Journal International Inspire Education Technology (JIIET), 1.
- December 2022 138-144 Implementation of the Mamdani Fuzzy Method to Evaluate the Performance of Lecturers in the Research Field Alwendi1.
Khairunnisa Samosir2.
Mingliang Suo3.
Fan Yang4 1Universitas Graha Nusantara Padangsidimpuan.
Indonesia E-mail: alwendi60@gmail.
2Universitas Graha Nusantara Padangsidimpuan.
Indonesia E-mail: ksamosir35@gmail.
3School of Astronautics.
Harbin Institute of Technology.
China E-mail: buaasuozi@hotmail.
4University of Burgundy.
Francis E-mail: : fanyang@ubourgogne.
Corresponding author: alwendi60@gmail.
AbstractAi Today, information technology, especially soft computing technology, has grown very rapidly.
One of the soft computing technologies that has been widely developed is fuzzy logic, because it can be used to measure various phenomena that are ambiguous, disguised or fuzzy.
One of the research topics that uses the application of fuzzy logic is the assessment system in the field of research.
Research by Lecturers at the University of Graha Nusantara Padangsidimpuan in Simlitabmas Data Still in the Guidance Category to upgrade to the Middle Category UGN Padangsidimpuan lecturers are challenged to be able to develop, devote, and apply the knowledge needed in research.
For that we need an application that can be used to calculate and record the performance of Lecturers on the resulting Research.
The purpose of this study is to apply fuzzy logic with the Mamdani method in assessing the research performance of lecturers at the University of Graha Nusantara Padangsidimpuan.
This research uses Mamdani Fuzzy Logic.
Fuzzy Mamdani method is a way to map an input space into an output space.
This method is a mathematical framework used to represent uncertainty, ambiguity, imprecision, lack of information, and partial truth.
The stages of research using the Mamdani method are Creating Input Variables taken from Sinta-accredited Articles.
Simlitabmas Grant Articles and Articles in International and National Journals.
Finding the Max-Min Value of Each Variable.
Creating a fuzzy set using the Mamdani method.
Creating Assertions with Defuzzification using Matlab.
KeywordsAi Fuzzy Logic.
Mamdani Method.
Research Field Manuscript received Dec 29, 2022.
revised Dec 4, 2022.
accepted Dec 6, 2022.
Date of publication 17 Dec.
Journal International Inspire Education Technology (JIIET) is licensed under a Creative Commons Attribution-Share Alike 4.
0 International License.
How to cite:
Published by:
Alwendi.
Alwendi.
Samosir.
Suo.
Yang.
Implementation of the Mamdani Fuzzy Method to Evaluate the Performance of Lecturers in the Research Field.
Journal International Inspire Education Technology (JIIET), 1.
, 138-144.
https://doi.
org/10.
55849/jiiet.
Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar
INTRODUCTION
According to Junaidi et al .
, teaching staff is an important component to enter high-quality lecturers, therefore to become teaching staff must have high abilities in accordance with these limitations (Wawan et al.
, 2.
Meanwhile.
Universitas Graha
Nusantara
(UGN)
Padangsidimpuan has a vision of becoming a superior, independent and nationally competitive higher lecturer by 2030 (Rustum et al.
, 2.
realize this vision.
UGN has one of its missions, namely Organizing education, research and community service, as well as conducting studies and periodic studies.
(Sadi, 2.
so that the UGN faculty can develop, dedicate and apply their knowledge in research so that it can be upgraded to the intermediate category.
(Devaraj et al.
, 2.
For this reason, an application is needed that can be used to calculate the lecturer's (Hardianto & Nurhasanah, 2.
performance score on the research he produces (Budi Indra Gunawan & Unan Yusmaniar Oktiawati, 2.
To make it easier to see the performance of lecturers (Sofhian et al.
from the research results they produce, the researchers use fuzzy logic applications.
(Anisah et , 2.
Various theories of the development of fuzzy logic show that fuzzy logic can be used to model various systems in general.
(Rodryguez et al.
To evaluate research performance, researchers used 3 input variables, namely through Sinta.
Simlitabmas, and the journal publication (Izvozchikova et al.
, 2.
To get these results, we need to go through the stages of fuzzy set construction, application of implication functions, and compilation of rules.
(Acosta-Prado et al.
, 2.
The results obtained indicate that the evaluation of academic results using fuzzy logic (Abbasipayam & Makrova, 2.
can show differences in the final value of lecturer research (Ain et al.
, 2.
To obtain these results, it is necessary to carry out the stages of forming a fuzzy set, applying the implication function and compiling rules (Li et al.
, 2.
The results obtained indicate that the assessment of academic (Alwendi, 2.
performance using fuzzy logic can show differences (Baliuta et al.
, 2.
in the final value of lecturer research activities.
(Tariq et al.
Based on the explanation contained in the background (Tahri et al.
, 2.
a problem can be formulated, namely, (Aslam, 2.
how to determine the value of lecturer research (Alwendi & Masriadi, 2.
using the Fuzzy Mamdani (Yetilmezsoy et al.
, 2.
method as an alternative tool to calculate the value of lecturer research (Keviczky et al.
, 2.
at Graha Nusantara Padangsidimpuan University.
(Ningrum et al.
, 2.
Knowing the results obtained from the results of calculations using fuzzy logic to assess the performance of lecturers 3.
Knowing the use of the Mamdani fuzzy logic method in evaluating the research .
an Krieken et al.
, 2.
activities of lecturers at Graha Nusantara Padangsidimpuan University in 2020-2021.
Provide knowledge about how to determine the evaluation of research activities A lot of research has demonstrated the ability of fuzzy logic (Mudia, 2.
in dealing with vague and uncertain linguistic information.
For the purpose of representing human perception, fuzzy logic (Rani Roopha Devi & Mahendra Chozhan, 2.
has been employed as an effective tool in intelligent decision making.
Due to the emergence of various studies(Ain et al.
, 2.
on fuzzy logicbased decision-making methods (Mittal et al.
, 2.
Faculty Graha Nusantara Padangsidimpuan University using the Fuzzy Mamdani method .
An ascending linear representation that represents the increase in the set, starting from the domain value which has zero membership degree (Wu & Xu, 2.
, moving to the right to the domain value which has the same higher degree of membership.
Fig 1.
Linear representation a descending linear Representation is the inverse of the first.
A straight line starts from the left of the domain value with the highest membership degree, then decreases to the domain value with the lowest membership (Kimura et al.
, 2.
Fig 2.
Decreasing Linear Representation II.
METHOD
The steps carried out in this study are shown in Figure 3 below.
Data collection Data identification System test At the system testing stage, testing and simulation will be carried out to evaluate lecturer research activities using fuzzy logic.
Data processing i.
RESULTS AND DISCUSSION
This study examines the Mamdani fuzzy method in evaluating research activities of the Faculty of Based on the research steps in Figure 3, each step Graha Nusantara Padangsidimpuan University can be explained as follows:
using an application built with Matlab R2013a Data collection The data needed for this software.
In this study, it consists of 3 input research is research data from the Faculty of variables, namely the variables that are used as Graha Nusantara University in 2020 and 2021 evaluation materials, which include variables from which is taken from data from the Sinta cluster.
Sinta.
Simlitabmas and Klater Jurnal, which will Simlitabmas and journal publications.
provide 33 rules or 27 rules.
The fuzzy set for the Data identification Data identification is done to input and output variables is presented in Table 1 select the variables needed to perform below:
calculations and analyze the problem.
Data processing The data processing stage is to create a Mamdani fuzzy system using Matlab
Table 1
Formation of fuzzy sets for variables Input and output variables System testing Function
Input
Sinta
Variable C1-C3
S6-S0
Simlitabmas RAP natural resources Journal Cluster national journal International Journal Output Cluster PT In the mamdani method, the implication function used for each rule is a minimum After determining the membership function of a variable, fuzzy logic rules are Based on the available data, fuzzy rules Universe Of Talk Fuzzy Set a little Currently a little Currently a little Currently a little Currently a little Currently a little Currently a little Currently a little Currently a little Currently can be formed.
Rules of analysis Evaluation of lecturers' scientific activities in terms of input variables and output variables As in table 2 Table 2 Derivatives of Final Stage Evaluation Variables Rule Entrance A little A little a little Currently Currently Currently A little Currently a little Currently a little Currently GO OUT Ie Ie Ie Ie Ie Ie Ie Ie Ie In this study, the discussion process was carried out in several stages, namely:
Determine the input variables taken from the lecturer's research assessment data, where the variables used are the sinta variable, the simlitabmas variable, and the journal cluster variable.
Fuzzification: determining the degree of membership of the input and output Fuzzy logic operations must be performed if the previous part of more than one statement performs fuzzy logic operations.
The final result of this operation is the degree of truth of the antecedent, which is a single number.
Fuzzy operators to perform operations and and or can be made Implication: Apply the implication method to determine the final form of fuzzy set The consequence or inference of a fuzzy rule is determined by filling the output of the fuzzy set with the output The implication function used is Min.
Aggregation: The process of combining the outputs of all if-then rules into one fuzzy set using the Max function.
DefuzzificationThe inference process in the application of fuzzy statements uses the MIN implication function.
In addition, the composition of all fuzzy outputs is done using max.
Then do validation or called defuzzification using Centroid.
In this method, a crisp solution is obtained by taking the center point of the fuzzy area as Final Score A little A little Currently A little Currently Currently The validation of research evaluation data for graha nusanatara university lecturers using the mamadani method can also be done using the matlab fuzzy toolkit version R2013a.
This software serves to interpret the variables of lecturer research activities Fig 4.
Input and output variables of the Mamdani method This study has 3 input variables and 2 output The input variables consist of Sinta.
Simlitabmas, and cluster.
2020 and 2021.
While the minor and interim release variables.
This can be seen in Figure 4.
Fig 5.
Defuzzification Calculation Process Fuzzy Statement Analysis Using Matlab Defuzzification fills the output variable with one number using the centroid or area center The last step in this implementation is the process of taking the input value to get the output value.
In this study, the input value is 104, the initial output value is 103, and the final stage output is 324.
Abbasipayam.
, & Makrova.
Fuzzy logic and intelligent control of engineering systems of buildings.
Vestnik of Astrakhan State Technical University.
Series:
Management.
Computer Science and Informatics.
https://doi.
org/10.
24143/20735529-2022-1-22-32
Acosta-Prado.
Lazo.
, & TafurMendoza.
Application of fuzzy logic in the relationship between technologies and economic performance.
Journal of Intelligent and Fuzzy Systems.
https://doi.
org/10.
3233/JIFS-189180
Ain.
Saifizi.
Othman.
Aziz.
Mustafa.
, & Khairunizam.
Temperature Control Using Fuzzy Controller for Variable Speed Vapor Compression Refrigerator System.
Proceedings of International Conference on Artificial Life Robotics.
https://doi.
org/10.
5954/icarob.
Alwendi.
Optimalisasi Internet of Things untuk Meningkatkan Produksi pada Sektor Usaha Kecil dan Menengah di Masa Pandemi Covid-19.
Jurnal Informatika Dan Rekayasa Perangkat Lunak.
https://doi.
org/10.
36499/jinrpl.
Alwendi.
, & Masriadi.
APLIKASI
PENGENALAN
WAJAH
MANUSIA
PADA
CITRA
MENGGUNAKAN
METODE FISHERFACE.
Jurnal Digit.
https://doi.
org/10.
51920/jd.
Anisah.
Yulianto.
, & Faisol.
Perbandingan Fuzzy Sugeno dan Fuzzy Mamdani Pada Analisis Minat Masyarakat Terhadap Produk Air Minum Dalam Kemasan Lokal dan Nasional di Madura.
Zeta Math Journal.
https://doi.
org/10.
31102/zeta.
Aslam.
Sustainable processing:
Examples in process intensification & commercial catalysis.
AIChE Annual Meeting.
Conference Proceedings.
Baliuta.
Kopylova.
Kuievda.
Kuevda, , & Kovalchuk.
Fuzzy logic energy management system of food manufacturing processes.
Ukrainian Food Journal.
https://doi.
org/10.
24263/2304974x-2020-9-1-19 Budi Indra Gunawan, & Unan Yusmaniar Measurement accuracy rate The definition of accuracy is how close the measurement result is to the actual number.
Because this study is so precise, we start with the number of measurements, the Y value of the Mamdani method, which uses a standard set of values to give the correct result.
The default value of the Mamdani method is the value of the output variable for assessing lecturer research activities, determined using the membership If the conclusion is the result of a fuzzy assessment, and the final assessment results are the same, then it is declared accurate.
If not, then the result is NOT ACCURATE The result of the accuracy of the Mamdani method is 9 and the value of the fuzzy calculation is 16.
Thus, the percentage of accuracy of the Mamdani method can be To evaluate outstanding lecturers according to the following equation:
% Akurasi=((Jumlah aturan akura.
)/(Nilai Fuzzy ) X100 % Akurasi=(( 9 ))/16 X100= 56.
CONCLUSION
Based on the results of the tests and discussions that have been described, the following conclusions Fuzzy logic with the Mamdani method can be used to predict the evaluation of lecturer activities from the results of the panel research that has been carried out.
There is a significant difference between the Matlab software and calculations performed This system can only evaluate lecturer performance in general.
REFERENCE