Normalization-Simple Additive Weighting to Determination of a Single Tuition

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Normalization-Simple Additive Weighting to Determination of a Single Tuition

Winda Aprianti^1,*, Herfia Rhomadhona², Jaka Permadi³, Yuniska Fithriyyah⁴

Politeknik Negeri Tanah Laut, Jalan Ahmad Yani Km.06, Tanah Laut, Indonesia

1 [email protected]*; ²[email protected]; ³[email protected], ⁴[email protected]

* corresponding author

I. Introduction

The tuition payment system for each student is regulated in Minister of Research, Technology, and Higher Education in Regulation Number 39 of 2016. The payments system, known as single tuition is a system that regulates every student to pay only one cost component [1]. Every student pays a different amount of a single tuition, depending on the economic and social conditions of the parents or guardians of the student. The cross-subsidies of a single tuition fees between students are expected to help the tuition fees that must be paid by family with a lower economy so that all levels of society can continue their education to higher education in Indonesia.

Determination of a single tuition for each student in Politeknik Negeri Tanah Laut is doing by a single tuition determination team. They consist of representative of Finance and representatives of each department at Politeknik Negeri Tanah Laut. Determination of a single tuition is held at beginning of semester. The implementation of this process by classifying a single tuition based on the student data that collected during registration of new students. Data consist of photocopies of salary slips or income statements of parents or guardians of students, photocopies of electricity payment account and achievement data. The next steps are combines data into files using Microsoft excel and classification a single tuition based on criteria of specified requirement. Single tuition is classified into 5 (five) class where each class has a quota percentage of the number of new students.

After team getting quota amount for each class of a single tuition, they will calculate each student’s score based on existing data. Based on the final score, score of each student will be sorted, then decision to determine the single tuition is taken by team. Sometimes, there is a difference of opinion in team when determining a single tuition. This situation happens when there are students with the same final score but they must be classified in different classes because the class quota is sufficient.

This situation makes the team had difficulty in determining tuition fees class and it requires

ARTICLE INFO A B S T R A C T

Article history:

Accepted

Determination of a single tuition appropriately is important to ensure that every student gets a fair single tuition in accordance with their economic conditions. The single tuition team to determine a single tuition sometimes they have differences of opinion in determining a single tuition so that it requires a relatively long time. This problem can be solved by applying Simple Additive Weighting to student data. The collected data is formed into Data1 and Data 2. There is a value of 0 for the cost attribute so that it requires normalization which can solve the problem. The results of the application of Simple Additive Weighting facilitate the determination of a single tuition because by entering student data a preference value is generated as a reference for determining a single tuition. Application in Data 1 produces several similarities in preference values but is in different groups, but the application of Data 2 always results in different preference values. This difference shows that the application of Simple Additive Weighting to Data 2 is better than the application of Data 1.

Keywords:

Simple Additive Weighting Normalization

Single Tuition

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relatively long time to get an agreement about their single tuition fee class because it must get approval of all team members.

Determination a single tuition problem requires solution in the form of decision support system to classification a single tuition fee. Decision support system using Technique For Order Preference By Similarity To Ideal Solution (TOPSIS) was used by [2] and [3]. In [2] explained that determined a single tuition fee using interview method which interviewer fatigue factor that affects the assessment, so it becomes one of the unstructured problems. Determination tuition fee using Analytical Hierarchy Process (AHP) on [4]–[6]. The research on [7], [8] used Simple Additive Weighting to determination a single tuition. Research on [7] study in Kolaka University with 7 criteria, namely family circumstances, education, occupation, age, number of children, income and assets.

Simple Additive Weighting method also has been used in various previous studies, namely personnel selection problem on [9], teacher performance assessment on [10], site selection for new store on [11], determination of the best staff on [12], and determination of salary increase rate on [13].

In this research, decision support system will be designed using Simple Additive Weighting method which applies on two data, that is simple additive weighting with data transformation into score based on announcement number 371/PL40/TU/2017 and simple additive weighting with the real data (without score transformation). Simple additive weighting with score transformation allows occurrence of a zero value in data which affect in normalization stage. Therefore, simple additive weighting in this study will use normalization adopted from [14], [15].

Based on description above, this study will apply simple additive weighting through an application system to simplify decision making process. The application system that is built will provide ranking results to find out the highest to lowest values. The next step is comparation the ranking result between two methods applied to data that has been collected

II. Method

The research method is divided into several steps, namely as follows.

A. Data collection

Data collection is process of collecting data by interviewing representative of department who became member of determination single tuition team in 2017. The data collected was used as criteria to determining a single tuition.

B. Data formation

Data formation is process to form Data 1 and Data 2. Data 1 is data have been transformed into score based on announcement number 371/PL40/TU/2017. While Data 2 is real data without score transformation.

C. Application of Simple Additive Weighting

This stage is application of simple additive weighting to Data 1 and Data 2. Based on studies to [14]–[16] is got the following algorithm.

i. Determine the criteria that will be used as a reference

ii. Give value for each alternative to each criterion that has been set before iii. Determine the match rating for each alternative on each criterion iv. Give weight value based on crips value

v. Perform matrix normalization by calculating normalized performance rating values using Equation (1).

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Explanation:

= The highest value on each criteria j

= The lowest value on each criteria j

= The value of attribute from each alternative i on criteria j Profit attribute = If the highest value is the best

Cost attribute = If the lowest value is the best

= Normalized performance rating values

vi. Determine preference value for each alternative by adding up product result of normalization matrix with weight value that shown on Equation (2).

(2) Explanation:

= Preference value for each alternative i = Weight value for each criteria j

= Normalized performance rating values

vii. Perform a ranking process for each alternative by ordering the highest preference value to the lowest preference

viii. Determine a single tuition D. Comparative analysis

This stage is process to compare the result of applying simple additive weighting to Data 1 and Data 2.

III. Results and Discussion A. Data Collection

Data collection is done by interviewing representative of informatics engineering department.

The results of interview are information about classes, percentage quota for each class and the criteria and weight for each criterion used in decision making to classify single tuition fee. Class and quota percentage are presented in Table 1.

Table 1. Percentage of single tuition class

Class Amount of single tuition quota percentage

I Rp 0 5%

II Rp 750.000 10%

III Rp 1.250.000 20%

IV Rp 1.500.000 55%

V Rp 1.750.000 10%

Total 100%

The criteria for determining a single tuition and each weight applied in 2017 are as follows.

i. Parent income calculated from income of both parents is divided by number of dependents. This criterion has a weight of 50%

ii. Achievements are classified into four, namely national, provincial, district/regional, and there are no achievements. This criterion has a weight of 25%.

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iii. Electricity payment bill per month. This criterion has a weight of 25%.

In addition to these three criteria, if there is a student who has a certificate explaining that he/she is not economically capable (certificate of inability), he/she will get an additional score for determining a single tuition of 2 points. In this study because normalization value in simple additive weighting method not more than 1, then the value of 2 will be divided by 10 so that it becomes 0,2.

This calculation is done with aim that value lies in one number behind comma.

Data collection produces data for new students for 2017 as many as 220 students. However, for calculation in this study 20 data will be used. Every five data from each department is taken randomly as a sample. Preliminary data are presented in Table 2.

Table 2 Preliminary Data

No Name

(Alternative)

Criteria Certificate

of inability Parent Income Electricity

payment bill Achievement

1 Student 1 Rp 1.976.500 Rp 85.915 No No

2 Student 2 Rp 333.333 Rp 57.086 No No

3 Student 3 Rp 707.500 Rp 88.000 No Yes

8 Student 8 Rp1.000.000 Rp 66.090 Yes (District) Yes

9 Student 9 Rp1.089.411 Rp 224.628 No No

12 Student 12 Rp 500.000 Rp 90.000 Yes (District) Yes

B. Data Formation

The formation of data is divided into two, namely Data 1 and Data 2.

B.1. Data 1

Score transformation for each criterion determine through announcement number:

371/PL40/TU/2017 is described in Table 3, Table 4, and Table 5.

Indicators of parent income and weight of assessment are presented in Table 3.

Table 3 Parent Income

No Criteria Sub Criteria Score

1

Parent Income (50%)

Rp 300.000 0

2 Rp 300.000 Rp 600.000 3

3 Rp 600.000 Rp 1.000.000 6

4 Rp 1.000.000 9

Indicators of electricity payment bill and weight of assessment are presented in Table 4.

Table 4 Electricity Payment Bill

No Criteria Sub Criteria Score

1

Electricity Payment Bill (25%)

Rp 50.000 0

2 Rp 50.000 Rp 100.000 3

3 Rp 100.000 Rp 200.000 6

4 Rp 200.000 9

Indicators of achievement and weight of assessment are presented in Table 5.

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Table 5 Achievement

No Criteria Sub Criteria Score

1

Achievement (25%)

National 9

2 Provincial 6

3 District/Regional 3

4 No Achievement 0

Score transformation for Tabel 2 based on determination in Table 3, Table 4, and Table 5 is presented in Table 6.

Table 6 Data 1

No Name

(Alternative)

Criteria Parent Income Electricity

1 Student 1 9 3 0

2 Student 2 3 3 0

3 Student 3 6 3 0

4 Student 4 3 9 0

5 Student 5 6 6 0

6 Student 6 0 0 0

7 Student 7 6 3 0

8 Student 8 6 3 3

9 Student 9 9 9 0

10 Student 10 6 6 0

11 Student 11 6 0 0

12 Student 12 3 3 3

13 Student 13 9 6 0

14 Student 14 6 3 0

15 Student 15 6 6 0

16 Student 16 3 3 0

17 Student 17 0 6 0

18 Student 18 3 3 0

19 Student 19 3 3 0

20 Student 20 3 3 0

B.2. Data 2

Data 2 is real data without score transformation, but achievement criteria must be change from category into numerical use Table 4. Data 2 is presented in Table 7.

Table 7 Data 2

No Name

(Alternative)

1 Student 1 Rp 1.976.500 Rp 85.915 0

2 Student 2 Rp 333.333 Rp 57.086 0 3 Student 3 Rp 707.500 Rp 88.000 0

4 Student 4 Rp 500.000 Rp 225.428 0

5 Student 5 Rp 750.000 Rp 150.000 0

6 Student 6 Rp 233.333 Rp 39.697 0 7 Student 7 Rp 900.000 Rp 54.967 0

8 Student 8 Rp1.000.000 Rp 66.090 3

9 Student 9 Rp1.089.411 Rp 224.628 0

10 Student 10 Rp 666.667 Rp 200.000 0

11 Student 11 Rp1.000.000 Rp 22.173 0 12 Student 12 Rp 500.000 Rp 90.000 3 13 Student 13 Rp4.045.000 Rp 91.794 0 14 Student 14 Rp 750.000 Rp 62.383 0

15 Student 15 Rp1.000.000 Rp 136.937 0

16 Student 16 Rp 500.000 Rp 100.000 0 17 Student 17 Rp 300.000 Rp 150.000 0 18 Student 18 Rp 500.000 Rp 80.000 0 19 Student 19 Rp 500.000 Rp 52.000 0 20 Student 20 Rp 375.000 Rp 60.264 0

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C. Application of Simple Additive Weigthing (SAW)

C.1. Application of Simple Additive Weigthing (SAW) on Data 1

Stage i until stage iv have explained at part A and part B.1, next stage Table 6 is normalized using Equation (1). Parent income and electricity payment bill are criteria with cost attribute, while achievement is criteria with profit attribute. As example, normalization process using Equation (1) to performance rating values , , and as follows.

=

= =

=

= =

=

= = 0

Using the same way, we get results are presented in Table 8.

Table 8 Normalization Result of Data 1

No Name

(Alternative)

1 Student 1 0 0,666667 0

2 Student 2 0,666667 0,666667 0

3 Student 3 0,333333 0,666667 0

4 Student 4 0,666667 0 0

5 Student 5 0,333333 0,333333 0

6 Student 6 1 1 0

7 Student 7 0,333333 0,666667 0

8 Student 8 0,333333 0,666667 1

9 Student 9 0 0 0

10 Student 10 0,333333 0,333333 0

11 Student 11 0,333333 1 0

12 Student 12 0,666667 0,666667 1

13 Student 13 0 0,333333 0

14 Student 14 0,333333 0,666667 0

15 Student 15 0,333333 0,333333 0

16 Student 16 0,666667 0,666667 0

17 Student 17 1 0,333333 0

18 Student 18 0,666667 0,666667 0

19 Student 19 0,666667 0,666667 0

20 Student 20 0,666667 0,666667 0

Based on stage vi, preference value is calculated using Equation (2). The preference value for Student 1 is given as follows.

Using the same way, then result of preference value for each alternative is presented in Table 9.

Table 9 Preference Value of Data 1

No Name

(Alternative)

Criteria

Preference Value Parent Income Electricity

1 Student 1 0 0,666667 0 0,166667

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2 Student 2 0,666667 0,666667 0 0,5

3 Student 3 0,333333 0,666667 0 0,333333

4 Student 4 0,666667 0 0 0,333333

5 Student 5 0,333333 0,333333 0 0,25

6 Student 6 1 1 0 0,75

7 Student 7 0,333333 0,666667 0 0,333333

8 Student 8 0,333333 0,666667 1 0,583333

9 Student 9 0 0 0 0

10 Student 10 0,333333 0,333333 0 0,25

11 Student 11 0,333333 1 0 0,416665

12 Student 12 0,666667 0,666667 1 0,75

13 Student 13 0 0,333333 0 0,083333

14 Student 14 0,333333 0,666667 0 0,333333

15 Student 15 0,333333 0,333333 0 0,25

16 Student 16 0,666667 0,666667 0 0,5

17 Student 17 1 0,333333 0 0,583333

18 Student 18 0,666667 0,666667 0 0,5

19 Student 19 0,666667 0,666667 0 0,5

20 Student 20 0,666667 0,666667 0 0,5

Preference values in Table 9 are not final value because there is rule about student who has certificate of inability will get additional value as 0,2. Based on Table 2, Table 9, and preference value ranking is obtained alternative order from the highest value to the lowest value. The next step is adjusting distribution of a single tuition into class based on Table 1. If there are two or more students have same final value, then system will compare parent income of these students. Student with parent income is lower than other student will lie on higher ranking than other student ranking.

Result of classification single tuition is presented in Table 10.

Table 10 Result of classification single tuition for Data 1

No Name

(Alternative)

Certificate of Inability

(0,2)

Preference Value

Final Preference Value (3+4)

Single Tuition

1 2 3 4 5 6

1 Student 6 Yes 0,75 0,95 Rp 0

2 Student 12 Yes 0,75 0,95 Rp 750.000

3 Student 8 Yes 0,583333333 0,783333333 Rp 750.000

4 Student 16 Yes 0,5 0,7 Rp 1.250.000

5 Student 20 Yes 0,5 0,7 Rp 1.250.000

6 Student 17 No 0,583333333 0,583333333 Rp 1.250.000

7 Student 3 Yes 0,333333333 0,533333333 Rp 1.250.000

8 Student 7 Yes 0,333333333 0,533333333 Rp 1.500.000

9 Student 14 Yes 0,333333333 0,533333333 Rp 1.500.000

10 Student 2 No 0,5 0,5 Rp 1.500.000

11 Student 18 No 0,5 0,5 Rp 1.500.000

12 Student 19 No 0,5 0,5 Rp 1.500.000

13 Student 11 No 0,416665 0,416665 Rp 1.500.000

14 Student 4 No 0,333333333 0,333333333 Rp 1.500.000

15 Student 5 No 0,25 0,25 Rp 1.500.000

16 Student 10 No 0,25 0,25 Rp 1.500.000

17 Student 15 No 0,25 0,25 Rp 1.500.000

18 Student 1 No 0,166666667 0,166666667 Rp 1.500.000

19 Student 13 No 0,083333333 0,083333333 Rp 1.750.000

20 Student 9 No 0 0 Rp 1.750.000

C.2. Application Simple Additive Weighting (SAW) on Data 2

Stage i until stage iv have explained at part A and part B.1, so next stage Table 7 is normalized using Equation (1). Parent income and electricity payment bill are criteria with cost attribute, while achievement is criteria with profit attribute. As example, normalization process using Equation (1) to performance rating values , , and as follows.

=

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= =

=

= =

=

= = 0

Using the same way, then we get normalization results are presented in Table 11.

Table 11 Normalization Result of Data 2

No Name

(Alternative)

1 Student 1 0,54268 0,68639 0

2 Student 2 0,97376 0,82823 0

3 Student 3 0,87560 0,67614 0

4 Student 4 0,93004 0,00000 0

5 Student 5 0,86445 0,37110 0

6 Student 6 1,00000 0,91378 0

7 Student 7 0,82510 0,83866 0

8 Student 8 0,79886 0,78393 1

9 Student 9 0,77541 0,00394 0

10 Student 10 0,88631 0,12510 0

11 Student 11 0,79886 1,00000 0

12 Student 12 0,93004 0,66630 1

13 Student 13 0,00000 0,65747 0

14 Student 14 0,86445 0,80217 0

15 Student 15 0,79886 0,43537 0

16 Student 16 0,93004 0,61710 0

17 Student 17 0,98251 0,37110 0

18 Student 18 0,93004 0,71550 0

19 Student 19 0,93004 0,85325 0

20 Student 20 0,96283 0,81260 0

Based on stage vi, preference value is calculated using Equation (2). Preference value for Student 1 is given as follows.

Using the same way, then result of preference value for each alternative is presented in Table 12.

Table 12 Preference Value of Data 2

No Name

(Alternative)

Criteria

Preference Value Parent Income Electricity

1 Student 1 0,54268 0,68639 0 0,44294

2 Student 2 0,97376 0,82823 0 0,69394

3 Student 3 0,87560 0,67614 0 0,60683

4 Student 4 0,93004 0,00000 0 0,46502

5 Student 5 0,86445 0,37110 0 0,52500

6 Student 6 1,00000 0,91378 0 0,72845

7 Student 7 0,82510 0,83866 0 0,62221

8 Student 8 0,79886 0,78393 1 0,84541

9 Student 9 0,77541 0,00394 0 0,38869

10 Student 10 0,88631 0,12510 0 0,47443

11 Student 11 0,79886 1,00000 0 0,64943

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12 Student 12 0,93004 0,66630 1 0,88159

13 Student 13 0,00000 0,65747 0 0,16437

14 Student 14 0,86445 0,80217 0 0,63277

15 Student 15 0,79886 0,43537 0 0,50827

16 Student 16 0,93004 0,61710 0 0,61929

17 Student 17 0,98251 0,37110 0 0,58403

18 Student 18 0,93004 0,71550 0 0,64389

19 Student 19 0,93004 0,85325 0 0,67833

20 Student 20 0,96283 0,81260 0 0,68457

Preference values in Table 12 are not final value because there is rule about student who has certificate of inability will get additional value as 0,2. Based on Table 2, Table 12, and preference value ranking is obtained alternative order from the highest value to the lowest value. The next step is adjusting distribution of a single tuition into class based on Table 1. Result of classification single tuition is presented in Table 13.

Table 13 Result of classification single tuition for Data 2

No Name

(Alternative)

Certificate of Inability (0,2)

Preference Value

Final Preference

Value (3+4) Single Tuition

1 2 3 4 5 6

1 Student 12 Yes 0,88159 1,08159 Rp 0

2 Student 8 Yes 0,84541 1,04541 Rp 750.000

3 Student 6 Yes 0,72845 0,92845 Rp 750.000

4 Student 20 Yes 0,68457 0,88457 Rp 1.250.000

5 Student 14 Yes 0,63277 0,83277 Rp 1.250.000

6 Student 7 Yes 0,62221 0,82221 Rp 1.250.000

7 Student 16 Yes 0,61929 0,81929 Rp 1.250.000

8 Student 3 Yes 0,60683 0,80683 Rp 1.500.000

9 Student 2 No 0,69394 0,69394 Rp 1.500.000

10 Student 19 No 0,67833 0,67833 Rp 1.500.000

11 Student 11 No 0,64943 0,64943 Rp 1.500.000

12 Student 18 No 0,64389 0,64389 Rp 1.500.000

13 Student 17 No 0,58403 0,58403 Rp 1.500.000

14 Student 5 No 0,52500 0,52500 Rp 1.500.000

15 Student 15 No 0,50827 0,50827 Rp 1.500.000

16 Student 10 No 0,47443 0,47443 Rp 1.500.000

17 Student 4 No 0,46502 0,46502 Rp 1.500.000

18 Student 1 No 0,44294 0,44294 Rp 1.500.000

19 Student 9 No 0,38869 0,38869 Rp 1.750.000

20 Student 13 No 0,16437 0,16437 Rp 1.750.000

D. Comparative Analysis

Application of simple additive weighting on Data 1 and Data 1 results in a different single tuition fee for some students, namely Student 6, Student 12, Student 17, Student 3, Student 7, dan Student 14. Difference between Student 6 and Student 12 is presented in Table 14.

Table 14 Comparison between Student 6 and Student 12

No Name

(Alternative)

Data 1 Data 2

Value Single Tuition Final Preference

Value Single Tuition

1 Student 6 0,95000 Rp 0 0,92845 Rp 750.000

2 Student 12 0,95000 Rp 750.000 1,08159 Rp 0

Table 14 show that Student 6 and Student 12 in Data 1 have same final preference value as 0,95000 but they are classified into a different single tuition class. Student 6 which has parent income is smaller than Student 12 gets a single tuition less than Student 12. This similarity makes determination of a single tuition class becomes bias. In contrast with application of simple additive weighting on Data 2, final preference value between Student 6 and Student 12 show clear difference so that determination of a single tuition can be done with certainty.

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Besides Student 6 and Student 12, differences in determination of a single tuition also occur in Student 3, Student 7, and Student 14. Comparative data is presented in Table 15.

Table 15 Comparison between Student 3, Student 7, and Student 14

No Name

(Alternative)

Data 1 Data 2

Value Single Tuition Final Preference

Value Single Tuition

1 Student 3 0,53333 Rp 1.250.000 0,80683 Rp 1.500.000

2 Student 7 0,53333 Rp 1.500.000 0,82221 Rp 1.250.000

3 Student 14 0,53333 Rp 1.500.000 0,83277 Rp 1.250.000

Table 15 show that Student 3, Student 7, and Student 14 in Data 1 have same final preference value as 0,53333 but they are classified into a different single tuition class. This similarity makes determination of a single tuition class becomes bias. In contrast with application of simple additive weighting on Data 2, final preference value between Student 3, Student 7, and Student 14 show clear difference so that determination of a single tuition can be done with certainty.

Whereas for Student 17 in Table 10 is classified in single tuition class of Rp 1.250.000, while Student 17 in Table 13 is classified in single tuition class of Rp 1.500.000. This difference is due to score transformation in Data 1, so that it affects final preference value.

Based on the comparison of simple additive weighting on Data 1 and Data 2, determination single tuition class of student using Data 2 recorded in Table 13 is better than using Data 1 recorded in Table 10.

IV. Conclusion

Based on the above explanation, application of simple additive weighting makes determination single tuition easy because team only need to enter data and then they get classification according preference value ranking. The application of simple additive weighting on Data 2 is better than application method on Data 1. This is because application of simple additive weighting on Data 1 has some final preference value, so that determination of a single tuition becomes a bias.

References

[1] Peraturan Menteri Riset, Teknologi, dan Pendidikan Tinggi Republik Indonesia Nomor 39 Tahun 2016. Indonesia: Kementerian Riset, Teknologi, dan Pendidikan Tinggi, 2016.

[2] B. I. Libing, D. M. Sihotang, and M. Boru, “Sistem Pendukung Keputusan Penentuan Uang Kuliah Tunggal Kepada Mahasiswa Baru Di Universitas Nusa Cendana Menggunakan Metode Technique For Order Preference By Similarity To Ideal Solution (TOPSIS),” J-ICON, vol. 7, no. 1, pp. 27–36, 2019.

[3] Mia Purnama, S. H. Sitorus, and M. Diponegoro, “Sistem Pendukung Keputusan Penentuan Kelompok Uang Kuliah Tunggal Menggunakan Metode Topsis Dengan Pembobotan Metode Ranking (Studi Kasus: Fakultas MIPA Universitas Tanjungpura Pontianak),” Coding J.

Komput. dan Apl., vol. 7, no. 1, pp. 11–18, 2019.

[4] H. Novianti, M. Mulya, and I. Jambak, “Sistem Pendukung Keputusan Penentuan Uang Kuliah Tunggal Dengan Metode Analitical Hierarchy Process (AHP) Di Universitas Sriwijaya,” JSI J. Sist. Inf., vol. 11, no. 1, pp. 1711–1721, 2019.

[5] A. Baskoro and M. Kamisutara, “Perancangan Sistem Pendukung Keputusan Penerima Bantuan UKT/SPP Mahasiswa dengan Metode Analytical Hierarchy Process (AHP) pada Perguruan Tinggi,” Explor. J. Keilmuan dan Apl. Tek. Inform., vol. 13, no. 2, pp. 17–25, 2021.

[6] S. Purnawanti, “Pengambilan keputusan golongan UKT di UIN Sunan Ampel Surabaya dengan menggunakan Analytical Hierarchy Process (AHP),” 2021.

[7] I. Tahir, “Model Pengambilan Keputusan Penentuan Uang Kuliah Tunggal (UKT) Pada Perguruan Tinggi Negeri (Studi Kasus: Universitas Sembilanbelas November Kolaka),” J.

Speed – Sentra Penelit. Eng. dan Edukasi, vol. 8, no. 2, pp. 1–9, 2016.

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[8] G. Testiana and Rachmansyah, “Pemanfaatan Metode Simple Additive Weighting (SAW)untuk Penentuan Penerima UKT Kelompok,” in Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI) 9, 2017, pp. 1–7.

[9] A. Afshari, M. Mojahed, and R. M. Yusuff, “Simple Additive Weighting approach to Personnel Selection problem,” Int. J. Innov. Manag. Technol., vol. 1, no. 5, pp. 511–515, 2010.

[10] A. Munawaroh, S. Sunardiyo, and Isdiyarto, “Penerapan Metode Simple Additive Weighting (SAW) pada Perancangan Decision Support System untuk Penilaian Kinerja Guru (PKG),”

Edu Komputika J., vol. 4, no. 1, pp. 19–28, 2017.

[11] N. C. Resti, “Penerapan Metode Simple Additive Weighting (SAW) pada Sistem Pendukung Keputusan Pemilihan Lokasi untuk Cabang Baru Toko Pakan UD. Indo Multi Fish,” Intensif, vol. 1, no. 2, p. 102, 2017.

[12] V. Julianto, Lastriani, W. Aprianti, and Herpendi, “Penerapan Metode Simple Additive Weighting (SAW) untuk Penentuan Seleksi Staf Terbaik Politeknik Negeri Tanah Laut Berbasis Web Mobile,” J. Sains dan Inform., vol. 4, no. 2, pp. 120–129, 2018.

[13] S. H. Sahir, R. Rosmawati2, and K. Minan, “Simple Additive Weighting Method to Determining Employee Salary Increase Rate,” nternational J. Sci. Res. Sci. Technol., vol. 3, no. 8, pp. 42–48, 2017.

[14] A. Jahan, K. Edwards, and M. Bahraminasab, Multi-criteria Decision Analysis for Supporting the Selection of Engineering Materials in Product Design, 2nd Editio. Oxford: Butterworth- Heinemann, 2016.

[15] Nazanin Vafaei, R. A. Ribeiro, and L. M. Camarinha-Matos, “Selection of Normalization Technique for Weighted Average Multi-Criteria Decision Making,” Technol. Innov. Resilient Syst., vol. 521, pp. 43–52, 2018.

[16] D. Nofriansyah, Konsep Data Mining vs Sistem Pendukung Keputusan. Yogyakarta:

Deepublish, 2015.