Development of Population Data Cluster Application based on

Real-time Expertise

Herry S. Lang, Dostenreyk Kantohe, Ottopianus Mellolo, Sonny Kasenda and Tracy Marcela

Manado State Polytechnic, Jl. Raya Politeknik Ds. Buha, Kairagi, Manado, Indonesia

cy@elekt

o.poli

do.ac.id

Keywords: Data, Application, Real-time, Bitung City Academic.

Abstract: Population data is one of the information needed for sustainable development planning. Sustainable

development is a planned development in all fields to create an ideal comparison between population

development and the carrying capacity and capacity of the environment and to meet the needs of the current

generation, without having to reduce the capabilities and needs of future generations, to support the life of the

nation from generation to generation throughout time. Population data as essential regional data is relatively

static, such as data on changes in migration between regions. Changes in the general structure of the

population, socio-economic structure, vertical and horizontal population mobility are an essential part of

population data collection and planning at the regional and national levels. Invalid population data is one of

the weak points in implementing sustainable development in the regions. For example, in human resource

planning, data is needed on the number of school-age population and workers. By incorporating population

into the national economic and development strategy, sustainable development will accelerate by growing the

workforce. As a result, development goals will be achieved more quickly. Information on population data in

the city of Bitung is not efficient in its management. The data input process should be carried out at the unit

closest to the community, such as the village. This application will be developed using PHP and MySQL. By

using the mixing method, the application will be developed to the trial stage in real conditions. This

application was created to classify the population's expertise in real-time to support sustainable development

in the city of Bitung in particular.

1 INTRODUCTION

A population is an object as well as a subject in

national development. The policy in the field of the

population is not only about the number and density

of the population, immigration flows, births, and

deaths, but also policies in terms of controlling high

population growth and directing mobility and a more

even distribution of the population, especially in

sparsely populated areas. Regulate the desired

population will cause social and economic problems.

The very large population growth will affect the

facilities and infrastructure in the fields of education,

health, and so on.

Referring to data from the BPS (Central Statistics

Agency) of Bitung City through a publication entitled

"Bitung City in Figures 2021", it was noted that the

number of the labor force in Bitung city in 2020 was

91,622 people with the percentage of the population

working towards the workforce in Bitung city was

89.77%, where the working-age population is the

population aged 15 years and over while the labor

force is the population working age (15 years and

over)who are working, have a job but temporarily not

working, and are unemployed. The number of skilled

and skilled workforce at BLKI Bitung City in 2020 is

859people. The problems caused by the large number

and growth of the labor force, on the one hand,

demand greater job opportunities. On the other hand,

demand the development of the workforce itself so

that it is able to produce higher outputs. This increase

must be anticipated by the government and the

business world as employers or job openings.

Employment comes from economic growth.

However, high growth does not always provide large

jobs. This relates to the economic development

strategy carried out by the government and the

business world. Another thing that must also be

considered in analyzing the relationship between the

labor force and employment opportunities is that if

Lang, H., Kantohe, D., Mellolo, O., Kasenda, S. and Marcela, T.

Development of Population Data Cluster Application based on Real-time Expertise.

DOI: 10.5220/0010949900003260

In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2021), pages 613-618

ISBN: 978-989-758-615-6; ISSN: 2975-8246

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

613

job opportunities are above the labor force, it does not

mean that employment problems, ormore specifically

unemployment, are resolved. The existence of new

job opportunities is a "potential," and this "potential"

may not be utilized if the available workforce does

not have adequate quality.

A large population and workforce will be able to

become development potential if properly fostered.

Good coaching will produce a good quality

workforce. The quality of the workforce is reflected,

among others, in the level of education and

skills/expertise. A large population with low

population quality causes the population to become a

burden for economic growth and not a driver.

Employment problems are also caused by the lack of

competence and expertise needed by labor users.

Therefore, this application is made for grouping

population skills in real-time to support sustainable

development in the city of Bitung in particular.

2 SYSTEM ANALYSIS METHOD

Data analysis is one of the important steps in

obtaining research findings. This is because the data

will lead us to scientific findings when analyzed with

the proper techniques. The analysis of this system is

carried out using the Simple Additive Weighting

(SAW) method, which is often also known as the

weighted addition method. The basic concept of the

SAW method is to find the weighted sum of the

performance ratings on each alternative on all

attributes (Fishburn, 1967) (MacCrimmon, 1968).

The SAW method requires the process of normalizing

the decision matrix (X) to a scale that can be

compared with all existing alternative ratings. The

SAW method recognizes 2 (two) attributes, namely

the benefit criteria and the cost criteria.

The steps for completing the Simple Additive

Weighting (SAW) method are as follows

a. Determine the criteria that will be used as a

reference in decision making, namely Cj

b. Determine the suitability rating of each alternative

on each criterion.

c. Make a decision matrix (X) which is formed from

the suitability rating table for each alternative on

each criterion. The value of X for each alternative

(Ai) on each criteria (Cj) that has been

determined, where, i=1,2,…m and j=1,2,…n.

(1)

d. Normalize the decision matrix by calculating the

value of the normalized performance rating (rij)

from the alternative Ai on the Cj criteria. If j is a

benefit attribute, then

(2a)

If j is a cost attribute, then

(2b)

Where:

= normalized performance rating value of

alternative A

on attribute Cj

Xi = attribute value owned by each criterion Max(i)

Xij= the largest value of each criterion i Min(i)

Xij= the smallest value of each criterion i

Benefits= if the largest value is the best

Cost= if the smallest value is the best value

e. The results of the normalized performance rating

value (r

) form a normalized matrix (R)

(3)

f. The final result obtained from the ranking

process is normalized matrix multiplication R

with the weight vector. The largest value is

chosen as the best alternative (Ai) as the solution.

V =∑

𝑗

𝑤

𝑟

𝑗

(4)

Where:

Vi = ranking for each alternative

3 SYSTEM DESIGN METHOD

In developing the system, we have conducted surveys

and interviews both in person and through online

questionnaires to prospective system users. We use

the results as the basis of the design system to develop

an expertise-based population data system.

System design is the process of developing new

system specifications based on the results of system

analysis recommendations. The objectives of system

design are:

a. Meeting the needs of users of the system (users),

such as designing a decision support system to

help find population data based on expertise

mapped based on the address of the residence in

question.

iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science

614

b. Provide an overview through flowcharts to build

Applications Population Data Cluster based on

Expertise in Real-time in Bitung city.

After the population data collection is completed, we

must determine the criteria that will be used as a

reference in decision making. The following is a table

that contains 6 criteria used to make decisions

through the SAW method.

Table 1: Table of Criteria Terms.

Criteria

Code

Criteria

Weight

Informatio

1 2

3 4

5 6

C1 C2 C3

C4 C5 C6

Wages

Work

experience

Distance

Education

Age

Skill

10 25 15 15

10 25

Cost

Benefits

Cost

Benefits

Cost

Benefits

The value of each criterion is as follows, referring to

the residents' answers in the Application evaluation

section Population Data Cluster based on Expertise in

Real-time in Bitung city.

1. Salary Criteria

Table 2: Value of Salary Criteria.

Wages Criteria Value

1 2 3

4 5

< 1M

>1 M & <= 2 M >2 M

& <= 3 M > 3 M & <=

4 M > 4 M

1 2 3 4 5

2. Work Experience Criteria

Table 3: Value of Work Experience Criteria.

No Work experience Criteria Value

There is not any yet

<= 1 year

> 1 Year & <= 3 Years >

3 Years & <= 5 Years > 5

Years

1 2 3 4 5

3. Distance Criteria

Table 4: Distance Criteria Value.

No Distance (km)

Criteria

Value

1 <=2 1

>2 & <=5

>5 & <=8

>8 &

<=15 >15

4. Education Criteria

Table 5: Value of Education Criteria.

No Certificate Criteria Value

1 2

3 4

Junior high school

Senior High School

Diploma I/II/III

S1 / Diploma IV S2 /

1 2 3 4 5

5. Age Criteria

Table 6: Age Criteria Value.

No Age (Years)

Criteria

Value

1 2 3

4 5

<=17

>17 & <=20 >20 & <=30

>30 & <=40 >40

1 2 3 4 5

6. Expertise Criteria

Table 7: Value of Expertise Criteria.

No Certificate Type

Criteria

Value

1 2 3 There is not any Training

Certificate Certificate of

expertise

1 2 3

Several analytical tools are needed to assist in

conducting the analysis in this study, including:

a. Flowchart used to analyze systems and programs

b. Entity Relationship Diagram (ERD) is used to

analyze the relationship between entities in the

system to be built

Both of them is shown in Figure 1 and Figure 2.

4 RESULT AND DISCUSSION

After the population data is obtained, the next step in

the settlement using the SAW method is to convert

the answers from the residents as the value of each

alternative (people who have filled out the evaluation

form) on each predetermined criterion. Considering a

large amount of data, we only took 4 alternative

samples to explain the SAW method calculation.

Development of Population Data Cluster Application based on Real-time Expertise

615

Figure 1: System design.

Figure 2: Entity relationship diagram.

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Table 8: Conversion of population values.

Alternative

C1 C2 C3 C4 C5 C6

Resident 1 2 1 4 5 2 1

Resident 2 3 3 3 5 3 3

Resident 3 4 2 1 4 5 2

Resident 4 5 5 2 3 4 1

After the value of the suitability rating of each

alternative on each criterion is determined in Table 8,

it is obtained decision matrix X with the following

data:

Next, normalizing the X matrix is obtained by

calculating the rij normalized performance rating

value from the Ai attribute on the Cj attribute based

on an equation that is adjusted to the type of attribute

(benefit/cost). Because each weight n. The value

given to each criterion is a matching value (the most

significant value is the best), then all the criteria given

are assumed to be profit criteria with equations. The

results of the normalized matrix R can be seen as

follows:

After normalization, the next step is to determine

the ranking value of the alternatives using the SAW

method. By using the formula, the preference value

for each alternative (Vi) can be seen in Table 9.

Table 9: Calculation Value of Ranking Process.

Alternative Results Rank

Resident 1

Resident 2

Resident 3

Resident 4

0.52

0.73

0.63

0.59

The most significant value is in Resident 2, the

alternative chosen as the best alternative with a result

of 0.73. This ranking will be used in the Application

Search feature to display population expertise data

based on a specified location. The data is presented in

tabular form with resident data ranking based on the

calculation of the SAW method. The initial view of

the Cluster Application Population Based on

Expertise in Real-Time in Bitung City can be seen in

Figure 3, while the display of the population data

search feature based on expertise according to the

specified location can be seen in Figure 4.

Figure 3: The initial view of the Population Data Cluster

Application Based on Expertise.

Figure 4: Display of the population data search page based

on expertise and location of residence.

5 CONCLUSIONS

The Population Cluster Application Based on Real-

Time Expertise in Bitung City was built to provide

integrated and valid population data to support

sustainable development in the city of Bitung in

particular. This application uses the Simple Additive

Weighting (SAW) method to rank population data

according to predetermined criteria, including salary

criteria, work experience, distance, education, age,

and expertise. The Search feature in the application

can display population expertise data based on a

specified location. The data is presented in tabular

form with population data ranking based on the

calculation of the SAW method.

ACKNOWLEDGEMENTS

We thank you very much for the Manado State

Polytechnic that has facilitated and financed this

research activity and to all those who have been

Development of Population Data Cluster Application based on Real-time Expertise

617

involved in assisting the completion of research

activities, we would like to thank you.

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