Recommendation of SAW “Simple Additive Weighting” Model Employee

Acceptance Decision Support System with Analysis Regression

M. Z. Yumarlin

, Ryan Ari Setyawan

, Jemmy Edwin B.

, Sri Rahayu

and Nur Fitri Asih

Informatics Engineering Department, Faculty of Engineering, Janabadra University

Keywords:

Decision Support System, Employee acceptance , Simple Additive Weighting and Regression

Abstract:

Decision support system as a tool for decision makers that is integrated directly with computers provide useful

information to help make structured and unstructured decisions. This study aims to implement the SAW

(Simple Additive Weighting) Model to calculate the weights of the criteria that become benchmarks for the

feasibility of prospective applicants in providing optimal solutions. The results of correlation and regression

in building this application analysis for the sixth criteria used in the assessment of employee acceptance for

each alternative candidate there are ﬁve criteria that can be recommended in the employee acceptance decision

support system, with the result that the correlation criteria value for work experience is 0.300, Educational

Level criteria 0.253, Psychological Test criteria 0.479, criteria for the Administrative Test is 0.723 and the

criteria for the Interview Test 0.748 shows a sufﬁcient and very high relationship, while for the Age criteria

-0.112 for a weak relationship so it is not recommended.

1 INTRODUCTION

An institution or college is driven by humans who are

trained and have certain skills and have experience.

Human resources in an institution or college are very

important things to support the progress and quality

of institutions or universities in achieving their goals

(Sinaga et al., 2016).

Employees are one of the resources used as a driv-

ing force in advancing a company(Umar et al., 2018).

Employee acceptance selection is a very important

factor for the smooth process in an institution or col-

lege to ﬁll in a position that is classiﬁed as suitable

criteria for occupying a position proposed by an in-

stitution or college (Sinaga et al., 2016). In accor-

dance with the goals of institutions or universities, it

is very necessary for the process of receiving human

resources in a professional and accurate way to pro-

duce human resources that can support the quality and

success of institutions or universities.

Problems encountered in employee recruitment

are difﬁculties in determining standards that will

be used to measure selection qualiﬁcations objec-

tively(N, 2014). Difﬁculty in getting the right, hon-

est and objective selector(Hidayat, 2015), and to de-

termine applicants in accordance with speciﬁcations

do not have a standardized decision system that can

assess the feasibility of prospective job applicants in

accordance with the needs of the agency or univer-

sity(Sinaga et al., 2016). Decision is a series of activ-

ities to choose an action in solving a problem. The

act of choosing from an alternative faced based on

facts and carried out through a systematic approach

that can provide the best solution done by the leader

is called decision making(Palasara, 2017).

The study entitled The Effect of Financial Perfor-

mance on Stock Prices (Rinianty and Sukardi, ) aims

to develop theory and problem solving through sys-

tematic analysis. In managing data, the analysis used

in this paper is descriptive, in the form of hypothesis

testing using statistical tests, namely Statistical Prod-

uct and Service Solution (SPSS).

Assessment of employee soft skills by applying

four criteria has been discussed (Umar et al., 2018).

These four criteria are communication skills, ability

to work together, honesty, and interpersonal skills.

Data analysis applies the Analytical Hierarchical Pro-

cess (AHP) method, which allows mathematical cal-

culations with various criteria. The results showed the

value of the consistency ratio of 0.053 which means

less than the value of the consistency ratio used in the

AHP method that is 0.1, so the results of the calcu-

lation are valid, and can be used. This study resulted

in the competency competency skills assessment re-

quired by the company as follows: 48% of Commu-

nication, 27% of Cooperation, 16% of Honesty, and

Yumarlin, M., Setyawan, R., B., J., Rahayu, S. and Asih, N.

Recommendation of SAW “Simple Additive Weighting” Model Employee Acceptance Decision Support System with Analysis Regression.

DOI: 10.5220/0009878800900095

In Proceedings of the 2nd International Conference on Applied Science, Engineering and Social Sciences (ICASESS 2019), pages 90-95

ISBN: 978-989-758-452-7

10% of Interpersonal. The results of this study prove

that the AHP method can be used in the assessment of

employee soft skills.

In a study of the best decision making system for

employee selection using Analytic Hierarchy Process

(Komalasari, 2020) shows clear differences in priori-

tization because there is data until the 4th digit is be-

hind the comma still has the same numbers, which is

ranked 2 and 3 with the same value - equal to 0.1040

and at rank 6 and 7 with a value equal to 0.0980.

This study implements the SAW (Simple Additive

Weighting) Model to calculate the weights of the cri-

teria that become benchmarks for the feasibility of ap-

plicants in providing optimal solutions and informa-

tion as a tool for making employee acceptance deci-

sions, and knowing the relationship between the cri-

teria contained in the employees acceptance system

using Correlation and Regression analysis.

2 METHODOLOGY

2.1 Research Mechanism

For the calculation of criteria with each weight that

has been determined, this study was taken using the

calculation of Simple Additive Weighting.

The SAW method can assist in the decision-

making of a case, in a calculation that produces the

greatest preference value that will be chosen as the

best alternative. SAW method is more efﬁcient be-

cause the time needed in the calculation is shorter

(De Christin and Djamain, 2015).

The SAW method requires the process of nor-

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

be compared with all available alternative ratings.

Where determining the transformation value into a

decision matrix (X) is a value from the results of the

above match rating table made into a matrix form as

follows:





x1 x2 x3

x4 x5 x6

x7 x8 x9





(1)

The formula for normalizing it (De Christin and

Djamain, 2015) is as follows:

i j

(

i j

Maxx

i j

Minx

i j

Where r

i j

is the normalized performance rating of

the alternative A

in the attribute C

; i=1,2,...,m and

j=1,2,...,n. The preference value for each alternative

) the following.

= Σ

j=1

i j

(2)

Larger V

values indicate that alternatives A

are

more chosen.

Following is the ﬂow diagram of the calculation

using the SAW method, can be seen in ﬁgure 1 below.

Figure 1: Flowchart for calculation of SAW method

2.2 System Requirements

In this study used 6 criteria symbolized by C, that is

work experience (C1), education level (C2), age (C3),

administrative test (C4), interview test (C5), and psy-

chological test (C6). Making Alternative Data used

in this employee acceptance system are prospective

employees who submit applications. From several

applicants who submitted applications, 12 alternative

people were taken. Next is the determination of the

weight of each criterion for each Alternative Value

(Ai) in each Criteria (Ci) that has been determined.

Each component of the criteria must be given a weight

or value, according to the degree of importance, the

weight value of the criteria component is obtained

from the results of interviews related to which value

is greater or smaller.

the importance of each criterion is shown in Fig-

ure 2, judged by weights 1 to 4, where weight 1 (KP)

is less important, weight 2 (CP) is quite important,

weight 3 (P) is important and weight 4 (SP) is very

important shown in the following ﬁgure 2

Figure 2: Weighted fuzzy numbers

Recommendation of SAW “Simple Additive Weighting” Model Employee Acceptance Decision Support System with Analysis Regression

In this study decision-making will be carried out

using the Simple Additive Weighting (SAW) Model.

Suppose the data used is as in Figure 3.

Figure 3: Rating match alternatives and criteria.

Based on the alternative suitability rating table and

the above criteria, an X decision matrix can be formed

(Figure 4) as follows.

Figure 4: Alternative match matrix and criteria

1) Normalizing the matrix X to be the matrix R

based on equation

i j

(

i j

Maxx

i j

Minx

i j

The results of normalization of the X matrix are

obtained by the R matrix, presented in the following

Figure 5:

Figure 5: Matrix of normalization results

2) Perform ranking process by doing multiplica-

tion process using equation

= Σ

j=1

i j

(3)

The weight vector (W) that has been determined

is: W = [3, 4, 3, 3, 4, 4]

V1 = (3)(1) + (4)(0.75) + (3)(0.5) + (3)(0.5) +

(4)(0.75) + (4)(0.25) = 3 + 3 + 1.5 + 1.5 + 3 + 1 =

V2 = (3)(0.5) + (4)(0.25) + (3)(1) + (3)(0.75) +

(4)(0.5) + (4)(0.75) = 1.5 + 1 + 3 + 2.25 + 2 + 3 =

12.75

V3 = (3)(1) + (4)(0.75) + (3)(1) + (3)(0.5) +

(4)(0.5) + (4)(0.25) = 3 + 3 + 3 + 3 + 3 + 2 = 17

V4 = (3)(0.25) + (4)(0.75) + (3)(1) + (3)(0.5) +

(4)(0.5) + (4)(0.25) = 0.75 + 3 + 3 + 1.5 + 2 + 1 =

11.25

V5 = (3)(0.25) + (4)(0.5) + (3)(1) + (3)(0.25) +

(4)(0.5) + (4)(0.75) = 0.75 + 2 + 3 + 0.75 + 2 + 3 =

11.5

V6 = (3)(0.5) + (4)(0.75) + (3)(0.75) + (3)(0.5) +

(4)(1) + (4)(1) = 1.5 + 3 + 2.25 + 1.5 + 4 + 4 = 16.25

V7 = (3)(1) + (4)(0.25) + (3)(0.5) + (3)(0.5) +

(4)(0.75) + (4)(0.75) = 3 + 1 + 1.5 + 1.5 + 3 + 3 =

V8 = (3)(1) + (4)(1) + (3)(0.75) + (3)(0.75) +

(4)(0.5) + (4)(0.25) = 3 + 4 + 2.25 + 2.25 + 2 + 1

= 14.5

V9 = (3)(1) + (4)(0.25) + (3)(0.5) + (3)(0.5) +

(4)(0.75) + (4)(0.75) = 3 + 1 + 1.5 + 1.5 + 3 + 3 =

V10 = (3)(1) + (4)(0.5) + (3)(0.75) + (3)(0.75) +

(4)(0.5) + (4)(0.25) = 3 + 2 + 2.25 + 2.25 + 2 + 1 =

12.5

ICASESS 2019 - International Conference on Applied Science, Engineering and Social Science

V11 = (3)(0.75) + (4)(0.5) + (3)(0.75) + (3)(1) +

(4)(1) + (4)(1) = 2.25 + 2 + 2.25 + 3 + 4 + 4 = 17.5

V12 = (3)(0.75) + (4)(0.5) + (3)(0.5) + (3)(1) +

(4)(0.75) + (4)(1) = 2.25 + 2 + 1.5 + 3 + 3 + 4 = 15.75

3) From the calculation of the ﬁnal value, the

biggest value is found in V11 so that alternative V11

is the alternative chosen as the best alternative. The

following ranking for prospective applicants can be

seen in Figure 6 and Figure 7 below.

Figure 6: Ranking for prospective applicants.

Figure 7: Chart ranking prospective applicants

From the graph in Figure 7 above shows the ﬁrst

rank 17.5 for A11 and second rank 17 for A3 as a

recommendation for hiring employees

3 RESULTS AND DISCUSSION

3.1 Test System for Users

In this study, there are 4 step in the trial Implementa-

tion of the SAW Method and Regression in the Em-

ployee Acceptance decision support system, as fol-

lows:

1. The technique used in this trial uses a question-

naire

2. The trial implementation of the SAW Method and

Regression in the Employee Acceptance System

was tested at the Janabadra University Campus

3. Determine the average user rating of the system

with a Likert scale

4. Calculate the percentage of user ratings of the sys-

tem.

(a) Determining the answer score, is the answer

value that will be given by the respondent

(Sugiyono, 2017), the answer score can be seen

in Figure 8 below:

space

Figure 8: Score Answers.

(b) Calculating the ideal score, is a score used to

determine the rating scale and the number of

all answers, (Sugiyono, 2017). To calculate the

number of ideal scores (criteria) of all items,

use the following formula:

CriteriumScore = Scalevaluex

Numbero f respondents (4)

The ideal score results are presented in Figure

9 below:

Figure 9: Ideal score (Kriterium).

obtained are then entered into the rating scale

presented in Figure 10 below:

Figure 10: Skor rating scale.

Figure 11: User trials are presented.

Recommendation of SAW “Simple Additive Weighting” Model Employee Acceptance Decision Support System with Analysis Regression

space

Figure 12: Percentage of System Assessment

From the graph in Figure 12 above shows Q1 1%

average value for very good, Q2 0.87% average value,

Q3 average value 0.87%, Q4 average value 0.87%, Q5

average value average average 0.75% and Q6 average

value of the average value of 1% of the results of the

quiz assessment of users.

3.2 Results of Correlation and

Regression Analysis

1. Correlation Analysis of Relationships between

Criteria

Correlation is a statistical method that is used to

test the presence of relationships and the direction

of relationships or two variables, (Ary, ).

The results of data processing (see below) to see

the relationship between criteria for employee ac-

ceptance using SPSS 17 software, can be seen in

Figure 13 below:

Figure 13: Correlation values between criteria

Correlation (relationship) Work Experience to

Levels of Education, Age, Administrative Tests,

Interview Tests and Psychological Tests. There

are four criteria that have a Sig (Signiﬁcant)>

0.05 value which indicates a very high relation-

ship with Work Experience namely Education,

Age, Administrative Tests, Interview Tests, Psy-

chotest Tests while Age has a low relationship

with Work Experience because of the Sig (Signif-

icant) < 0.05.

2. Regression Analysis Based on the results of data

processing from each criterion as shown in ﬁgure

8, a regression (R) value of 1,000 is obtained. This

value (R) shows that the inﬂuence between inde-

pendent variables (Work Experience, Level of Ed-

ucation, Age, Administrative Test, Interview Test,

Psychological Test) with total dependent variable

(overall value) has a positive nature and has a very

strong relationship, because correlation value of

1,000. (Ary, ) And the coefﬁcient of determi-

nation (R2 Square) is 1,000. This indicates that

the overall criteria greatly affect employee accep-

tance.

Figure 14: Results of Data Processing Criteria for Regres-

sion analysis

4 CONCLUSIONS

From the research that has been done, it can be con-

cluded as explained below:

1. Decision support system application for employee

acceptance method of Simple Additive Weighting

(SAW) and Regression that is built can help in

evaluating according to predetermined criteria, so

that there is no exact ﬁnal value.

2. Based on the results of regression analysis for the

criteria used in building this application has an R-

sqaure value of 1,000, it can be recommended in

the employee acceptance decision support system

where the criteria have a very high relationship.

3. Correlation for the sixth criteria used in the as-

sessment of employee acceptance for each alter-

native candidate there are ﬁve criteria that can be

recommended in the employee acceptance deci-

sion support system, with the result that the corre-

lation criterion value for work experience is 0.300,

Educational Level criteria 0.253, Psychological

Test criteria 0.479, criteria for the Administrative

Test is 0.723 and the Criteria for the Interview

Test 0.748 shows a sufﬁcient and very high re-

lationship, while for the age criteria -0.112 for a

weak relationship so it is not recommended.

ICASESS 2019 - International Conference on Applied Science, Engineering and Social Science

REFERENCES

Ary, M. Analisis korelasi & regresi sederhana menggu-

nakan spss 17.0.

De Christin, H. and Djamain, Y. (2015). Sistem pendukung

keputusan penerimaan pegawai baru pt. pln (persero)

kantor pusat dengan menggunakan metode simple ad-

ditive weighting (saw). Jurnal Teknologi dan Infor-

masi, 5(1):11–24.

Hidayat, S. (2015). Msdm pengujian seleksi karyawan.

Komalasari, N. (2020). Sistem pendukung keputusan ke-

laikan terbang (spk2t). Jurnal Industri Elektro dan

Penerbangan, 4(1).

N, B. (2014). Manajemen perusahaan seleksi sdm.

Palasara, N. (2017). Sistem pengambilan keputusan pemil-

ihan karyawan terbaik menggunakan metode analytic

hierarchy process. Bina Insani ICT Journal, 4(1):31–

46.

Rinianty, R. and Sukardi, S. Sistem pendukung keputusan

penerimaan karyawan menggunakan metode saw pada

cv. green advertising. Creative Communication and

Innovative Technology Journal, 11(1):48–57.

Sinaga, A. R., Marbun, M., and Siregar, J. S. (2016). Sistem

pendukung keputusan penerimaan karyawan dengan

metode saw pada pt. karya sahata medan. Journal Of

Informatic Pelita Nusantara, 1(1).

Sugiyono, P. (2017). metode penelitian bisnis.(suryandari

soﬁa yustiyani, ed.)(edisi 3).

Umar, R., Fadlil, A., and Yuminah, Y. (2018). Sistem pen-

dukung keputusan dengan metode ahp untuk penila-

ian kompetensi soft skill karyawan. Khazanah In-

formatika: Jurnal Ilmu Komputer dan Informatika,

4(1):27–34.

Recommendation of SAW “Simple Additive Weighting” Model Employee Acceptance Decision Support System with Analysis Regression