An Interpretable and Hierarchical Positive-Prior-Driven Student Evaluation Enhancement Decision-Making Strategy

Selecting the Best Students Using the Simple Additive Weighting Method Decision Support System

Selecting the best student is one form of appreciation implemented by An-Nawa Islamic Boarding School in Serang Regency to encourage students' motivation, achievement, and discipline. The current selection process is conducted manually, which may lead to subjectivity and inconsistencies in decision-making. This study aims to design and implement a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to assist in determining the best student objectively, transparently, and systematically. The research employed a quantitative approach using five evaluation criteria: moral character, participation in learning activities, academic performance, attendance, and responsibility. The data consisted of 30 students evaluated based on these criteria. The SAW method was applied through the stages of decision matrix construction, normalization, weighting, and preference value calculation to generate a ranking of alternatives. The results indicate that the SAW method can provide an objective and effective selection process. Alternative A28 (Mashadi) achieved the highest preference value of 1.00 and ranked first, followed by A6 (Retno Safitri) with a score of 0.97 and A19 (Hoirun Saban) with a score of 0.91. The implemented system assists school administrators in accelerating the selection process, reducing assessment subjectivity, and producing more accurate and accountable decisions. Therefore, the SAW method is proven to be effective for application in a decision support system for selecting the best student.

Student Performance Evaluation Using an Interpretable Genetic–Fuzzy Framework with Rule-Base Reduction

Uncertainty in instructors' judgments and qualitative evaluations is difficult to capture in traditional student performance evaluations centered on descriptive statistics and averages. While traditional fuzzy systems are capable of handling this kind of ambiguity, they become less efficient and harder to understand as the number of evaluation variables rises since their rule bases are exponentially larger. Exam results may be used to evaluate students' performance, and this research suggests a genetic-fuzzy architecture with rule-base reduction that can be understood. The model was trained in MATLAB using the following parameters: 50 nodes, 25 generations, 0.8 crossover probability, 0.2 mutation rate, and roulette-wheel selection; Mamdani inference; centroid defuzzification; and a genetic algorithm. The non-optimized fuzzy method's MAE is 0.0208 and the genetic-fuzzy method's MAE is 0.0101, according to recalculation utilizing the 25 records shown in Table 1. These values are unique to the descriptive versus creative 25-record comparison and should not be used interchangeably with the separate function membership creative sensitivity results from Table 4. The original basic set of 125 rules was pared down to 46 rules. The leakage-free train-validation-test methodology is defined in this version, and final generalization estimations must be given from it.

Fuzzy Inference and Explainable Artificial Intelligence for Mapping Multi-Dimensional Academic Performance to Technical Demand

This work offers a robust decision-support framework for higher education institutions, accreditation bodies, and employers by systematically linking graduates’ knowledge, skills, and behavioral attributes to technical demand by systematically linking graduates’ knowledge, skills, and behavioral attributes to technical demand.

An ML Ensemble Model for Evaluating Student Performance and Recommending Academic Specializations in Science or Literature

A Fuzzy Tahani-Based Decision Support System for Selecting Academic Achievement Scholarship Recipients

Scholarship programs are essential for increasing access to higher education, yet selection processes are frequently hindered by subjective reasoning and procedural inefficiencies. This study presents an advanced Decision Support System (DSS) for academic achievement scholarships at Universitas Islam Majapahit using a modified Fuzzy Tahani method characterized by a dual-platform architecture, socio-economically tailored membership functions, and complete operational transparency. The model evaluates four primary criteria—Grade Point Average (GPA), parental income, number of dependents, and non-academic achievements—to provide objective prioritization through fuzzification and defuzzification processes. Testing conducted on 18 applicants across Microsoft Excel and Android platforms demonstrated 100% computational accuracy and a Spearman’s rank correlation of 1.0, confirming identical ranking consistency. Furthermore, the system exhibited high efficiency with processing times of 35 seconds on Excel and 2 seconds on Android, while sensitivity analysis and a 94% expert validation rate underscored its procedural robustness. The results indicate that students possessing high academic merit alongside significant financial need were consistently prioritized, addressing previous systemic deficiencies in fairness and transparency. Ultimately, this modified Fuzzy Tahani framework offers a reliable and validated multi-criteria decision-making system that other higher education institutions can adapt to ensure more equitable scholarship allocations.

Sensitivity Analysis of a Simple Additive Weighting-Based Decision Support System for Strategic E-Learning Evaluation

The implementation of e-learning often faces obstacles in the objectivity of student performanceevaluations which have an impact on the inaccuracy of managerial decision-making. This studyaims to design a Decision Support System (SPK) using the Simple Additive Weighting (SAW)method which is integrated with sensitivity analysis. Four main criteria were used: forum activity(25%), task accuracy (30%), evaluation value (30%), and duration of access (15%). Thesimulation results showed that the system succeeded in classifying students into three categoriesof adaptivity, with Citra (A3) being the highest ranked (score of 0.98). Crucial findings from the  sensitivity analysis showed that although the weight of the interaction was increased by 40%, theranking order remained robust, but the score in the passive student group decreased significantlyto 0.46 (below the critical threshold of 0.50). This proves that the model not only functions as aranking tool, but also as a precise early warning system for university management to carry outdata-driven preventive interventions.