Comparative Analysis of Multi-Criteria Decision Making for Student Degree Completion Time based on Entropy Weighted

Wati, Masna; Pakpahan, Herman Santoso; Novirasari, Niken

doi:10.1109/icaiti.2018.8686746

Cited by 3 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in the Figure 1, the flow of research conducted consists of three main stages, including: determination of criteria and weights, the application of the simple additive weighting (SAW) method and the weighted product (WP) method, and test of sensitivity of each method [33][34] [35].…”

Section: Methodsmentioning

confidence: 99%

Comparative analysis of the sensitivity test of the SAW and WP methods in scholarship selection

Supriyanti¹

2023

J. Teknik Informatika CIT Medicom,

View full text Add to dashboard Cite

Decision-makers frequently use the MADM (Multiple Attribute Decision Making) method to assist in solving decision-making issues. This approach can use a variety of algorithms, including Simple Additive Weighting (SAW) and Weighted Product (WP). The challenge in this research is determining which of the SAW and WP approaches is more pertinent or acceptable for solving scenarios involving scholarship recipient selection. Five factors were taken into consideration when deciding who would receive a scholarship: academic achievement index (GPA), parents' income, past accomplishments, participation in student organizations, and the number of parents' dependents. A sensitivity test, which involves altering the weight of each test method's criterion and then comparing the percentage change between the two ways, is one method that can be used to gauge the effectiveness of the MADM method. The SAW method implementation results show that alternative nine (MHS9) has the highest preference value, which is 0.95. The WP method implementation results show that alternative nine (MHS9) has the highest preference value, which is 0.12. The total change in the findings of the sensitivity test for the SAW method is 7.64%, compared to 2.06% for the WP method. Thus, it can be inferred that the SAW approach is thought to be pertinent for addressing the issue of choosing scholarship applicants.

show abstract

Section: Methodsmentioning

confidence: 99%

Comparative analysis of the sensitivity test of the SAW and WP methods in scholarship selection

Supriyanti¹

2023

J. Teknik Informatika CIT Medicom,

View full text Add to dashboard Cite

show abstract

Comparison SAW, TOPSIS and MOORA to evaluation socio-economic welfare

Wati,

Hasanah,

Jamil

et al. 2024

Proceedings of the 6th International Conference on Computing and Applied Informatics 2022

View full text Add to dashboard Cite

Analysis K-Means Clustering to Predicting Student Graduation

Wati

Rahmah

Novirasari

et al. 2021

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The prediction of students’ graduation outcomes has been an important field for higher education institutions because it provides planning for them to develop and expand any strategic programs that can help to improve student academics performance. Data mining techniques can cluster student academics performance in predicting student graduation. The aim of this study is to analysis the performance of data mining techniques for predicting students’ graduation using the K-Means clustering algorithm. The data pre-processing used for data cleaning, and data reducing using Principle Component Analysis to determine any variables that affect the graduation time. This algorithm processes datasets of student academics performance numbering 241 students with 16 variables. Based on the clustering using K-means, the highest accuracy rate is 78.42% in the 3-cluster model and the smallest accuracy rate is 16.60% in the 4-cluster model. The influential variable in predicting student graduation based on the value of the loading factor is the GPA total of the 1st to 6th semester.

show abstract

Comparative Analysis of Multi-Criteria Decision Making for Student Degree Completion Time based on Entropy Weighted

Cited by 3 publications

References 8 publications

Comparative analysis of the sensitivity test of the SAW and WP methods in scholarship selection

Comparative analysis of the sensitivity test of the SAW and WP methods in scholarship selection

Comparison SAW, TOPSIS and MOORA to evaluation socio-economic welfare

Analysis K-Means Clustering to Predicting Student Graduation

Contact Info

Product

Resources

About