Technique for Order Preference by Similarity to Ideal Solution as Decision Support Method for Determining Employee Performance of Sales Section

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International Journal of Engineering & Technology, 7 (2.14) (2018) 281-285
International Journal of Engineering & Technology
Website: www.sciencepubco.com/index.php/IJET
Research Paper
Technique for Order Preference by Similarity to Ideal Solution as
Decision Support Method for Determining Employee Performance of
Sales Section
V N S Lestari1*
, Hardianto Djanggih2
, Aan Aswari3
, Nasrun Hipan4
, A P U Siahaan5
1
Faculty of Economic and Business, Universitas Dr. Soetomo, Surabaya, Indonesia
2
Universitas Tompotika Luwuk Banggai, Sulawesi Tengah, Indonesia
3
Universitas Muslim Indonesia, Makassar, Indonesia
4
Universitas Muhammadiyah Luwuk Banggai, Sulawesi Tengah, Indonesia
5
Faculty of Science and Technology, Universitas Pembangunan Panca Budi, Medan, Indonesia
*Corresponding author E-mail: venugra@unitomo.ac.id
Abstract
Employees are the backbone of corporate activities and the giving of bonuses, job titles and allowances to employees to motivate the
work of employees is very necessary, salesman on the company very much and to find the best salesman cannot be done manually and
for that required the implementation of a system in this decision support system by applying the TOPSIS method, it is expected with the
implementation of TOPSIS method the expected results of top management can be fulfilled.
Keywords: Decision Support System, TOPSIS, Selection Sales
1. Introduction
Selection is an activity of the company to select the right employ-
ees to occupy a particular position offered by management[1]–[3].
At the same time as a tool to eliminate or to not choose other em-
ployees who seek to occupy the position offered and tailored to a
job within the organization or company [4], [5]. The selection
process is a systematic process undertaken to ensure that those
who are accepted are deemed most appropriate, either by estab-
lished criteria or by the amount required[6]–[10]. Weak selection
process implemented in the company will result in low morale,
work discipline, and loyal attitude of employees in implementing
the company's goals[11]–[13].
PT. XYZ is a company that acts as a distributor of Drug Products
from several drug suppliers, limitations faced by PT. XYZ is
choosing an accomplished salesman among those who deserve to
get the achievement title for promotion and bonuses. To perform
and simplify the selection process, then applied method of deci-
sion support system. One method of decision support system that
can be applied is TOPSIS method that apply the concept of com-
pletion with Fuzzy Multi Criteria Decision Making[14], [15].
Fuzzy Multi Criteria Decision Making is a decision making pro-
cess contained in the TOPSIS method considering several alterna-
tives and criteria in a fuzzy situation[16]–[20].
2. Methodology
Basically Decision Support System (DSS) is a further develop-
ment of computerized management information system designed
in such a way that is interactive with the wearer[15], [21], [22].
This interactive nature is intended to facilitate the integration be-
tween the various components in the decision-making process
such as procedures, policies, analytical techniques, and experience
and managerial insight to establish a flexible decision
framework[23], [24].
Decision Support System is a computer-based information system
that generates various decision alternatives to assist management
in handling various structured or unstructured problems using data
and models [25]–[33]. Computer-based word is a keyword, be-
cause it is almost impossible to build a DSS without using a com-
puter as a tool, especially for storing data and managing models.
Here are the characteristics and capabilities of decision support
systems obtained from various references[20], [34], [35]:
a. Decision Support System Characteristics
1) Support all organizational activities
2) Support multiple interacting decisions
3) Can be used repeatedly and is constant
4) There are two main components, namely data and mod-
els
5) Use both external and internal data
6) Have the ability of What-if Analysis and Goal Seeking
Analysis
7) Using some quantitative models
b. Decision Support System capabilities
1) Supporting management decision making in handling
problems semi structured and unstructured.
2) Assist managers at various levels of management, rang-
ing from upper management to lower level management.
3) Supports group and individual decision making.
4) Supports interdependent and consecutive decision-
making.
5) Supporting the stages of decision-making include intel-
ligence, design, choice, and implementation.

282 International Journal of Engineering & Technology
6) Support the various forms of decision-making and deci-
sion-making processes.
7) Ability to adapt at any time and be flexible.
8) Ease of system interaction.
9) Improving effectiveness in decision-making rather than
efficiency.
10) Easy to develop by expert users.
11) Modeling ability and decision-making analysis.
12) Ease of accessing various sources and data formats.
In addition to the various Characteristics and Abilities as noted
above, DSS also has some limitations as follows:
a. There are some management abilities and human talents that
cannot be modeled, so the models that exist in the system do
not all reflect the real problem.
b. The ability of a DSS is limited to the knowledge that it pos-
sesses (basic knowledge and basic model).
c. The processes that can be performed by the DSS usually
depend also on the software capabilities it uses.
d. DSS does not have the ability of intuition as it is owned by
humans. Because no matter how sophisticated a DSS is, it's
just a collection of hardware, software and operating systems
not equipped with the ability to think.
The TOPSIS method is based on the concept that the best chosen
alternative not only has the shortest distance from the ideal solu-
tion, but also has the longest distance from the ideal solution. This
concept is widely used in some MADM models to solve practical
decision problems[36], [37].
This is because the concept is simple and easy to understand,
computing is efficient, and has the ability to measure the relative
performance of decision alternatives in simple mathematical form.
As for the steps in solving a Multi Attribute Decision Making
(MADM) case with TOPSIS[38]:
a. Make a normalized decision matrix.
b. Make a decision matrix that is normally weighted.
c. Determine the matrix of positive ideal solutions and the ma-
trix of the ideal solution.
d. Determine the distance between the value of each alternative
with a matrix of positive ideal solutions and the ideal nega-
tive solution matrix.
e. Determine the preference value for each alternative.
TOPSIS requires performance rating of each alternative Ai
on loyal Cj normalized criteria.
3. Results and Discussion
The process of applying the Fuzzy Multi Criteria Decision Making
method in performing alternative performance appraisal as follows:
a. Weighting Criteria
Determining the ranking of each alternative, then the first deter-
mination of the importance weight of each criterion (Wj). The
determination of the importance weight of each criterion (Wj) is
formed in Table 1 below.
Table.1: Criteria Weight Value (Wj)
Criteria Weight Value
Data Sales 4
Absence 3
Number of Visits 2
b. Initial Data of each alternative
From the criterion data already started, the next step is to deter-
mine the match rating as Table 2 below:
Table.2: Alternative
Alternative
Criteria
C1 C2 C3
Alternative 1 3 2 3
Alternative 2 2 2 3
Alternative 3 4 1 3
After the initial data obtained from each alternative, then begins
calculation of Fuzzy Multi Criteria Decision Making method by
building a decision matrix. In the decision matrix, the matrix col-
umn expresses the attributes of the existing criteria, while the
matrix line represents the alternative. The decision matrix refers to
the alternative m that will be evaluated on the basis of n criteria.
Decision matrix can be seen in table 3 that is:
Table.3: Decision Matrix
Alternative
Criteria
C1 C2 C3
Alternative 1 X11 X12 X13
Next is to create a normalized R decision matrix whose function is
to minimize the range of data, with the aim of making it possible
to calculate Fuzzy Multi Criteria Decision Making.
Table.4: Matrix Normalized
Criteria
C1 C2 C3
A1
√ √ √
A2
√ √ √
A3
√ √ √
So the result of normalized matrix is seen in the following calcula-
tion:
A1 (Criteria 1)
√
√
√
(Criteria 2)
√
√
√
(Criteria 3)
√
√
√
A2 (Criteria 1)
√
√
√

International Journal of Engineering & Technology 283
(Criteria 2)
√
√
√
(Criteria 3)
√
√
√
A3 (Criteria 1)
√
√
√
A3 (Criteria 2)
√
√
√
(Criteria 3)
√
√
√
After the decision matrix has normalized the next step is to create
a weighted normalized matrix V whose elements are determined
by the formula:
Table.5: Weight Matrix Normalized
No Alternative Criteria
1 A1
2 A2
3 A3
So the results of calculations on weighted normalized matrices can
be seen in table 6.
Table.6: Results of normalized Matrices are weighted
No Alternative Criteria
1 A1 2.23 2.00 1.15
2 A2 1.49 2.00 1.15
3 A3 2.97 1.00 1.15
After performing the above stages then the next stage determines
the ideal positive solution matrix (A +) and the ideal solution (A-).
The A + value is derived from the highest value of each criterion
while the A-value is derived from the lowest value. The second
criterion, there are 3 values that are 2.23, 1.49 and 2.97, then the
highest value is 2.97 while the lowest value is 1.49, means for the
second criterion, A + = 2.97 and A- = 1.49.
The distance of the positive ideal solution is the total distance
difference between each normalized weighted matrix value with
its maximum value while the ideal negative solution distance is
the total distance difference between each weighted normalized
matrix with its minimum value. Then obtained the value of S +
and S- of each alternative as follows:
A+
1
√
( ) ( ) ( )
√
( ) ( ) ( )
√
( ) ( )
√
√
0.74
A-
1
√
( ) ( ) ( )
√
( ) ( ) ( )
√
( ) ( )
√
√
=1,24
A+
2
√
( ) ( ) ( )
√
( ) ( ) ( )
√
( ) ( )
√
√
1.48
A-
2
√
( ) ( ) ( )
√
( ) ( ) ( )
√
( ) ( )

284 International Journal of Engineering & Technology
√
√
=1
A+
3
√
( ) ( ) ( )
√
( ) ( ) ( )
√
( ) ( ) ( )
√
√
1
A-
3
√
( ) ( ) ( )
√
( ) ( ) ( )
√
( ) ( ) ( )
√
√
=1,48
After calculating S + and S- then the next step is calculating the
proximity relative to the ideal solution (C), so the result of the C
value of each alternative can be calculated as follows:
A1
=0.63
A2
=0.40
A3
=0.60
So at the value of each alternative can be sorted to know which
alternative is best.
4. Conclusion
The application of TOPSIS method to determine the best salesman
can run well, positive and negative ideal concept can give compar-
ison between each alternative of each criterion, this research is far
from good and the results obtained also only based on one method
only and for further development can be combined or compared
with other methods to obtain varying results
.
References
[1] S. Robbins, “Organizational Behavior,” in Zhurnal
Eksperimental’noi i Teoreticheskoi Fiziki, 2013, p. 676.
[2] C. Truss, A. Shantz, E. Soane, K. Alfes, and R. Delbridge,
“Employee engagement, organisational performance and individual
well-being: Exploring the evidence, developing the theory,”
International Journal of Human Resource Management, vol. 24, no.
14, pp. 2657–2669, 2013.
[3] T. Suryanto, R. Rahim, and A. S. Ahmar, “Employee Recruitment
Fraud Prevention with the Implementation of Decision Support
System,” J. Phys. Conf. Ser., vol. 1028, no. 1, p. 012055, Jun. 2018.
[4] K. Jiang, D. P. Lepak, K. Han, Y. Hong, A. Kim, and A. L. Winkler,
“Clarifying the construct of human resource systems: Relating
human resource management to employee performance,” Hum.
Resour. Manag. Rev., vol. 22, no. 2, pp. 73–85, 2012.
[5] R. Nasriyah, Z. Arham, and Q. Aini, “Profile matching and
competency based human resources management approaches for
employee placement decision support system (case study),” Asian J.
Appl. Sci., vol. 9, no. 2, pp. 75–86, 2016.
[6] A. Yanie et al., “Web Based Application for Decision Support
System with ELECTRE Method,” J. Phys. Conf. Ser., vol. 1028, no.
1, p. 012054, Jun. 2018.
[7] R. Rahim et al., “TOPSIS Method Application for Decision
Support System in Internal Control for Selecting Best Employees,”
J. Phys. Conf. Ser., vol. 1028, no. 1, p. 012052, Jun. 2018.
[8] A. Alesyanti, R. Ramlan, H. Hartono, and R. Rahim, “Ethical
decision support system based on hermeneutic view focus on social
justice,” Int. J. Eng. Technol., vol. 7, no. 2.9, pp. 74–77, 2018.
[9] Y. Rossanty, D. Hasibuan, J. Napitupulu, M. Dharma, and T. Putra,
“Composite performance index as decision support method for
multi case problem,” Int. J. Eng. Technol., vol. 7, no. 2.9, pp. 33–36,
2018.
[10] S. H. Sahir, R. Rosmawati, and R. Rahim, “Fuzzy model tahani as a
decision support system for selection computer tablet,” Int. J. Eng.
Technol., vol. 7, no. 2.9, pp. 61–65, 2018.
[11] T. Simanihuruk et al., “Hesitant Fuzzy Linguistic Term Sets with
Fuzzy Grid Partition in Determining the Best Lecturer,” Int. J. Eng.
Technol., vol. 7, no. 2.3, pp. 59–62, 2018.
[12] M. D. T. P. Nasution et al., “Decision Support Rating System with
Analytical Hierarchy Process Method,” Int. J. Eng. Technol., vol. 7,
no. 2.3, pp. 105–108, 2018.
[13] A. Indahingwati, M. Barid, N. Wajdi, D. E. Susilo, N. Kurniasih,
and R. Rahim, “Comparison Analysis of TOPSIS and Fuzzy Logic
Methods On Fertilizer Selection,” Int. J. Eng. Technol., vol. 7, no.
2.3, pp. 109–114, 2018.
[14] P. harliana and R. Rahim, “Comparative Analysis of Membership
Function on Mamdani Fuzzy Inference System for Decision
Making,” J. Phys. Conf. Ser., vol. 930, no. 1, p. 012029, Dec. 2017.
[15] R. Risawandi and R. Rahim, “Study of the Simple Multi-Attribute
Rating Technique For Decision Support,” Int. J. Sci. Res. Sci.
Technol., vol. 2, no. 6, pp. 491–494, 2016.
[16] G. Kabir and M. A. A. Hasin, “Comparative analysis of TOPSIS
and Fuzzy TOPSIS for the evaluation of travel website service
quality,” Int. J. Qual. Res., vol. 6, no. 3, pp. 169–185, 2012.
[17] B. (Kiran) Bulgurcu, “Application of TOPSIS Technique for
Financial Performance Evaluation of Technology Firms in Istanbul
Stock Exchange Market,” Procedia - Soc. Behav. Sci., vol. 62, pp.
1033–1040, 2012.
[18] A. M. Yaakob and A. Gegov, “Interactive TOPSIS Based Group
Decision Making Methodology Using Z-Numbers,” Int. J. Comput.
Intell. Syst., vol. 9, no. 2, pp. 311–324, 2016.
[19] A. Łatuszyńska, “Multiple-Criteria Decision Analysis Using Topsis
Method For Interval Data In Research Into The Level Of
Information Society Development,” Folia Oeconomica Stetin., vol.
13, no. 2, pp. 63–76, Jan. 2014.
[20] C. H. Primasari, R. Wardoyo, and A. K. Sari, “Integrated AHP,
Profile Matching, and TOPSIS for selecting type of goats based on
environmental and financial criteria,” Int. J. Adv. Intell. Informatics,
vol. 4, no. 1, pp. 28–39, Mar. 2018.
[21] A. A. Esfahani et al., “An evaluation model for the implementation
of hospital information system in public hospitals using multi-
criteria-decision-making (MCDM) approaches,” Int. J. Eng.
Technol., vol. 7, no. 1, p. 1, Dec. 2017.
[22] M. Min, “A rule based expert system for analysis of mobile sales
data on fashion market,” in 2013 International Conference on
Information Science and Applications, ICISA 2013, 2013.
[23] D. Napitupulu, M. Syafrullah, R. Rahim, A. Amar, and Y. Sucahyo,
“Content validity of critical success factors for e-Government

International Journal of Engineering & Technology 285
implementation in Indonesia,” IOP Conf. Ser. Mater. Sci. Eng., vol.
352, p. 012058, May 2018.
[24] K. Adiyarta, D. Napitupulu, H. Nurdianto, R. Rahim, and A. Ahmar,
“User acceptance of E-Government Services Based on TRAM
model,” IOP Conf. Ser. Mater. Sci. Eng., vol. 352, p. 012057, May
2018.
[25] J. Papathanasiou, N. P. B, T. Bournaris, and B. Manos, “A Decision
Support System for Multiple Criteria Alternative Ranking Using
TOPSIS and VIKOR : A Case Study on Social Sustainability in
Agriculture,” ICDSST, vol. 2, pp. 3–15, 2016.
[26] R. Rahim, A. S. Ahmar, A. P. Ardyanti, and D. Nofriansyah,
“Visual Approach of Searching Process using Boyer-Moore
Algorithm,” J. Phys. Conf. Ser., vol. 930, no. 1, p. 012001, Dec.
2017.
[27] R. Rahim, I. Zulkarnain, and H. Jaya, “A review: search
visualization with Knuth Morris Pratt algorithm,” in IOP
Conference Series: Materials Science and Engineering, 2017, vol.
237, no. 1, p. 012026.
[28] P. He, L. Chen, and X. H. Xu, “Fast C4.5,” in Proceedings of the
Sixth International Conference on Machine Learning and
Cybernetics, ICMLC 2007, 2007, vol. 5, pp. 2841–2846.
[29] R. Rahim, S. Nurarif, M. Ramadhan, S. Aisyah, and W. Purba,
“Comparison Searching Process of Linear, Binary and Interpolation
Algorithm,” J. Phys. Conf. Ser., vol. 930, no. 1, p. 012007, Dec.
2017.
[30] R. Rahim, I. Zulkarnain, and H. Jaya, “Double hashing technique in
closed hashing search process,” IOP Conf. Ser. Mater. Sci. Eng.,
vol. 237, no. 1, p. 012027, Sep. 2017.
[31] D. U. Sutiksno, A. S. Ahmar, N. Kurniasih, E. Susanto, and A.
Leiwakabessy, “Forecasting Historical Data of Bitcoin using
ARIMA and α-Sutte Indicator,” J. Phys. Conf. Ser., vol. 1028, no. 1,
p. 012194, 2018.
[32] A. S. Ahmar, A. Rahman, A. N. M. Arifin, and A. A. Ahmar,
“Predicting movement of stock of „Y‟ using sutte indicator,”
Cogent Econ. Financ., vol. 5, no. 1, 2017.
[33] N. Kurniasih, A. S. Ahmar, D. R. Hidayat, H. Agustin, and E. Rizal,
“Forecasting Infant Mortality Rate for China: A Comparison
Between α-Sutte Indicator, ARIMA, and Holt-Winters,” J. Phys.
Conf. Ser., vol. 1028, no. 1, p. 012195, 2018.
[34] Ep. E.P. and S. R, “Big data management with machine learning
inscribed by domain knowledge for health care,” Int. J. Eng.
Technol., vol. 6, no. 4, p. 98, Sep. 2017.
[35] J. Shang, P. R. Tadikamalla, L. J. Kirsch, and L. Brown, “A
decision support system for managing inventory at
GlaxoSmithKline,” Decis. Support Syst., vol. 46, no. 1, pp. 1–13,
2008.
[36] P. Karande, E. K. Zavadskas, and S. Chakraborty, “A study on the
ranking performance of some MCDM methods for industrial robot
selection problems,” Int. J. Ind. Eng. Comput., vol. 7, no. 3, pp.
399–422, 2016.
[37] S. H. Zanakis, A. Solomon, N. Wishart, and S. Dublish, “Multi-
attribute decision making: A simulation comparison of select
methods,” Eur. J. Oper. Res., vol. 107, no. 3, pp. 507–529, 1998.
[38] A. Zadeh Sarraf, A. Mohaghar, and H. Bazargani, “Developing
TOPSIS method using statistical normalization for selecting
knowledge management strategies,” J. Ind. Eng. Manag., vol. 6, no.
4, pp. 860–875, 2013.

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