Determination of Breakfast Menu Patterns on the Fast Food

Restaurant Using Apriori Algorithm

Anggi Yhurinda Perdana Putri

, Dzulfiqar Nawa Fauzan

, Hana’ Nabilah

, Ruli Utami

and Suryo Atmojo

Department of Information System, Institute Technology of Adhi Tama Surabaya, Surabaya, Indonesia

Department of Informtics Engineering, University of Wijaya Putra, Surabaya, Indonesia

Keywords: Fast-Food, Fast-Food Restaurant, Apriori, Association Rule

Abstract: Nowadays the workload is increasing and inversely proportional to idle time, everyone is in rush especially

in the morning. Whereas breakfast is very important to start the day. Fast food restaurant offers a solution to

this problem. Fast food defined as cuisine prepared by restaurant in quick time and ready to consume. Fast

food restaurants have growing rapidly in Indonesia. The number of similar restaurants resulted very tight

competition, under these conditions fast food restaurant entrepreneurs must consider various ways to survive

in competition. Researchers offer collaboration between marketing strategy and computational knowledge for

business. One of the computational sciences that can be applied is Apriori algorithm to combine relationships

between products from the Fast-Food restaurant transaction data. This algorithm resulted 27 association rules

with 12 rules has met minimum confidence requirement. In this research minimum support and confidence

for each ≤ 2 and ≤ 75%.

1 INTRODUCTION

Fast food defined as cuisine prepared by restaurant in

quick time and ready to consume, such as fried chicken,

hamburger, or pizza (Nagarajan et al, 2017) (Smith et al,

2013). Nowadays the workload is increasing and

inversely proportional to free time, everyone is in rush

especially in the morning. Thus, time kept aside to have

breakfast is very limited or sometimes they even skip it

(Mardiyati & Nurul, 2017). Whereas breakfast is very

important to start the day. Fast food restaurant offers a

solution to this problem. Fast Food Restaurant is a type

of restaurant that serve fast food cuisine, has minimal

table service, offer cheap yet delicious food often lacks

much nutritional value (Anwar, 2017). Fast food

restaurants become the most popular place for dining,

among the public especially at breakfast. It helps rush

worker can grab a quick bite from any of the fast-food

restaurants around his business, within the span of 10-15

minutes.

Fast food restaurants have growing rapidly in

Indonesia, particularly in terms of variation of menu,

taste, restaurant facilities, and services. The number of

similar restaurants resulted very tight competition, this

competition does not only occur between restaurants,

but also with other forms such as outlets. Under these

conditions fast food restaurant entrepreneurs must

consider various ways to survive in competition.

Choosing the right marketing strategy can be determined

long term success and competitive advantage of

restaurant (Tampubolon et al, 2013). Strategy selection

and implementation in this marketing is expected to be

more helped if collaborated with the application of

knowledge computing for business (Utami, 2019)

(Marpaung, 2016). Data mining tools predict future

trends and behaviors, helps organizations to make

proactive knowledge-driven decisions. Data mining

tools has the answer of this question (Dongre et al,

2014). One of the data mining method that can be

applied is Apriori algorithm to combine relationships

between products from the Fast-Food restaurant

transaction data. This data mining association technique

will assist management to find relationships between

items in one transaction (Prakoso et al, 2017)

. Research

related to the Apriori method conducted by (Putra et al,

2019) (Kurnia et al, 2019) (Panjaitan et al, 2019)

(Ndruru and Hasugian, 2020) and many more, the

application of the Apriori Method in Fast Food

restaurant has not much explored yet.

2 RESEARCH METHOD

Apriori Algorithm serves to identify the relationship

Putri, A., Fauzan, D., Nabilah, H., Utami, R. and Atmojo, S.

Determination of Breakfast Menu Patterns on the Fast Food Restaurant Using Apriori Algorithm.

DOI: 10.5220/0012109200003680

In Proceedings of the 4th International Conference on Advanced Engineering and Technology (ICATECH 2023), pages 76-81

ISBN: 978-989-758-663-7; ISSN: 2975-948X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

between items in a sales transaction by finding the

highest frequency in an iteration and between item set in

a transaction data set, where the minimum requirements

are support and confidence predetermined. The equation

used in the apriori algorithm is as follows (Kusrini &

Luthfi, 2009) (Ruswati et al, 2018).

𝑆𝑢𝑝𝑝𝑜𝑟𝑡



𝑋









𝑥 100% (1)

𝑆𝑢𝑝𝑝𝑜𝑟𝑡



𝑋, 𝑌









𝑥 100% (2)

𝐶𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 





𝑥 100% (3)

Where:

Support (X): Support value for item X

Tx: Transaction Amount contains item X

Q: Transaction Amount

Support (X,Y) : Support values for item X and item Y

Txy: Transaction Amount contains item X and item Y

Confidence: confidence value of X → Y rule

Figur 1 shows the process stages Figure 1 shows the

process stages in the Apriori algorithm after going

through the process of data collection and data analysis.

Then, we determine the minimum support and

confidence.

2.1 Item Set Candidate

The k-item set candidate is formed from a combination

(k-1)-item set obtained from the previous iteration. One

feature of the Apriori algorithm is the elimination of k-

item set candidates whose subsets containing k-1 items

are not included in the high frequency pattern with the

length of k-1.

2.2 Support Value Calculation

Support Value for each k-item set candidate is obtained

by scanning the database to calculate the number of

transactions containing all items in the k-item set

candidate. It is also a feature of the a priori algorithm,

which is the calculation required by scanning the entire

data base of the longest k-item set.

2.3 High Frequency

High frequency patterns containing k-items or k-item

sets are determined from candidate k-item set whose

support is greater than the minimum support. Then

calculated confidence in each item combination.

Iteration stops when all items have been counted until

there are no more item combinations.

2.4 Association Rule

Association Rule provides rules in the form of ‘X → Y’,

where X and Y are sets of items. X and Y can be

regarded as the “If” part and the “Then” part respectively

which means the causality of X and Y.

Figure 1: Apriori Method Process Stages

3 RESEARCH AND DISCUSSION

The study begins with the collection of ABC Fast Food

Restaurant daily data shown in table 1 and then

processed using the Apriori method.

Table 1: Transaction List

Transaction

Purchased Menu

T1 Riser, Scramble Egg, Waffle, Hot

Coffee, Hot Tea, Hazelnut Coffee

T2 Super Breakfast, Scramble Egg,

Waffle, Hot Coffee, Hazelnut

Coffee

T3 Riser, Super Break 1, Hot Coffee,

Hot Tea

T4 Riser, Pancake, Pom-pom, Hot

Coffee, Hazelnut Coffee

T5 Pom-pom, Porridge, Scramble Egg,

Hot Coffee, Hot Tea

The apriori method is shown in Table 2 to Table 11.

In this study, the minimum support = 2 and minimum

confidence = 75% are determined. Table 2 is iteration 1

to get the menu patterns that most often appear in

consumer transactions.

Item Set Candidate

Support Value Calculation

High Frequency

Association Rule

Determination of Breakfast Menu Patterns on the Fast Food Restaurant Using Apriori Algorithm

Table 2: Iteration 1

Item

Support

Count

Support

Riser 3 60%

Scramble Egg 3 60%

Waffle 2 40%

Hot Coffee 5 100%

Hot Tea 4 40%

Hazelnut Coffee 3 60%

Super Break 1 20%

Super Break 1 1 20%

Pancake 1 20%

Pom-pom 2 40%

Porridge 1 20%

Table 3: Result of iteration 1

Item

Support

Count

Support

Riser 3 60%

Scramble Egg 3 60%

Waffle 2 40%

Hot Coffee 5 100%

Hot Tea 4 40%

Hazelnut Coffee 3 60%

Pom-pom 2 40%

From the Table 2 above, we found that 1-itemset

meets the minimum support is an item with a support

value ≥ 2 (the value in bold), there are seven items that

are qualify (shown at Table 3). After obtaining the item

with the highest frequent pattern, then calculated

iteration 2; i.e. looking for the relationship of two items

in a concurrent transaction. This is done by combining 2

items that meet the requirements in iteration 1.

At the iteration 2 we found that 2-itemset

meets the minimum support is an item with a support

value ≥ 2 (the value in bold), there are thirteen items that

are qualify (shown at Table 5). After obtaining the item

with the highest frequent pattern, then calculated

iteration 3; i.e. looking for the relationship of three items

in a concurrent transaction. This is done by combining 3

items that meet the requirements in iteration 2.

Continuously at the iteration 3 we found that 3-itemset

meets the minimum support is an item with a support

value ≥ 2 (the value in bold), there are seven items that

are qualify (shown at Table 7).

Then calculated iteration 4; i.e. looking for the

relationship of four items in a concurrent transaction.

This is done by combining 4 items that meet the

requirements in iteration 3. At the iteration 4 we found

that 4-itemset meets the minimum support is an item

with a support value ≥ 2 (the value in bold), there are

three items that are qualify (shown at Table 9).

Table 4: Iteration 2

Item

Support

Count

Support

Riser, Scramble Egg 1 20%

Riser, Waffle 1 20%

Riser, Hot Coffee 3 60%

Riser, Hot Tea 3 60%

Riser, Hazelnut Coffee 1 20%

Riser, Pom-pom 1 20%

Scramble Egg, Waffle 2 40%

Scramble Egg, Hot Coffee 3 60%

Scramble Egg, Hot Tea 3 60%

Scramble Egg, Hazelnut

Coffee

2 40%

Scramble Egg, Pom-pom 1 20%

Waffle, Hot Coffee 2 40%

Waffle, Hot Tea 2 40%

Waffle, Hazelnut Coffee 2 40%

Waffle, Pom-pom 0 0%

Hot Coffee, Hot Tea 4 80%

Hot Coffee, Hazelnut

Coffee

3 60%

Hot Coffee, Pom-pom 2 40%

Hot Tea, Hazelnut Coffee 2 40%

Hot Tea, Pom-pom 1 20%

Hazelnut Coffee, Pom-pom 2 20%

Table 5: The Result of iteration 2

Item

Support

Count

Support

Riser, Hot Coffee 3 60%

Riser, Hot Tea 3 60%

Scramble Egg, Waffle 2 40%

Scramble Egg, Hot Coffee 3 60%

Scramble Egg, Hot Tea 3 60%

Scramble Egg, Hazelnut

Coffee

2 40%

Waffle, Hot Coffee 2 40%

Waffle, Hot Tea 2 40%

Waffle, Hazelnut Coffee 2 40%

Hot Coffee, Hot Tea 4 80%

Hot Coffee, Hazelnut

Coffee

3 60%

Hot Coffee, Pom-pom 2 40%

Hot Tea, Hazelnut Coffee 2 40%

Table 6: Iteration 3

Item

Support

Count

Support

Riser, Hot Coffee, Hot Tea 2 40%

Riser, Hot Coffee, Scramble Egg 1 20%

Riser, Hot Coffee, Waffle 1 20%

ICATECH 2023 - International Conference on Advanced Engineering and Technology

Riser, Hot Coffee, Hazelnut

Coffee

2 40%

Riser, Hot Coffee, Pom-pom 1 20%

Riser, Hot Tea, Scramble Egg 1 20%

Riser, Hot Tea, Waffle 1 20%

Riser, Hot Tea, Hazelnut Coffee 1 20%

Riser, Hot Tea, Pom-pom 0 0%

Riser, Scramble Egg, Waffle 1 20%

Riser, Scramble Egg, Hazelnut

Coffee

1 20%

Riser, Scramble Egg, Pom-pom 0 0%

Riser, Waffle, Hazelnut Coffee 1 20%

Riser, Waffle, Pom-pom 0 0%

Riser, Hazelnut Coffee, Pom-

pom

1 20%

Hot Coffee, Hot Tea, Scramble

Egg

3 60%

Hot Coffee, Hot Tea, Waffle 2 40%

Hot Coffee, Hot Tea, Hazelnut

Coffee

2 40%

Hot Coffee, Hot Tea, Pom-pom 1 20%

Hot Tea, Scramble Egg, Waffle 2 40%

Hot Tea, Scramble Egg,

Hazelnut Coffee

2 40%

Hot Tea, Scramble Egg, Pom-

pom

1 20%

Scramble Egg, Waffle, Hazelnut

Coffee

1 20%

Scramble Egg, Waffle, Pom-

pom

0 0%

Waffle, Hazelnut Coffee, Pom-

pom

0 0%

Table 7: The Result of iteration 3

Item

Support

Count

Support

Riser, Hot Coffee, Hot Tea 2 40%

Riser, Hot Coffee, Hazelnut

Coffee

2 40%

Hot Coffee, Hot Tea,

Scramble Egg

3 60%

Hot Coffee, Hot Tea,

Waffle

2 40%

Hot Coffee, Hot Tea,

Hazelnut Coffee

2 40%

Hot Tea, Scramble Egg,

Waffle

2 40%

Hot Tea, Scramble Egg,

Hazelnut Coffee

2 40%

At the iteration 5, we found the last combination of

five item that meets the minimum support, which is Hot

Coffee, Hot Tea, Hazelnut Coffee, Scramble Egg, and

Waffle (Shown at table 10). Thus, the iteration process

has stopped at fifth iteration. To determine the

association rules in table 11 the results are used iteration

5, using equation 3 to calculate the value of confidence.

In this case It has been determined that the minimum

confidence value in the X → Y association rule is 40%.

Where the value of Support {X Ս Y} is a value that

indicates the level of product possibility X and Y are

bought simultaneously, while the confidence value

indicates the level of trust or the possibility that

consumers will buy product Y after purchasing product

Table 8: Iteration 4

Item

Support

Count

Support

Riser, Hot Coffee, Hot Tea,

Hazelnut Coffee

1 20%

Riser, Hot Coffee, Hot Tea,

Scramble Egg

1 20%

Riser, Hot Coffee, Hot Tea,

Waffle

1 20%

Hot Coffee, Hot Tea, Hazelnut

Coffee, Scramble Egg

2 40%

Hot Coffee, Hot Tea, Hazelnut

Coffee, Waffle

2 40%

Hot Tea, Hazelnut Coffee,

Scramble Egg, Waffle

2 40%

Table 9: The Result of iteration 4

Item

Support

Count

Support

Hot Coffee, Hot Tea, Hazelnut

Coffee, Scramble Egg

2 40%

Hot Coffee, Hot Tea, Hazelnut

Coffee, Waffle

2 40%

Hot Tea, Hazelnut Coffee,

Scramble Egg, Waffle

2 40%

Table 10: Iteration 5

Item

Support

Count

Support

Hot Coffee, Hot Tea, Hazelnut

Coffee, Scramble Egg, Waffle

2 40%

The iteration process stops at the 5th iteration due to

there is no longer a support value of less than 40%. So that,

it can proceed to the association rule formation from the

combination of items obtained in iteration 5 and confidence

value calculation that shown on table 11.

Table 11: Confidence Value at The Association Rule

No Item B A Confidence

{Hot Coffee, Hot Tea,

Hazelnut Coffee,

Scramble Egg} →

{Waffle}

40% 40% 100%

Determination of Breakfast Menu Patterns on the Fast Food Restaurant Using Apriori Algorithm

{Hot Tea, Hazelnut

Coffee, Scramble Egg,

Waffle} → {Hot

Coffee}

40%

100

40%

{Hot Coffee, Hot Tea,

Hazelnut Coffee,

Waffle} → {Scramble

Egg}

40% 60% 67%

{Hot Coffee, Hot Tea,

Hazelnut Coffee} →

{Scramble Egg,

Waffle}

40% 40% 100%

{Waffle, Hot Tea,

Hazelnut Coffee} →

{Scramble Egg, Hot

Coffee}

40% 60% 67%

{Waffle, Hot Coffee,

Hazelnut Coffee} →

{Scramble Egg, Hot

Tea}

40% 60% 67%

{Waffle, Hot Coffee,

Hot Tea} →

{Scramble Egg,

Hazelnut Coffee}

40% 40% 100%

{Waffle, Scramble

Egg, Hazelnut Coffee}

→ {Hot Coffee, Hot

Tea}

40% 80% 80%

{Waffle, Scramble

Egg, Hot Tea} → {Hot

Coffee, Hazelnut

Coffee}

40% 60% 67%

{Hot Coffee, Scramble

Egg, Hot Tea} →

{Waffle, Hazelnut

Coffee}

60% 40% 67%

{Scramble Egg,

Hazelnut Coffee} →

{Hot Coffee, Hot Tea}

40% 80% 50%

{Waffle, Hazelnut

Coffee} → {Hot

Coffee, Hot Tea}

40% 80% 50%

{Hot Tea, Hazelnut

Coffee} → {Scramble

Egg, Waffle}

40% 40% 100%

{Hot Tea, Hot Coffee}

→ {Scramble Egg}

80% 60% 75%

{Hot Tea, Hot Coffee}

→ {Waffle}

80% 40% 50%

{Hot Coffee, Hot Tea}

→ {Hazelnut Coffee}

80% 60% 75%

{Hot Tea, Scramble

Egg} → {Waffle}

0% 0% 67%

{Hot Tea, Scramble

Egg} → {Hazelnut

Coffee}

60% 60% 100%

{Scramble Egg} →

{Waffle}

60% 40% 67%

{Scramble Egg} →

{Hot Coffee}

60%

100

60%

{Scramble Egg} →

{Hot Tea}

60% 80% 75%

{Scramble Egg} →

{Hazelnut Coffee}

60% 60% 100%

23 {Waffle} → {Hot Tea} 40% 80% 50%

{Waffle} → {Hazelnut

Coffee}

40% 60% 67%

{Hot Coffee} → {Hot

Tea}

100

80% 80%

{Hot Coffee} →

{Hazelnut Coffee}

100

60% 60%

{Hot Tea} →

{Hazelnut Coffee}

80% 60% 75%

The calculation results in table 11 show that there are

twelve items set that meet the value minimum

confidence (75%) in the X → Y association rule, that is,

the item set with the confidence value be bolded.

4 CONCLUSIONS

There are 12 (twelve) association rules that meets the

minimum confidence requirement with a confidence

value of ≥75%, which is the rule Association number 1,

4 7, 8, 13, 14, 16, 18, 21, 22, 25, 27. As the example

association rule number 22 shows that if the consumer

buys menu {Scramble Egg}, then the possibility to buy

menu {Hazelnut Coffee} is 100% and so on. So that

management staff could manage marketing strategies by

offering promo menu that contain two type cuisine

according to the results of the discussion above for

weekend promotion to increase competitive advantage.

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Determination of Breakfast Menu Patterns on the Fast Food Restaurant Using Apriori Algorithm