Implementation of TOPSIS Algorithm for Evaluating Lecturer Performance

Various activities are carried out in an effort to improve teaching and learning processes as well as graduates, one of which is to improve the quality of performance and quality of each lecturer. STMIK Bina Patria Magelang is a private university in the field of information and computers. In assessing the performance of lecturers, agencies still experience difficulties. This is due to the difficulty of assigning value to each assessment criterion where the data is separate. In evaluating the performance of lecturers, STMIK Bina Patria Magelang applies seven (7) criteria, namely: attendance, teaching, research, dedication, loyalty, cooperation and responsibility. Where for each criterion has different assessment weights. A R T I C L E I N F O ABSTRACT

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a good settlement method in decision support systems [4] [5].The TOPSIS algorithm uses the principle that the chosen alternative must have the longest (farthest) distance from the negative ideal solution from a geometric point of view using the relative proximity of an alternative [6][7] [8].
Several studies that have been conducted related to research conducted by researchers are research conducted, produce decision support systems in lecturer performance evaluation.In this study using 5 (criteria) as a decision-making consideration [9].Research conducted by, C. Surya, (2018) produced a prototype decision support system by implementing the TOPSIS method.In this study using eight (8) criteria with the data of respondents as many as 5 lecturers [10], conducted a study on the assistance of poor students in elementary schools [11].This study aims to measure the performance of lecturers by implementing the TOPSIS method in Decision Support Systems (DSS).The solution presented in this study is a recommendation assessment with a lecturer performance rating system through a decision support system built [12].

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Algorith
TOPSIS is based on the concept that the best chosen alternative does not only have the shortest distance from the positive ideal solution, but also thinks of the longest distance from the ideal negative solution.The concept of the TOPSIS method is simple and easy to understand, the decision is efficient and has the ability to measure the relative performance of decision alternatives in a mathematical form [13] .First, confirm that you have the correct template for your paper size.This template has been tailored for output on the A4 paper size.If you are using US letter-sized paper, please close this file and download the file "MSW_USltr_format".

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In general the TOPSIS method has the following solutions to the solution:  Make a normalized decision matrix  Make a normalized decision matrix weighted  Make a positive ideal solution matrix and a negative ideal solution matrix  Determine the distance between the values of each alternative with the positive ideal solution matrix and the negative ideal solution matrix.
 Determine the preference value for each alternative With Xij, the performance of the calculation for the third alternative to the jth attribute.In taking a solution, TOPSIS requires a performance rating of each alternative A1 on each normalized C1 criterion using Equation 2.
where i=1,2,3, …m; and j=1,2,3 Calculate the positive A + ideal solution and the negative ideal solution A-using Equation 4and Equation 5 is determined based on the normalized weight rating (Yij) with Equation 3 according to the type of criteria according to Equation 6 and Equation 7.
Calculating the distance between alternative Ai and the negative ideal solution is formulated in Equation 9.
Calculate the preference value of each alternative (V i ) against the ideal solution using Equation 11.
Then the ranking is based on the largest value (V i )

Method
In this study using the approach with the waterfall method.At the data collection stage researchers used methods of observation, interviews to the staffing division and literature study.The data analysis method used is the TOPSIS method for the final ranking process in the performance evaluation of lecturers with the best criteria.The tool for modeling is using the Unified Modeling language (UML), while for the prototype implementation it uses the PHP programming language and the database uses MySQL.

Results and Discussion
In the data analysis, the TOPSIS method is used in evaluating the performance of lecturers based on the criteria in Table 1.

Table 1.
Criteria of lecture evaluation

Criteria Description
Then the importance level used for each criterion is shown in Table 2.The weight value for each criterion is then determined, shown in Table 3.In the data analysis, this study uses sample data from 5 lecturers so that the weighting preference data is generated in Table 4. W= {3,4,5,3,5,2,5} In the data analysis with TOPSIS method with sample data of 5 lecturers in Table 4, the analysis will be carried out as follows:

Make normalized data / matrices
In determining normalized matrix values, Equation 2 is used.

0.309
And so on for each alternative value, so that a matrix is produced in Table 5.At the stage of building the weighted matrix is done with Equation 3.
Y 1,1 = 0.617 * 3 = 1.852 And so on for each normalized matrix value, so that the normalized matrix values are weighted in Table 6.

Table 7.
Positive and negative ideal solutions

Calculating the distance between values of each alternative (D)
In determining the distance between the alternative values of the positive ideal solution (D+) digunakan Persamaan 8 Equation 8 is used while to calculate the distance between values the ideal negative solution (D-) is used Equation 9.While the TOPSIS process will be shown in the digram activity in Figure 2. In the topsis process, the user will select the TOPSIS menu and the system will retrieve data, including lecturer data, Criteria data and Criteria value data.Then the system will calculate the normalized matrix, the normalized matrix is weighted, calculate the ideal solution, calculate the distance and calculate the preference value.The system will then check the data.If there is no data, the system will save the data, but if the data already exists, the system will update the existing data.The system ranks lecturer data by DESC and displays ranking data to the user.

System implementation
Furthermore, in carrying out the process of evaluating the performance of the lecturer, the user performs the data on the value of each Criteria on the TOPSIS process page in Table 8.

Conclusion
Based on the results of the research implementation of the technique for others reference by similarity to ideal solution (TOPSIS) in the decision support system for lecturer performance evaluation concluded that TOPSIS can provide recommendations in lecturer performance evaluation, where the results of calculations based on the highest preference value (Vi) of each alternative.The highest score is the first priority as the best performance lecturer.In analyzing data using 5 lecturer samples, the preference value was 0.612 for lecturers 1, 0.409 for lecturers 2, 0.398 for lecturers 3, 0.601 for lecturers 4, and 0.405 for lecturers 5. this study produced a WEB-based decision support system prototype using the PHP programming language and its database using MySQL.

Table 2 .
The importance level

Table 3 .
Weight value for each criterion

Table 4 .
The preference weight of each lecturer

Table 8 .
Lecturer performance results based on TOPSIS calculations