MADM Model for Evaluation of Non-permanent Teacher Performance

using Fuzzy AHP and TOPSIS Methods

Sunardi

, Salahudin Robo

and Trisno

Department of information system, Universitas Nahdlatul Ulama Nusa Tenggara Barat, Indonesia

Department of information system, Universitas Yapis Papua, Papua, Indonesia

Department of Informatika Engineering, STIMIKOM Stellamaris Sumba, Indonesia

Keywords:

Non-Permanent Teacher, MADM, FAHP, TOPSIS.

Abstract:

Teachers are a key element in the education system, especially non-permanent teachers in schools with the

current condition is very apprehensive, starting from a less obvious future, sometimes receive honorarium

after three months of duty even uncertain. This is because of the determination process for outstanding non-

permanent teachers there are still obstacles encountered there is no assessment indicator for honorary teachers.

As a result, non-permanent teachers who excel did not get an award from school. To meet these requirements

required a model ie the model Multi-Attribute Decision Making (MADM) which aims from certain criteria is

determined the best alternative from many existing criteria. In the process of MADM model AHP method is re-

quired to ﬁnd the weight of the performance of non-permanent teachers and for ranking used TOPSIS method.

All series of methods collaborate with fuzzy logic with the aim of minimizing uncertainty so hopefully, the

results obtained more accurate. The aim of this research is to determine the performance of non-permanent

teachers. The results of this study are 44.8% pedagogical criteria, personality criteria 26.1%, social criteria

16.5%, professional criteria 12.5%.

1 INTRODUCTION

Teachers are a key element in the education system,

especially in schools. All other components, from the

curriculum, infrastructures, costs, and so forth will

not mean much if the teacher interaction with unqual-

iﬁed learners. Many experts claim that in school there

will be no change or quality improvement without

changes and improvements in teacher quality. Cur-

rently, most of the teachers’ fate in Indonesia is get-

ting better and there is a change. Although it cannot

be equally perceived by the teachers especially teach-

ers who teach in remote areas, village, outer islands,

and inland areas. They are still difﬁcult to enjoy the

word prosperous let alone sufﬁcient and still many

teachers who have status as local Non-Permanent

teachers. The condition of Non-Permanent teachers

today is very alarming, starting from an unclear fu-

ture, long service period, serving in underdeveloped

regions, uncertain honorarium system, sometimes re-

ceive honorarium after three months of duty even un-

certain. The most unique thing of Non-Permanent

teachers is still carrying out its main task is that as a

permanent teacher work, namely educating, teaching,

guiding, directing learners to be human beings who

believe and cautious to Almighty God.

Teacher performance (Supiandi, 2016) has cer-

tain speciﬁcations/criteria. Teacher performance can

be viewed and measured by speciﬁcation/criteria the

competencies each teacher must possess. Based

on Regulation of the Minister of National Educa-

tion of the Republic of Indonesia Number 16 the

Year 2007 regarding Academic Qualiﬁcation Stan-

dard and Teacher Competencies. It is explained that

the Teacher Competency Standards are developed as

a whole of the 4 major competencies, that is (1) peda-

gogic competence, (2) personality, (3) social, and (4)

professional. The fourth competency integrated into

teacher performance.

Central Lombok Regency is one of the districts lo-

cated in the province of West Nusa Tenggara (NTB)

with the number public high schools as many as

18 institutions with a fairly large number of Non-

Permanent teachers. But the process of determining

for teachers achievers still found obstacles faced ie

none an assessment indicator for teachers. As a result,

teachers who excel do not get an award from school,

while the non-achievers are rewarded, this is due to

Sunardi, ., Robo, S. and Trisno, .

MADM Model for Evaluation of Non-permanent Teacher Performance using Fuzzy AHP and TOPSIS Methods.

DOI: 10.5220/0009906300980104

In Proceedings of the International Conferences on Information System and Technology (CONRIST 2019), pages 98-104

ISBN: 978-989-758-453-4

 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reser ved

the selection process non-permanent teachers are still

less effective. therefore it can have an impact on the

learning process. From the problem, the researchers

provide solutions that are performance evaluation of

non-permanent teachers in high school teachers in

central Lombok district.

Multi-Attribute Decision Making (MADM)

decision-making models that utilize and deﬁne

(Galankashi et al., 2016) the best alternative of many

alternatives based on existing criteria. There are

several methods that are often used in making the

system, such methods are Simple Additive Weighting

(SAW), Weighted Product (WP), Electre, Technique

For Others Reference by Similarity to Ideal Solution

(TOPSIS) and Analytical Hierarchy Process (AHP).

Fuzzy logic is a logic that has the value of vague-

ness (Keprate and Ratnayake, 2016) between two val-

ues. The fuzzy approach is particularly the approach

triangular fuzzy number to scale AHP is expected to

be able to minimize uncertainty (Junior et al., 2014)

so hopefully, the results obtained more accurate. (Mo-

hyeddin and Gharaee, 2014) (Salah and Saadi, 2016)

In this research method used is method FAHP

and Topsis, FAHP used to look for weight (Taylan

et al., 2014) (Yudatama and Sarno, 2015) of the per-

formance of Non-Permanent teachers and for ranking

using methods Topsis, this is because the combina-

tion of methods is the result is considered the most

Valid.(Sudiatmika et al., 2017) (Alizadeh et al., 2016)

This study was conducted with the aim to deter-

mine the performance of Non-Permanent teachers us-

ing the method FAHP dan TOPSIS in order to as-

sist the stakeholders, in this case, the Principal to

determine whether the teacher deserves an award as

an Achieving Teacher or not, thus for the future no

longer errors occur in the assessment process of Non-

Permanent teachers.

2 LITERATURE REVIEW

Currently, many researchers are doing research Non-

Permanent teachers, from several studies are consid-

ered to be still related to the topic discussed so that it

can be used as a reference source. The following are

some of the previous studies that serve as a reference.

Research conducted by (Balkis and Masykur,

2017) about understanding subjective well-being

Non-Permanent teachers, the results showed that the

three subjects enjoy their profession today, work mo-

tivation that exists on the individual gives effect on job

satisfaction, subjective well-being the three subjects

are inﬂuenced by the perspective of his profession.

In research conducted by (Meiza, 2017) discusses

the difference of happiness to the teachers of civil ser-

vants and Non-Permanent status, with the result that

there is no difference of happiness on teachers with

civil servants and Non-Permanent status, the civil ser-

vant teacher has the empirical mean the happiness

scale is in the high category, while the teacher of Non-

Permanent status has the empirical mean of happiness

that is in the high category.

In the study (Mahmudah et al., 2015) discusses the

perception of Non-Permanent teachers against Law

No. 5 of 2014 about the PPPK system, the results

of his research indicators of understanding of Non-

Permanent teachers to PPPK in the categories do not

understand to be the most. Thus, PPPK in the dissem-

ination of information is still poorly understood.

Research conducted by (Arfa et al., 2013) about

the incidence and depression rates of Non-Permanent

teachers in public primary schools, the results of the

study there were no signiﬁcant differences between

the incidence and depression level of honorary teach-

ers in public elementary schools in four sub-districts

in Kota Kotamobagu, North Sulawesi Province.

In the study (HARIWIBOWO et al., 2015) work

motivation of Non-Permanent teachers in terms of

quality of work life. The results of his research that

there is a signiﬁcant inﬂuence quality of work life

with work motivation of honorary teachers, the result-

ing inﬂuence is positive. This means that the higher

the quality of work life of a teacher, the higher the

motivation for his work.

3 METHODOLOGY

3.1 Data Collection

A combination of empirical and non-empirical meth-

ods was used for data collection in this study. An em-

pirical study approach is used to collect primary data

and non-empirical approaches are used to collect sec-

ondary data.

3.1.1 Primary Data

Primary data is data generated from questionnaires

and interviews obtained from the supervisors who are

incorporated in Musyawarah Kerja Pengawas Sekolah

(MKPS) and headmaster. In this study, questionnaires

were prepared based on the research model and used

as a primary measuring tool.

MADM Model for Evaluation of Non-permanent Teacher Performance using Fuzzy AHP and TOPSIS Methods

3.1.2 Secondary Data

Secondary data is data collected from literature re-

view such as books, journals, articles. To determine

the concept relevant to this study and create a research

framework.

Figure 1: Research Steps

AHP displayed in the form of a hierarchical model

of purpose, criteria and some level subcriteria. This

method is built on three principles namely principles

for building hierarchy, principles for setting priorities

and a principle of logical consistency. AHP frame-

work that is ﬂexible and effective can help a person in

making decisions. Because all parts of the hierarchy

are interconnected, then it can look related, one fac-

tor may affect other factors. Hierarchy is an efﬁcient

way of solving complex systems a linear structure in

which the inﬂuence is distributed from top to bottom.

efﬁcient because the problem will be more structured,

organized, and functional in controlling and reducing

information into the system.

Figure 2: Hierarchical Structure of Performance Evaluation

of Non-Permanent Teachers

3.2 Multiple Criteria Decision Making

(MCDM)

Multiple Criteria Decision Making (MCDM)(Hanine

et al., 2016) is a method of decision making to de-

termine the best alternative from a number of alter-

natives based on certain criteria.(Vinodh et al., 2014)

Criteria are usually sizes, rules or standards used in

decision making. Based on the purpose, MCDM

can be divided into 2 models (Khademolqorani and

Hamadani, 2013): Multi-Attribute Decision Mak-

ing (MADM), and Multi-Objective Decision Making

(MODM). MADM used to solve problems in discrete

space. Therefore, in MADM is usually used to per-

form the assessment or selection of several alterna-

tives in limited quantities. While MODM used to

solve problems in continuous space. In general, it can

be said that MADM selects the best alternative from

a number of alternatives, while MODM designed the

best alternative. Basically, the MADM process is

done through 3 stages the preparation of the compo-

nents of the situation, analysis, and synthesis of infor-

mation. At the component compilation stage, a com-

ponent of the situation will be formed table containing

the estimates identiﬁcation of alternatives and objec-

tives speciﬁcations, criteria and attributes.

3.3 Teacher Performance Evaluation

Teacher performance evaluation is a process that aims

to know or understand teacher performance levels one

with another teacher performance level or compared

to predeﬁned standards. Teacher performance evalu-

ation has beneﬁts for schools because this assessment

will provide the level of achievement of the standard,

the size or criteria set by the school. So the weak-

nesses that exist in a teacher can be addressed and will

provide feedback to the teacher. Teacher performance

evaluation is not meant to criticize and ﬁnd fault, but

as an incentive for teachers to develop themselves be-

come more professional and eventually later will im-

prove the quality of education of learners.

3.4 Metode Analythic Hierarchy

Process (AHP)

Analytical Hierarchy Process (AHP) is a deci-

sion support model developed by Thomas L.

Saaty,(Prakash and Barua, 2015). the concept of

changing qualitative values into quantitative values so

that the decisions taken can be more objective. Basi-

cally, the AHP method breaks down a complex situa-

tion and unstructured into its component parts. Then

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100

organize this part or variable in a hierarchical arrange-

ment and gives numerical values on subjective consid-

erations about the relative importance of each variable

which one has the highest priority and acts for the ef-

fect the outcome of the situation. AHP is used to solve

problems by changing them in the form of hierarchy

and deﬁning all of the problems. The process of de-

veloping a hierarchy of problems with AHP depends

on experience, knowledge, logic, and imagination to

give consideration.

Figure 3: AHP Comparative Assessment Scale (Source:

Saaty(1994)).

Suppose criterion X has some elements below it,

ie C1, C2, ..., Cn. Table matrix pairwise comparison

based on criterion C as follows:

Figure 4: Matched Comparison Matrices.

What is measured in AHP is the ratio of consis-

tency by looking at the consistency index. The ex-

pected consistency is near perfect in order to produce

decisions that are close to valid. Although it is dif-

ﬁcult to achieve perfect, the consistency ratio is ex-

pected to be less than or equal to 10 %. (Saaty, 2002)

CI =

λmax − n

n − 1

(1)

Where CI = Index consistency, λ Maks = The

biggest eigenvalues are obtained by adding up the re-

sult of multiplying the number of columns with the

main vector eigen. CR = consistency ratio, that is data

that has less than or equal CR 10% which is consid-

ered consistent.

CR =

(2)

The following table Random Index (RI)

Figure 5: Random Index (RI).

3.5 Triangular Fuzzy Number (TFN)

F-AHP is a combination of AHP method with fuzzy

concept approach. F-AHP includes weaknesses found

in AHP, namely problems with more subjective crite-

ria. Number uncertainty is represented by a sequence

of scales (Keprate and Ratnayake, 2016), (Salah and

Saadi, 2016). To determine the level of member-

ship in F-AHP, function rules are used in Triangular

Fuzzy Numbers (TFN) which are arranged based on

linguistic sets (Mohyeddin and Gharaee, 2014), (Ju-

nior et al., 2014). So, the number at the level of in-

tensity of interest in AHP is changed to the speciﬁed

TFN scale. So, the numbers at the level of interest in-

tensity are presented by Saaty (1980), changed to the

speciﬁed TFN scale

Figure 6: Function of Fuzzy Scale Membership (Source:

Chang, D.Y. (1992)).

Furthermore, given the rules of operation com-

monly used triangular fuzzy number arithmetic. Sup-

pose there are 2 TFNs : M1 = (l1, m1, u1) and M2 =

(l2, m2, u2), apply

M1 ⊕ M2 = (l1 + l2,m1 + m2,u1 + u2) (3)

M1  M2 = (l1 − l2,m1 − m2,u1 − u2) (4)

M1 ⊗ M2 = (l1.l2, m1.m2, u1.u2) (5)

λ ⊗ M2 = (λ.l2,λ.m2,λ.u2) (6)

M1 − 1 = (1/u1,1/m1,1/l1) (7)

From the fuzzy triangular matrix determined the

value fuzzy synthetic extents for each criteria (Chang,

D. Y. 1996).

= ⊕

j=1

⊗ [⊕

i=1

⊕

j=1

]

−1

(8)

MADM Model for Evaluation of Non-permanent Teacher Performance using Fuzzy AHP and TOPSIS Methods

101

To get the value of each priority criterion, it is ob-

tained by calculating the normalization of the weight

vector and the minimum value d’l = min V (Si≥Sk)

which compares the value of fuzzy synthetic extent

(Si≥Sk).

W = (d1,d2,...,dn)T (9)

With the formulation of normalization is :

i=1

(10)

for l = 1,2,3. . . ,n .

3.6 Technique for Order Preference by

Similarity to Ideal Solution

(TOPSIS)

Technique for Order Preference by Similarity to Ideal

Solution (TOPSIS) is a multicriteria decision-making

method (Primasari and Setyohadi, 2017) which was

ﬁrst introduced by Yoon and Hwang in 1981. The ba-

sic idea of this method is that the alternative is chosen

have the closest distance to a positive ideal solution

and the furthest from the negative ideal solution (Su-

diatmika et al., 2017). TOPSIS pays attention to the

distance to positive ideal solutions or the distance to

the negative ideal solution by taking a close relation-

ship to an ideal solution. Alternatives that have been

ranked can be used as a reference for decision-makers

to choose the best solution

Here are the steps of the TOPSIS method (Source:

Hwang and Yoon 1981):

3.6.1 Build a Normalized Decision Matrix

Elemen rij the result of the normalization of deci-

sion matrix R with the Euclidean length of a vector

method:

i j

i=1

i j

;withi = 1,2,3,...m; and j = 1,2,3,...n.

(11)

3.6.2 Build a Weighted Normalized Decision

Matrix

The ideal solution positif A+ and ideal negative solu-

tions A- can be determined by a normalized weighted

rating (yij) as:

yi j = wiri j;withi = 1,2,3,...m;and = 1,2,3,...n

(12)

3.6.3 Determine the ideal Solution Matrix and

the Ideal Negative Solution Matrix

The positive ideal solution (A+ )is calculated on the

basis of:

= (E1

,Y 2

,Y 3

,. . . .,Y n

) (13)

The ideal solution (A-) is calculated on the basis

of:

−

= (E1

−

,Y 2

−

,Y 3

−

,. . . .,Y n

−

) (14)

3.6.4 Determine the Distance between the Values

of Each Alternative with a Matrix of

positive Ideal Solutions and a Negative

Ideal Matrix

The distance between Ai alternatives with positive

ideal solutions is deﬁned as:

j=1

i j

− y

)

;i = 1,2,3,...,m. (15)

The distance between alternatives Ai with the

ideal solution is deﬁned as:

−

j=1

i j

− y

−

)

;i = 1,2,3,...,m. (16)

3.6.5 Determine the Preference Value for Each

Alternative

The proximity of each alternative to the ideal solution

is calculated based on the formula:

V =

−

+ Di

;i = 1,2,3,...,m. (17)

4 RESEARCH FINDINGS AND

DISCUSSIONS

4.1 Matched Comparison Matrix

among the Main Criteria

At this stage the superintendent ﬁlls out the ques-

tionnaire by choosing numbers 1 through 9 to ﬁnd

which criteria are the most important of the four cri-

teria, after which the principal ﬁlled out a question-

naire of grades 1 to 4 for the overall value earned

by non permanent teachers which are ranked among

teachers, with respondents in this activity are respon-

dents who are considered experts in this ﬁeld. Re-

spondents who ﬁll this data are 12 supervisors from

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Musyawarah Kerja Pengawas Sekolah (MKPS) and

18 Principals. Based on data obtained from the

School and Musyawarah Kerja Pengawas Sekolah

(MKPS), there are four main criteria, namely peda-

gogic criteria, personality criteria, social criteria, pro-

fessional criteria. So there are four elements to be

compared. Calculation and determination of consis-

tency for the main comparison.

Figure 7: Comparison Matrix Under Criteria.

Figure 8: Normalization of Matrices.

From the comparison matrix table above, obtained

value CI = 0.087, RI = 0.90, and value CR = 0.097.

According to Saaty, if CR≤10% then the pairwise

comparison matrix is consistent. Consistent means

that all elements have been grouped homogeneously

and the relation between criteria mutually justiﬁes

logically.

4.2 Weight with Fuzzy

Figure 9: Weighting with Fuzzy AHP.

The next step is to normalize on fuzzy synthetic

extends are listed in Figure 10.

Figure 10: Normalization of Fuzzy Synthetic Extend.

Then calculated the weight and normalization of

weight vector so that we know the value of the weight

of the main criterion. W’ = (1, 1, 0.792, 0.812)

After the normalization, the ﬁnal weight of the

main criteria is as follows.

W = (0.277, 0.277, 0.219, 0.225)

space

Figure 11: Level of interest Criteria

From the results of priority weighting analysis on

the main criteria using FAHP obtained criteria ped-

agogic has a weight of 44.8%, personality criteria

26.1%, social criteria 16.5% and professional criteria

12.5%.

4.3 Alternative Ranking

In this research, determining the ranking of each

candidate’s alternative non-permanent teacher using

TOPSIS method calculation. So get the ranking as

follows:

Figure 12: Ranking Alternative With TOPSIS.

ManualResult =

Value

ΣCriteria

x100% (18)

Figure 13: Ranking Alternative With Manual.

5 CONCLUSIONS

Based on the results of manual calculations and cal-

culation using FAHP and TOPSIS methods with the

same value obtained the difference in results is 12.75

and 66.03.

MADM Model for Evaluation of Non-permanent Teacher Performance using Fuzzy AHP and TOPSIS Methods

103

The results of this study can be summarized by us-

ing method FAHP and TOPSIS can be used as an indi-

cator and is expected to solve the existing problems in

the performance assessment of non-permanent teach-

ers. The results of this calculation serve as a reference

by principals in determining the performance of non

-permanent teachers and the ﬁnal decision remains on

the principal.

In the decision-making process involving many

criteria, the Fuzzy AHP method can be used to de-

termine the priority weight on each of the criteria

on which the appropriate decision analysis is based.

From the results of priority weighting analysis on the

main criteria with Fuzzy AHP, pedagogic criteria have

a weight of 44.8%, personality criteria 26.1%, social

criteria 16.5%, and professional criteria 12.5%. The

use of the TOPSIS method is used for determining the

weight of each alternative ranking of candidate non-

permanent teachers

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