# Shortest Path Problem Solving in the Installation of DataInternet Network Using Apriori Algorithm

Shortest-Path Problem Solving in the Installation of

Data/Internet Network Using Apriori Algorithm

Ali Akbar 1

Nurul Adhayanti2

Faculty of Industrial Technology

Gunadarma University

Indonesia

akbarjawas@gmail.com

Faculty of Computer Science

Gunadarma University

Indonesia

nuruladhayanti@gmail.com

Hendri Dwi Putra 4

Ike Putri Kusumawijaya

3

Faculty of Computer Science

Gunadarma University

Indonesia

Hendri_dpg@gmail.com

Faculty of Industrial Technology

Gunadarma University

Indonesia

ikeputri30@gmail.com

Abstract—Data network is something highly important in

information development. The commonly occuring problem is

how to connect every node or town to make it connected to the

network. We develop a softaware tool for solving shortestpath problem with Apriori algorithm to solve problem in the

shortest-path in the implementation of internet network. Based

on the research results by comparing Apriori Algorithm to

genetics, it is found that Apriori Algorithm has advantages

from the distance side used. In this case, for an experiment of

10 urban points, a distance of 38 for genetic algorithm is

found as compared to 29 using the apriori algorithm and the

value increases when 200 points are experimented, resulting in

a value of 5931 for genetic and 242.5 (for apriori). From this

result, it can then be concluded that apriori algorithm has the

advantage in the form of lesser distance than the genetic

algorithm hence it can be expected to reduce the costs.

Keywords—Apriori algorithm; Data network ; routing

I.

INTRODUCTION

This routing problem can be presented as how to determine

the shortest track to find a path between two nodes in such a

way that the total weight of its constituent arcs can be as

minimum as possible [1]. The shortest-path problems include

djikstra algorithm, Floyd-Warshall algorithm and BellmanFord algorithm. Meanwhile, according to Rama M Sukaton in

his research entitled “Penggunaan Algoritma Genetika Dalam

Jalur Terpendek Pada Jaringan Data”, the most appropriate

method for shortest-path problem with increasingly greater

and more complex number of nodes and tracks is genetic

algorithm, since despite the great number of tracks it can still

be solved and it moves towards an optimal point when it is

followed by increased size of other populations/paths [2]. In

this writing, we try to use apriori algorithm to solve shortestpath problem and try to compare it to genetic algorithm.

II.

EASE OF USE (LANDASAN TEORI )

A. Routing

A process of finding a path when there is a node in a

communication path in computer network is an important task

of a router device in a routing operation which is governed in

a protocol. There are two types of routing, i.e. static and

dynamic. In case of static routing, the path between nodes is

determined manually based on certain factors and saved in a

routing table [1]. For example, in Rama M Sukaton’s research

[2] it is shown router A which has two Ethernet interfaces and

one ISDN (Integrated Services Digital Network) interface,

where the Ethernet0 (e0) interface is assigned an IP address

10.1.1/24 and the Ethernet1 (e1) interface is assigned an IP

address 10.1.2.1/24.

B. Apriori algorithm

Apriori algorithm is a highly popular pattern-finding

algorithm in data mining technique. This algorithm is aimed at

finding an itemset combination which has a certain value of

frequency according to the desired criteria or filter. This

algorithm is proposed by R. Agrawal and R. Srikant. The

result of apriori algorithm can be used to help the management

make decisions. Apriori algorithm do an iterative approach

known as level-wise search, where k-itemset is used to explore

or find (k+1)-itemset. Therefore, apriori algorithm is divided

into several stages called iteration. Every iteration produces a

high-frequency pattern (frequent itemset) [3]. The theoretical

and empirical comparison of R-Apriori with existing apriori

implementation on the Spark platform (YAFIM) is done to

give insight into the superiority of our approach. In addition,

R-Apriori is more superior than classic Apriori on the Spark

for different standard dataset.[5]. In his research, Sayeth

Saabith concludes that Hadoop-MapReduce platform is

efficient and for the calculation of huge data the Hadoop-

Mapreduce which uses apriori algorithm is more efficient than

the data search on Hadoop-MapReduce platform with no

apriori algorithm.[6] From this, we can conclude that apriori

algorithm can increase the efficiency of data calculation

analysis. Additionally, another study mentions that aprioribased algorithm has better speed in managing greater data [7].

Apriori Algorithm Analysis with Router Shortest-Path

Problem

•

Apriori algorithm belongs to association rule mining, i.e. a

data mining technique to find the associative rule between

item combinations. An example of associative rule of routing

path analysis is to find out the shortest path in a large data

network. Using this knowledge, the router can regulate the

placement of paths with combination of several existing paths.

In determining an association rule, there is an

interestingness measure which is obtained from processing the

data using certain calculation. There are generally two

measures, namely [4]

•

•

Support (supporting value): a measure which shows

how large an item/itemset’s domination level is over

the entire data path. This measure decides whether an

item/itemset’s (data path) confidence is worth-finding

(for example, out of the entire existing networks, how

large is the domination level which shows that the

network is used.

Confidence (certainty/confidence value): a measure

which shows the relationship between 2 paths

conditionally (for example, how frequent is path B

used if the network is in use).

These two measures would eventually be useful in

determining the interesting association rules, i.e. to be

compared to a threshold determined by users. This threshold

generally consists of min_support and min_confidence, where

it is taken using the following ways [4]:

•

Finding all frequent itemsets, i.e. the itemsets with

support ≥ minimum support values which is the

threshold given by users. Where these itemset are a set

of items that is the combined purchased products.

•

Finding the association rule which is the confidence of

the obtained frequent itemset.

•

Finally, finding the rules which match the obtained

target users of the previous association rule mining

process. The obtained rules decribe the itemset

combination based on which the conclusion is drawn.

C. Genetic Algorithm

Genetic algorithm is an algorithm of search which depicts

biological evolution as a problem solving technique. Genetic

algorithm uses heuristic adaptive search technique which finds

a set of best solutions of the newly-produced/developing

population from the chromosome using an operator such as

selection, crossover and mutation. The most suitable is to

move the chromosome to the next generation. Weaker

candidate have less chance to move to the next generation.

This process is repeated until the chromosome has the best

solution which match the given problem. In summary, the

average population fitness increases in each iteration, hence by

repeating the process for more iterations, a better result is

found. Genetic algorithm has been widely studied and

experimented in various engineering fields. Genetic algorithm

provides an alternative method to solve existing problems

which are hard to solve using traditional methods. Genetic

algorithm can be applied to non-linear programming such as

problem of a moving salesman, minimum spanning tree,

scheduling issues and many more.[1]

For genetic algorithm, the basis is as follows:

•

•

•

•

•

•

•

•

•

•

generation = 0;

population [generation] = initializing population

(population);

evaluating Population (population [generation]);

While isTermination Condition Met () == false do

Parents= choose Parents (population [generation]);

population [generation + 1] = crossover (parents);

population [generation + 1] = mutating (population

[generation + 1]);

evaluating Population (population [generation]);

generation ++;

End of circle;

The pseudo code begins with creating an initial population

of genetic algorithm. This population is then evaluated to find

the fitness value of individuals. Furthermore, a check is run to

decide whether the condition for genetic algorithm termination

has been met. If it has not, the genetic algorithm begins the

iteration and the population runs through the first cycle of

crossover and mutation before it is finally be re-evaluated.

From here, the crossover and mutation continue to be applied

until the termination condition is met, and the genetic

algorithm ends. This pseudo code shows the basic process [8].

In another study it is stated that the Genetic algorithm will be

used to optimize the set of items and find the optimal and

appropriate association rule.[9] And this algorithm is also used

to determine the shortest path in previous studies [2]. Studies

using genetic algorithm to solve the shortest-path problems

have been conducted by Gihan Nagib and Wahied G. Ali. The

research finds that genetic algorithm has similar result as

Dijkstra algorithm [10]

Based on the several studies which indicate that apriori

algorithm can improve the performance of huge data

management, we try to use apriori algorithm to solve the

problem of determining the shortest path. It is expected that

using apriori algorithm, significant influence in the selection

of shortest path can be obtained. As a comparison, we use

Genetic algorithm which has been previously used to solve the

shortest-path problem in data network.

III.

RESEARCH METHOD

3.1. Research Method

In this research we try to find out whether association rule

mining can be used in the shortest path and comparison with

genetic algorithm

In the apriori algorithm explained in this research, the type

of representation used is to use support and confidence, using

network nodes with positive numbers 1,2,3..,n where is the

amount of nodes in network, every amount of nodes serves as

input of support and confidence minimum values, thus it can

be depicted as a string of codes of nodes in network which

does not repeat and represents a sequence or path.

The research design flow method performed in apriori

algorithm to determine the shortest path in data

communication has the following flow:

•

Network traffic data, is a simulation of network points

which are likened to be in an area and town, which will

be used as an input of apriori algorithm

•

Apriori algorithm is done by turning the traffic data lalu

into input/network. In the initial process, the data will be

collected to be a database, then it will be processed to

generate a network with apriori iteration to obtain an

optimized shortest route of a network traffic data

•

Computation is the combination of results from the

traffic data with the design resulting from literature study

•

The resulting shortest path is the generated from the

computation containing description of the resulting

shortest path with optimized iteration from the achieved

shortest path

The implementation of apriori algorithm uses software

MATLAB R2013b.

The inputs from the program are:

• File input, containing network data such as number of

towns with random values,

• Apriori algorithm parameters, i.e. minimum support

(MinSup), minimum confidence (MinConf), number of

matching process or nRules, and number of iterations.

Below are the functions used in the program along with their

explanations.

• Aporiori_jalurter pendek

An apriori algorithm for shortest path problem in general

will be run by the aporiori_jalurterpendek function. This

function will summon other functions such as the parameters

used in the apriori namely, minimum support, minimum

confidence, n rules and iteration

The

program

summons

is

done

by

typing

“apriori_jalurterpendek” on the MATLAB Command

Window. The program will stop when the predetermined

generation limit has been met by users. An example of

program summons when entering data and outputs is: Type

“aporiori_jalurterpendenk” on the MATLAB command

window and press enter. The program will then go through a

running process as in figure 3.2

Figure 3.2 Apriori Running Process

The apriori_jalurterpendek process, during the running

process will generate output, as in figures 3.3 and 3.4

Figure 3.1 Research Design

The apriori algorithm for this case is as follows:

•

•

•

•

•

Determine the number of network nodes

Generate connection of each node/network using support

variable

Determine the value of each connection at each node,

with confidence variable

find the shortest node by generating the distance,

iteration, cost outputs

repeat step 2 if you want to use different number of nodes

3.2. Implementation

Figure 3.3. Best Solution Results of Apriori Algorithm

Figure 3.4 Overall Result of Apriori Algorithm

Figures 3.3 and 3.4 are the overall results of apriori algorithm.

Figure 3.3 shows the best solution results of apriori algorithm.

There are some colored lines indicating paths from data of

closest location, and there is a central point which can connect

to all location paths, at a total distance of 55.5802 and the

number of iterations is 2086 to reach an optimum level.

3.3 Results of Comparison Experiment

The experiments are done by changing the parameter

value of minimum confidence and support as well as the

iteration for all experiments. The experiment problem using

the problem of determining the solution optimality of apriori

algorithm will refert to the solution of genetic algorithm, since

genetic algorithm is widely used by other researchers in

determining the optimal solution to shortest path problem.

Below is the table of comparison between apriori algorithm

and genetic algorithm [2].

Figure 3.5 Best solutin results of apriori algorithm

Figure 3.6 Results of total apriori time

The next experiment result with large data is illustrated in the

following data:

Table 3 Comparsion Experiment of Genetic and Apriori

Algorithms (continued)

Algorithm

Genetika

Apriori

Numbe

r of

town

points

200

200

Source

node

Target

node

1

1

200

200

Optimal measure

iterati

Dista

Total

on

nce/c

time

ost

(second)

0.259

500

5931

58

4978

242.5

Table 3.1 Comparison Experiment of Genetic and Apriori

Algorithms

Algorithm

Genetic

Apriori

Numbe

r of

town

points

10

10

Source

node

Target

node

1

1

10

10

Optimal measure

iterati

Dista

Total

on

nce/c

time

ost

(second)

0.2

10

39

2

50

28

In table 3.1, two algorithms related to the material of finding

the shortest path with similar test parameters are tested. Using

the first test, the number of town points is 10, with source

node at 1 point, and target node in all points, i.e. 10 points.

The optimum measure is counted from the total time (second),

iteration, and distance/cost. In the first table, the genetic

algorithm is taken from Rama Sukaton’s, 2011, research

which has total time to obtain the shortest path with number of

iteration of 10 and distance or cost has a value of 39.

Meanwhile, the apriori algorithm delivers total time to obtain

the shortest path at 2 seconds, with number of iterations of 36

and distance/cost 26.

Figure 3.6 Result of iteration and distance/cost of apriori

algorithm

IV.

CONCLUSION AND SUGGESTION

This research proves that from execution time and

iteration perspective, genetic algorithm is far more superior,

while apriori algorithm is superior in its ability to determine

distance/cost. This algorithm can be used particularly to

reduce the costs spent to install new data network.

For further research, it is suggested to improve the ability

of finding the shortest path with apriori algorithm:

1.

2.

Another algorithm can be used to improve the optimum

to be a comparison of the more superior algorithm in

finding the solutions and shortest path

There is a need to add additional algorithms to awaken

the provision of support and confidence values to

guarantee that the apriori algorithm will not take to much

time and computation.

ACKNOWLEDGEMENT

This research was fully supported by Universitas Gunadarma,

Jakarta, Indonesia. The authors gratefully acknowledge

Universitas Gunadarma for providing research funding and for

permission in using the research facilities

REFERENCES

[1]

[2]

(R.Kumar dan M.Kumar, 2010). “Exploring Genetic Algorithm for

Shortest Path Optimization in Data Networks”, Global Journal of

Computer Science and Technology. Vol. 10 Issue 11 page 8-12 2010

Rama M Sukaton (2010), “Pengunaan Genetic algorithm Dalam

Masalah Jalur Terpedek Pada Data network”, Universitas Indonesia

2011

[3]

R. Agrawal and R. Srikant. “Fast, algorithms for mining association

rules in large databases”. Research Report RJ 9839, IBM Almaden

Research Center, San Jose, California, June 1994.

[4] Han, Jiawei; Kamber, Micheline; Data Mining: Concepts and

Techniques. Morgan Kaufmann, 2001

[5] Sanjay Rathee, et all; R-Apriori : An Efficient Apriori Based Algorithm

on Spark. Melbourne, VIC, Australia, October 2015

[6] A.L.Sayeth Saabith, Elankovan Sundararajan, And Azuraliza Abu

Bakar. “Parallel Implementation Of Apriori Algorithms On The

Hadoop-Mapreduce Platform An Evaluation Of Literature”. In Journal

of Theoretical and Applied Information Technology, Vol 85 No.3, 2016.

[7] Swami Konakanchi1, V P S Vinay Kumar, Chanda Srinivasarao.

Parallel Mining of Frequent Itemsets Based on MapReduce Approach.

In International Journal of Mechanical Engineering and Computer, 372378, 2015.

[8] J Lee, K Burak, Genetic Algorithms in Java Basics. An Apress

Advanced Book, Springer Science & Business Media New York NY:

2015.

[9] Shruti S. Gadgil, L.M.R.J. Lobo. MapReduce to Find Association Rules

Representing Social Network Data. In International Journal of Computer

Applications. Hal 15-18 . 2016

[10] Gihan Nagib and Wahied G. Ali., Network Routing Protocol using

Genetic Algorithms. In International Journal of Electrical & Computer

Sciences IJECS-IJENS. Vol:10 No:02. Hal 36-40. 2010

Data/Internet Network Using Apriori Algorithm

Ali Akbar 1

Nurul Adhayanti2

Faculty of Industrial Technology

Gunadarma University

Indonesia

akbarjawas@gmail.com

Faculty of Computer Science

Gunadarma University

Indonesia

nuruladhayanti@gmail.com

Hendri Dwi Putra 4

Ike Putri Kusumawijaya

3

Faculty of Computer Science

Gunadarma University

Indonesia

Hendri_dpg@gmail.com

Faculty of Industrial Technology

Gunadarma University

Indonesia

ikeputri30@gmail.com

Abstract—Data network is something highly important in

information development. The commonly occuring problem is

how to connect every node or town to make it connected to the

network. We develop a softaware tool for solving shortestpath problem with Apriori algorithm to solve problem in the

shortest-path in the implementation of internet network. Based

on the research results by comparing Apriori Algorithm to

genetics, it is found that Apriori Algorithm has advantages

from the distance side used. In this case, for an experiment of

10 urban points, a distance of 38 for genetic algorithm is

found as compared to 29 using the apriori algorithm and the

value increases when 200 points are experimented, resulting in

a value of 5931 for genetic and 242.5 (for apriori). From this

result, it can then be concluded that apriori algorithm has the

advantage in the form of lesser distance than the genetic

algorithm hence it can be expected to reduce the costs.

Keywords—Apriori algorithm; Data network ; routing

I.

INTRODUCTION

This routing problem can be presented as how to determine

the shortest track to find a path between two nodes in such a

way that the total weight of its constituent arcs can be as

minimum as possible [1]. The shortest-path problems include

djikstra algorithm, Floyd-Warshall algorithm and BellmanFord algorithm. Meanwhile, according to Rama M Sukaton in

his research entitled “Penggunaan Algoritma Genetika Dalam

Jalur Terpendek Pada Jaringan Data”, the most appropriate

method for shortest-path problem with increasingly greater

and more complex number of nodes and tracks is genetic

algorithm, since despite the great number of tracks it can still

be solved and it moves towards an optimal point when it is

followed by increased size of other populations/paths [2]. In

this writing, we try to use apriori algorithm to solve shortestpath problem and try to compare it to genetic algorithm.

II.

EASE OF USE (LANDASAN TEORI )

A. Routing

A process of finding a path when there is a node in a

communication path in computer network is an important task

of a router device in a routing operation which is governed in

a protocol. There are two types of routing, i.e. static and

dynamic. In case of static routing, the path between nodes is

determined manually based on certain factors and saved in a

routing table [1]. For example, in Rama M Sukaton’s research

[2] it is shown router A which has two Ethernet interfaces and

one ISDN (Integrated Services Digital Network) interface,

where the Ethernet0 (e0) interface is assigned an IP address

10.1.1/24 and the Ethernet1 (e1) interface is assigned an IP

address 10.1.2.1/24.

B. Apriori algorithm

Apriori algorithm is a highly popular pattern-finding

algorithm in data mining technique. This algorithm is aimed at

finding an itemset combination which has a certain value of

frequency according to the desired criteria or filter. This

algorithm is proposed by R. Agrawal and R. Srikant. The

result of apriori algorithm can be used to help the management

make decisions. Apriori algorithm do an iterative approach

known as level-wise search, where k-itemset is used to explore

or find (k+1)-itemset. Therefore, apriori algorithm is divided

into several stages called iteration. Every iteration produces a

high-frequency pattern (frequent itemset) [3]. The theoretical

and empirical comparison of R-Apriori with existing apriori

implementation on the Spark platform (YAFIM) is done to

give insight into the superiority of our approach. In addition,

R-Apriori is more superior than classic Apriori on the Spark

for different standard dataset.[5]. In his research, Sayeth

Saabith concludes that Hadoop-MapReduce platform is

efficient and for the calculation of huge data the Hadoop-

Mapreduce which uses apriori algorithm is more efficient than

the data search on Hadoop-MapReduce platform with no

apriori algorithm.[6] From this, we can conclude that apriori

algorithm can increase the efficiency of data calculation

analysis. Additionally, another study mentions that aprioribased algorithm has better speed in managing greater data [7].

Apriori Algorithm Analysis with Router Shortest-Path

Problem

•

Apriori algorithm belongs to association rule mining, i.e. a

data mining technique to find the associative rule between

item combinations. An example of associative rule of routing

path analysis is to find out the shortest path in a large data

network. Using this knowledge, the router can regulate the

placement of paths with combination of several existing paths.

In determining an association rule, there is an

interestingness measure which is obtained from processing the

data using certain calculation. There are generally two

measures, namely [4]

•

•

Support (supporting value): a measure which shows

how large an item/itemset’s domination level is over

the entire data path. This measure decides whether an

item/itemset’s (data path) confidence is worth-finding

(for example, out of the entire existing networks, how

large is the domination level which shows that the

network is used.

Confidence (certainty/confidence value): a measure

which shows the relationship between 2 paths

conditionally (for example, how frequent is path B

used if the network is in use).

These two measures would eventually be useful in

determining the interesting association rules, i.e. to be

compared to a threshold determined by users. This threshold

generally consists of min_support and min_confidence, where

it is taken using the following ways [4]:

•

Finding all frequent itemsets, i.e. the itemsets with

support ≥ minimum support values which is the

threshold given by users. Where these itemset are a set

of items that is the combined purchased products.

•

Finding the association rule which is the confidence of

the obtained frequent itemset.

•

Finally, finding the rules which match the obtained

target users of the previous association rule mining

process. The obtained rules decribe the itemset

combination based on which the conclusion is drawn.

C. Genetic Algorithm

Genetic algorithm is an algorithm of search which depicts

biological evolution as a problem solving technique. Genetic

algorithm uses heuristic adaptive search technique which finds

a set of best solutions of the newly-produced/developing

population from the chromosome using an operator such as

selection, crossover and mutation. The most suitable is to

move the chromosome to the next generation. Weaker

candidate have less chance to move to the next generation.

This process is repeated until the chromosome has the best

solution which match the given problem. In summary, the

average population fitness increases in each iteration, hence by

repeating the process for more iterations, a better result is

found. Genetic algorithm has been widely studied and

experimented in various engineering fields. Genetic algorithm

provides an alternative method to solve existing problems

which are hard to solve using traditional methods. Genetic

algorithm can be applied to non-linear programming such as

problem of a moving salesman, minimum spanning tree,

scheduling issues and many more.[1]

For genetic algorithm, the basis is as follows:

•

•

•

•

•

•

•

•

•

•

generation = 0;

population [generation] = initializing population

(population);

evaluating Population (population [generation]);

While isTermination Condition Met () == false do

Parents= choose Parents (population [generation]);

population [generation + 1] = crossover (parents);

population [generation + 1] = mutating (population

[generation + 1]);

evaluating Population (population [generation]);

generation ++;

End of circle;

The pseudo code begins with creating an initial population

of genetic algorithm. This population is then evaluated to find

the fitness value of individuals. Furthermore, a check is run to

decide whether the condition for genetic algorithm termination

has been met. If it has not, the genetic algorithm begins the

iteration and the population runs through the first cycle of

crossover and mutation before it is finally be re-evaluated.

From here, the crossover and mutation continue to be applied

until the termination condition is met, and the genetic

algorithm ends. This pseudo code shows the basic process [8].

In another study it is stated that the Genetic algorithm will be

used to optimize the set of items and find the optimal and

appropriate association rule.[9] And this algorithm is also used

to determine the shortest path in previous studies [2]. Studies

using genetic algorithm to solve the shortest-path problems

have been conducted by Gihan Nagib and Wahied G. Ali. The

research finds that genetic algorithm has similar result as

Dijkstra algorithm [10]

Based on the several studies which indicate that apriori

algorithm can improve the performance of huge data

management, we try to use apriori algorithm to solve the

problem of determining the shortest path. It is expected that

using apriori algorithm, significant influence in the selection

of shortest path can be obtained. As a comparison, we use

Genetic algorithm which has been previously used to solve the

shortest-path problem in data network.

III.

RESEARCH METHOD

3.1. Research Method

In this research we try to find out whether association rule

mining can be used in the shortest path and comparison with

genetic algorithm

In the apriori algorithm explained in this research, the type

of representation used is to use support and confidence, using

network nodes with positive numbers 1,2,3..,n where is the

amount of nodes in network, every amount of nodes serves as

input of support and confidence minimum values, thus it can

be depicted as a string of codes of nodes in network which

does not repeat and represents a sequence or path.

The research design flow method performed in apriori

algorithm to determine the shortest path in data

communication has the following flow:

•

Network traffic data, is a simulation of network points

which are likened to be in an area and town, which will

be used as an input of apriori algorithm

•

Apriori algorithm is done by turning the traffic data lalu

into input/network. In the initial process, the data will be

collected to be a database, then it will be processed to

generate a network with apriori iteration to obtain an

optimized shortest route of a network traffic data

•

Computation is the combination of results from the

traffic data with the design resulting from literature study

•

The resulting shortest path is the generated from the

computation containing description of the resulting

shortest path with optimized iteration from the achieved

shortest path

The implementation of apriori algorithm uses software

MATLAB R2013b.

The inputs from the program are:

• File input, containing network data such as number of

towns with random values,

• Apriori algorithm parameters, i.e. minimum support

(MinSup), minimum confidence (MinConf), number of

matching process or nRules, and number of iterations.

Below are the functions used in the program along with their

explanations.

• Aporiori_jalurter pendek

An apriori algorithm for shortest path problem in general

will be run by the aporiori_jalurterpendek function. This

function will summon other functions such as the parameters

used in the apriori namely, minimum support, minimum

confidence, n rules and iteration

The

program

summons

is

done

by

typing

“apriori_jalurterpendek” on the MATLAB Command

Window. The program will stop when the predetermined

generation limit has been met by users. An example of

program summons when entering data and outputs is: Type

“aporiori_jalurterpendenk” on the MATLAB command

window and press enter. The program will then go through a

running process as in figure 3.2

Figure 3.2 Apriori Running Process

The apriori_jalurterpendek process, during the running

process will generate output, as in figures 3.3 and 3.4

Figure 3.1 Research Design

The apriori algorithm for this case is as follows:

•

•

•

•

•

Determine the number of network nodes

Generate connection of each node/network using support

variable

Determine the value of each connection at each node,

with confidence variable

find the shortest node by generating the distance,

iteration, cost outputs

repeat step 2 if you want to use different number of nodes

3.2. Implementation

Figure 3.3. Best Solution Results of Apriori Algorithm

Figure 3.4 Overall Result of Apriori Algorithm

Figures 3.3 and 3.4 are the overall results of apriori algorithm.

Figure 3.3 shows the best solution results of apriori algorithm.

There are some colored lines indicating paths from data of

closest location, and there is a central point which can connect

to all location paths, at a total distance of 55.5802 and the

number of iterations is 2086 to reach an optimum level.

3.3 Results of Comparison Experiment

The experiments are done by changing the parameter

value of minimum confidence and support as well as the

iteration for all experiments. The experiment problem using

the problem of determining the solution optimality of apriori

algorithm will refert to the solution of genetic algorithm, since

genetic algorithm is widely used by other researchers in

determining the optimal solution to shortest path problem.

Below is the table of comparison between apriori algorithm

and genetic algorithm [2].

Figure 3.5 Best solutin results of apriori algorithm

Figure 3.6 Results of total apriori time

The next experiment result with large data is illustrated in the

following data:

Table 3 Comparsion Experiment of Genetic and Apriori

Algorithms (continued)

Algorithm

Genetika

Apriori

Numbe

r of

town

points

200

200

Source

node

Target

node

1

1

200

200

Optimal measure

iterati

Dista

Total

on

nce/c

time

ost

(second)

0.259

500

5931

58

4978

242.5

Table 3.1 Comparison Experiment of Genetic and Apriori

Algorithms

Algorithm

Genetic

Apriori

Numbe

r of

town

points

10

10

Source

node

Target

node

1

1

10

10

Optimal measure

iterati

Dista

Total

on

nce/c

time

ost

(second)

0.2

10

39

2

50

28

In table 3.1, two algorithms related to the material of finding

the shortest path with similar test parameters are tested. Using

the first test, the number of town points is 10, with source

node at 1 point, and target node in all points, i.e. 10 points.

The optimum measure is counted from the total time (second),

iteration, and distance/cost. In the first table, the genetic

algorithm is taken from Rama Sukaton’s, 2011, research

which has total time to obtain the shortest path with number of

iteration of 10 and distance or cost has a value of 39.

Meanwhile, the apriori algorithm delivers total time to obtain

the shortest path at 2 seconds, with number of iterations of 36

and distance/cost 26.

Figure 3.6 Result of iteration and distance/cost of apriori

algorithm

IV.

CONCLUSION AND SUGGESTION

This research proves that from execution time and

iteration perspective, genetic algorithm is far more superior,

while apriori algorithm is superior in its ability to determine

distance/cost. This algorithm can be used particularly to

reduce the costs spent to install new data network.

For further research, it is suggested to improve the ability

of finding the shortest path with apriori algorithm:

1.

2.

Another algorithm can be used to improve the optimum

to be a comparison of the more superior algorithm in

finding the solutions and shortest path

There is a need to add additional algorithms to awaken

the provision of support and confidence values to

guarantee that the apriori algorithm will not take to much

time and computation.

ACKNOWLEDGEMENT

This research was fully supported by Universitas Gunadarma,

Jakarta, Indonesia. The authors gratefully acknowledge

Universitas Gunadarma for providing research funding and for

permission in using the research facilities

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