

Pengujian dan Penyebaran. Hasil yang diharapkan untuk projek yang dicadangkan

berikut adalah pengguna dapat memilih apa jawapan yang telah disediakan oleh sistem

chatbot dan melakukan pemesanan. Kemudian pengguna akan dapat berkomunikasi

dengan chatbot pada bila-bila masa.

vi

CONTENTS

PAGE

DECLARATION i

CONFIRMATION ii

DEDICATION iii

ABSTRACT iv

ABSTRAK v

CONTENTS vi

LIST OF TABLE viii

LIST OF FIGURES ix

LIST OF ABBREVIATION x

CHAPTER I INTRODUCTION

1.1 Introduction 1

1.2 Problement Statement 2

1.3 Objectives 2

1.4 Scopes 3

1.5 Limitation of work 4

1.6 Expected Result 4

1.7 Activities and Milestones (Gantt Chart) 5

CHAPTER II LITERATURE REVIEW

2.1 Introduction for Chatbot 6

2.2 Introduction for Telegram 7

2.3 Related Research Technique and Tools 8

CHAPTER III METHODOLOGY

3.1 Introduction 11

3.2 Project Methodology 12

3.2.1 Phases in Rapid application development model 13

3.2.1.1 Analysis and Quick Design Phase 13

3.2.1.2 Prototypes Cycles Phase 13

3.2.1.3 Testing Phase 14

3.2.1.4 Deployment Phase 14

3.3 System Requirement 15

3.4 System Design 16

vii

3.4.1 Framework Design 17

3.4.2 Context Diagram (CD) 17

3.4.3 Data Flow Diagram (DFD) 18

3.4.3.1 DFD Level 0 18

3.4.3.2 DFD Level 1 19

3.4.4 Entity Relationship Diagram (ERD) 21

3.5 Data Dictionary

22

CHAPTER IV CONCLUSION 25

REFERENCES 26

viii

LIST OF TABLES

TABLE TITTLE

PAGE

1.1 Activity and milestone for FYP I 5

3.1 Hardware used 15

3.2 Software Used 15

ix

LIST OF FIGURES

FIGURE TITLE PAGE

2.1 Chatbot Operations 7

3.1 Rapid Application Development Model 12

3.2 Framework for Furniture Online Ordering using Chatbot

System

17

3.3 Context Diagram for Furniture Online Ordering using

Chatbot System

18

3.4 Data Flow Diagram Level 0 for Furniture Online Ordering

using Chatbot System

19

3.5 Data Flow Diagram Level 1 for Booking process 19

3.6 Data Flow Diagram Level 1 for Payment process 20

3.7 Entity Relationship Diagram for of Furniture Online

Ordering using Chatbot System

21

3.8 Data Dictionary for Customer 22

3.9 Data Dictionary for Booking 22

3.10 Data Dictionary for Sofa 23

3.11 Data Dictionary for Diningtable 23

3.12 Data Dictionary for Payment 24

x

LIST OF ABBREVIATIONS/TERMS/SYMBOLS

CD Context Diagram

DFD Data Flow Diagram

ERD Entity Relationship Diagram

FYP Final Year Project

RAD Rapid Application Development

1

CHAPTER I

INTRODUCTION

1.1 Introduction

A Chatbot is an assistant that communicates with the user through text messages, a

virtual companion that integrates into websites, applications or instant messengers and

helps entrepreneurs to get closer to customers. Such a bot is an automated system of

communication with users. It is a demand nowadays, where it can save time and efforts

by automating customer support. In this case, the chatbot for Furniture Online Ordering

using Chatbot is proposed to enhance the relationship with the customer. This chatbot

will help the seller to promote their products, engage customers and boost revenue.

Then, it also can assist the customer throughout the purchase process to make purchases

more easily

2

1.2 Problem Statement

Customer relationships are the most important of today, problems arise when

sellers take a long time to provide feedback to customer inquiries. Customers have to

wait a long time for the response that will affect their purchase decision. Moreover,

there is no faster platform for customers to communicate with the seller except with the

message. They need to have a company number to do so. Finally, customers are more

comfortable chatting online which makes them willing to type order and talk to a boat.

This is because with fast feedback it can facilitate and expedite their dealings.

1.3 Objectives

In general, the purpose of Furniture Online Ordering using Chatbot System is to

help customer to make purchasing easier and faster. Besides, it also assist the customer

to know the details about the furniture. The objectives are defined as below to design a

chatbot that contains information about furniture. Next, to develop a chatbot that

answers quickly and efficiently for some question from the customer. Finally, to test the

functionalities of the proposed system either it able to meet the requirement.

3

1.4 Scopes

The scope is going to outline the users and functions of this application system

and makes the implementation easier. The scope of this system is divided into two

which are user scope and system scope.

1.4.1 User Scope

• The system shall allow the user to start the conversation.

• The system shall allow the user to choose the answer option from the system.

• The system shall allow the user to view the information about the furniture.

• The system shall allow the user to make a booking.

• The system shall allow the user to cancel the product before making a

payment.

1.4.2 System Scope

• The system shall be able to respond to the questions that choose.

• The system shall be able to show the information about the furniture.

• The system shall be able to accept the booking from the user.

• The system shall be able to update the stock in the database.

4

1.5 Limitation of Work

There are limitations and constraint that occurred throughout the development of

this online chatbot. These problems and limitations in conducting this study are the

chatbots has a very limited dictionary where it is only able to understand a certain

question that has been asked by the user.

1.6 Expected Result

The expected result for the following proposed project is the user can choose

any answer that has been provided by the chatbot system and make a booking. Then the

user will be able to communicate with the chatbot anytime.

5

1.7 Activity and Milestone

Table 1.1 below show activity and duration time to complete the documentation for

Final Year Project 1 :

Table 1.1 : Activity and milestone for FYP 1

6

CHAPTER II

LITERATURE REVIEW

2.1 Introduction for Chatbot

A chatbot is artificial intelligence (AI) software. It was the most advanced and

promising expressions of interaction between humans and machines that only

represents the natural evolution of a Question Answering system leveraging Natural

Language Processing (NLP). Then, it has been used to simulate a conversation or a chat

with a user in natural language. Chatbot often used through messaging applications,

websites, mobile apps or through the telephone. The chatbot is very important

applications that can streamline interactions between people and services that enhance

the customer experience. It also offers new opportunities to many companies to

improve customers engagement process and operational efficiency that can reduce the

typical cost of customer service.

7

The picture below illustrates how a chatbot operates and responds to the

question. First, the chatbot will accepts the input from a user. Then, it analyzes the

user’s request to identify the user intent and to extract relevant entities. When the user’s

intent has been identified, the chatbot composes an answer and reply for the user’s

request.

Figure 2.1 Chatbot Operations

2. 2 Introduction for Telegram

Telegram is a cloud-based mobile and desktop messaging app with a focus on

security and speed. It use end-to-end encryption by mean that all data sent and

received via Telegram cannot be deciphered when intercepted by ISPs, network

administrators or other third parties. Telegram is for everyone who wants fast and

reliable messaging and calls. Telegram let user to send messages, photos, videos and

files of any type and create groups for up to 200,000 people or channels for

broadcasting to unlimited audiences. Business users and small teams may like the large

groups, usernames, desktop apps and powerful file sharing options.

Analyze

request

Identify intent

and entities

User

input

Compose

reply

8

2. 3 Related Research Technique and Tools

The first related works is form article [1] . ALICE Chatbot is the Artificial

Linguistic Internet Computer Entity that came from Wallace in 1995. In the ALICE

architecture, alternative language knowledge models can be plugged and played

because the chatbot engine and the language knowledge model are separated. The

advantages of ALICE are its deliberate simplicity of the pattern-matching algorithms

and rely on a very large number of basic categories or rules matching input patterns to

output templates. Then, ALICE also more advanced because of the number of simple

rules is large to make up for lack of morphological, syntactic, and semantic NLP

(programming language) modules. However, the main lack in ALICE is it needs to

develop the knowledge manually.

Second article [2] is about Chappie a semi-automatic intelligent chatbot that was

developed to meet the requirement from a business side and desire for efficiency and

automation. Chappie can be used as a routing agent that can classify the requirement of

the user. It changes it into one of the services provided by business through the first few

chats than transfer it to an agent expert in that service. The good thing about Chappie is

it has been designed smartly to extracts all sorts of information such as name, intent,

mail, city, et cetera and generates a coherent response to the user, but it needs more

sophisticated algorithms to extract intent and classify chats more accurately.

9

Third article [3] is DocChat, a novel information retrieval approach for chatbot

engines. It was used to leverage unstructured documents, instead of Q-R pairs to

respond to speech. The features of this models is depending on existing resources are

readily available such as Q-Q pairs, Q-A pairs, ‘sentence-next sentence’ pairs, and et

cetera, instead of requiring manually. Second, it was very good in adaptation capability

where it can learn internal relationships between speech and responses based on

statistical models. Regardless all of that, collecting such QR pairs is intractable for

many specific domains.

AliMe Chat [4] is the next article about an open-domain chatbot engine that

integrates the joint results for Information Retrieval (IR) and Sequence to Sequence

(Seq2Seq) based on generation models. To optimize the joint results of IR and

generation models, it use a novel hybrid approach. Then, it shows that the approach

outperforms both in IR and generation. The engine show a better performance

afterwards. AliMe Chat is used for a real-world industrial application. Unfortunately,

Seq2Seq generation models tend to generate inconsistent or meaningless answers and

do not scale up well according to context-aware techniques in some situation. Lastly,

AliMe Chat is lack of personification like empowering the chatbot with characters and

emotions.

10

Next article is MILABOT, a deep reinforcement learning chatbot [5] that was

developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon

Alexa Prize competition. It was well-known for its ability that capable of conversing

with humans. Then, the system consists of natural language generation and retrieval

models, including neural network and template-based models. MILABOT also has been

trained to only choose an appropriate response with reference to the models in its group.

Through the testing that has been performed on the system that involves a real-world

user, it shows that the system performed significantly better than other systems.

Last but not least is Pandaibot, a boat for Telegram that builds as a side project

to provide convenience to all in some cases. Functions that available in Pandaibot are

animated pictures and pictures, prayer times in Malaysia, and also functions to check

postage delivery status. Among the advantages of Pandaibot is that users can check a

delivery status from PosLaju, SkyNet, City Link and GD Express via Telegram without

leaving the messaging app. In addition, users can also check the time of the prayer, use

the dictionary and read the news from several portals and also check the weather.

PandaiBot also allows users to search for images and GIFs directly via Telegram.

However, Pandaibot has a very limited dictionary where it only allow the user to choose

the answer option that has been provided.

11

CHAPTER III

METHODOLOGY

3.1 Introduction

This chapter will discuss about the methodology that used to develop this

system. Therefore, the rapid application development model is used for Furniture

Online Ordering using Chatbot System. This model explains more detail about every

phase that involve in this project development to make sure this system developed

successfully. Furthermore, this chapter also explains justification for the use of methods

and technique as well as hardware and software requirement during this project.

Besides, this chapter contains Context Diagram (CD), Data Flow Diagram (DFD) and

Entity Relationship Diagram (ERD) for the this project.

12

3.2 Project Methodology

Methodology is a method used to develop a system. A good planning and

methodology must be used to accomplish the objectives of this project. Rapid

application development model has been chosen as the methodology to develop this

system. The benefits of rapid application development are changing requirements can

be accommodated and progress can be measured. Besides, iteration time can be short

with use of powerful RAD tools. The flexibility of the model make the development

time reduced and increases reusability of components. Hence, Rapid application

development model is encourages customer feedback. Figure 3.1 below shows the

Rapid application development model. This model have four main phases, which are

Analysis and Quick Design, Prototype Cycles, Testing and Deployment.

Figure 3.1 : Rapid Application Development Model

13

3.2.1 Phases in Rapid application development model

3.2.1.1 Analysis and Quick Design Phase

During this phase, all the modules will be design based on Furniture

Online Ordering using Chatbot system. The analysis and quick design phase is

the most important phase to make sure the module development is properly

planned. In the analysis phase, the detailed about this proposed system was

discussed. Problem statements, objectives, system’s scope and limitation of

work were defined as well. The quick design of the Furniture Online Ordering

using Chatbot System was conducted by reviewing the journal.

3.2.1.2 Prototypes Cycles Phase

This phase is to ensure that the project met every planning that has been

discussed in Analysis & Quick Design Phase. A prototype design for module

customer, view and check furniture information, booking and payment will be

developed. Each module will go through the developed, demonstrate and refine

phase before proceed to the testing phase.

14

3.2.1.3 Testing Phase

In this testing phase prototypes for each module in Furniture Online

Ordering using Chatbot systems are convert from the design phase into the

working model. The phase breaks down into several smaller steps like

preparation for rapid deployment, program and application development,

coding, unit, integration, and system testing.

3.2.1.4 Deployment Phase

This is the implementation phase. The finished product of Furniture

Online Ordering using Chatbot goes to launch. It includes data conversion,

testing, and changeover to the new system, as well as user training. The

objectives of this stage are to install the system in production operation with

minimal disruption of normal business activity, to maximize the effectiveness of

the system in supporting the intended business activities and to identify

potential future enhancement.

15

3.3 System Requirement

The requirement of hardware and software are the most important part of some

project because it will guide to the successful project. The hardware and software

requirements used in this project are shown in Table 3.1 and 3.2 below :

Table 3.1: Hardware used in this project

No Hardware Specification

1 Laptop Processor : AMD A-10-8700P Radeon R6, 10 Compute Cores 4C+6G 1.80GHz

Installed memory (RAM) : 4.00GB

2 Mouse For make easy task and faster to click when developing project.

3 Pendrive To store the backup file

Storage : 32GB

4 Printer Canon MP287 To print out the documentation

To print picture dictionary

Table 3.2 : Software used in this project

No Software Specification

1 Xampp To create a table and database

2 MySQL To save the data in database.

3 Notepad++ To make a coding for the system

4 Microsoft Word 2010 To make a proposal and thesis

5 Edraw Max To create Context Diagram(CD), Entity Relationship Diagram(ERD) and Data Flow Diagram(DFD)

16

3.4 System Design

Developing a planned system is a process and planning activity in designing the

system. This design was made to improve the existing system after its weakness was

identified through the results of the investigation and previous analysis. In Furniture

Online Ordering using Chatbot System , theContext Diagram (CD) and Data Flow

Diagram (DFD) act as physical design while Entities Relationship Diagram (ERD) act

as logical design.

3.4.1 Framework Design

A framework is a real or conceptual structure that use to serve as a guide for

expands the structure into something useful. A framework may be for a set of

functions within a system and how it interrelate between the layers of an

operating system, the layers of an application subsystem and how

communication should be standardized at some level of a network. The

framework shown in Figure 3.2 below shows how the system responds to the

user question. First, the chatbot will accepts the input from a user. Then, it

analyzes the user’s request to identify the user intent and to extract relevant

entities. When the user’s intent has been identified, the chatbot composes an

answer and reply for the user’s request.

.

17

Figure 3.2: Framework for Furniture Online Ordering using Chatbot

System

3.4.2 Context Diagram

The Context diagram is a Data Flow Chart which shows the scope

and boundaries of an information system. It usually shows the entities

and processes involved in the system to be redesigned which illustrates

how moving from one process to another. It is the first figure drawn

before the Data Flow Chart.

In Furniture Online Ordering using Chatbot System, it can be

identified how this system works. Among the major entities in the

system are customers. The context diagram in Figure 3.2 below shows

customers input their information as requested, customer input the

details of the furniture they want to see, then make a booking and

payment.

18

Figure 3.3: Context Diagram for Furniture Online Ordering using

Chatbot System

3.4.3 Data Flow Diagram

3.4.3.1 DFD Level 0

The Data Flow Chart is an overview of the overall system trip

diagram in detail and overall. It helps user to understand the flow of the

system that built. The first process is the customer authentication

process into the system. After conducting the validation process,

customer will go through the second process to view furniture

information. Then the customer will input their information into the

system which is the third process. Then the customer made the fourth

process of booking the furniture and then the customer made the

payment the fifth process.

19

Figure 3.4 : Data Flow Diagram Level 0 for Furniture Online Ordering

using Chatbot System

3.4.3.2 DFD Level 1

3.4.3.2.1 Booking

The figure 3.5 shows the DFD Level 1 for booking process. The

customer can make a booking and will be stored in booking

database.

Figure 3.5 : Data Flow Diagram Level 1 for Booking process

20

3.4.3.2.2 Payment

The figure 3.5 shows the DFD Level 1 for payment process. The

customer can make a payment and will be stored in payment

database.

Figure 3.6 : Data Flow Diagram Level 1 for Payment process

21

3.4.4 Entity Relationship Diagram

The Entity Relationship Diagram (ERD) is a graphical representation of an

information system that shows the relationship between people, objects, places,

concepts or event within the system. The ERD of Furniture Online Ordering

using Chatbot System project consist of four table entities such as Customer,

Booking, Furniture and Payment. The ERD of Furniture Online Ordering using

Chatbot System shown as the Figure 3.7 below

Figure 3.7 : Entity Relationship Diagram for of Furniture Online Ordering using

Chatbot System

22

3.5 Data Dictionary

The database in the main part of the system development because it will store

the data that are used by the system. The list of tables involved in this system are list as

the figure 3.8 until 3.12 below :

Figure 3.8 below is a Customer table. This table has 5 fields. The field is customer_id,

name, ic_num, address and phone_num. In this table customer_id is the primary key

(PK).

Figure 3.8 : Data Dictionary for Customer

Figure 3.9 below is a Booking table. This table has 7 fields. The field is booking_id,

customer_id, furniture_id, name, price, date and status. In this table booking_id is the

primary key (PK).

Figure 3.9 : Data Dictionary for Booking

23

Figure 3.10 below is a sofa table. This table has 4 fields. The field is fileToUpload,

id_sofa name and price. In this table id_sofa is the primary key (PK).

Figure 3.10 : Data Dictionary for Sofa

Figure 3.11 below is a Diningtable table. This table has 4 fields . The field is

fileToUpload, id_diningtable, name and price. In this table id_diningtable is the

primary key (PK).

Figure 3.11 : Data Dictionary for Diningtable

24

Figure 3.12 below is a Payment table. This table has 5 fields. The field is payment_id,

fileToUpload, customer_id, total and date. In this table payment_id is the primary key

(PK).

Figure 3.12 : Data Dictionary for Payment

25

CHAPTER IV

CONCLUSION

Furniture Online Ordering Using Chatbot is a system which focuses on helping

entrepreneurs to get closer to customers and assist the customer throughout the

purchase process to make purchases more easily. Based on previous studies and

discussion, a chatbot is proposed to be implemented for Furniture Online Ordering.

This chatbot is an assistant that communicates with the user through text messages. This

is very important things that will help the seller to enhance their customer relationship.

Hopefully, this system can help the seller to overcome their problem and make the

business more successful.

26

REFERENCES

[1] AbuShawar, B., & Atwell, E. (2015). ALICE chatbot: Trials and outputs.

Computación y Sistemas, 19(4), 625-632.

[2] Behera, B. (2016). Chappie-a semi-automatic intelligent chatbot. Write-Up.

[3] Yan, Z., Duan, N., Bao, J., Chen, P., Zhou, M., Li, Z., & Zhou, J. (2016).

Docchat: An information retrieval approach for chatbot engines using unstructured

documents. In Proceedings of the 54th Annual Meeting of the Association for

Computational Linguistics (Volume 1: Long Papers) (Vol. 1, pp. 516-525).

[4] Qiu, M., Li, F. L., Wang, S., Gao, X., Chen, Y., Zhao, W., ... & Chu, W. (2017,

July). Alime chat: A sequence to sequence and rerank based chatbot engine. In

Proceedings of the 55th Annual Meeting of the Association for Computational

Linguistics (Volume 2: Short Papers) (pp. 498-503).

[5] Serban, I. V., Sankar, C., Germain, M., Zhang, S., Lin, Z., Subramanian, S., ...

& Rajeswar, S. (2018). A deep reinforcement learning chatbot (Short Version).

arXiv preprint arXiv:1801.06700.