Page 30 - POSTER FYP MAC-OGOS 2025

A NOVEL SCORE FUNCTION FOR SVNS MULTI-CRITERIA DECISION FOR THE

SELECTION OF THE SOFTWARE ENGINEER AND THE INVESTMENT PROJECT K242/31

PREPARED BY: NUR YUSELYANIE AFIERA BINTI MOHD YUSOF

SUPERVISOR: DR. NORZIEHA MUSTAPHA

ABSTRACT

METHOD & IMPLEMENTATION

This study FORMULATE NEW SCORE FUNCTION :

introduces a novel score

function for Single-Valued

Neutrosophic Sets (SVNS) to enhance PROOF: SCORE FUNCTION OF SVNS NEED TO SATISFIES :

Multi-Attribute Group Decision-Making

(MAGDM) under uncertainty. The function

better integrates the SVNS components,

which are truth, indeterminacy, and

falsity, into a single value. Two case

studies validate the approach: selecting a

software engineer (with known weights)

and selecting an investment project (with

unknown weights). Results show the new

function yields more consistent, stable,

and sensitive rankings

than existing

methods.

PROBLEM STATEMENT ALGORITHMS:

Current SVNS score functions often fail STEP 1: CONSTRUCT DECISION MATRIX STEP 4: SCORE FUNCTION

to consistently rank alternatives in

uncertain environments. These

methods lack mathematical STEP 2: STANDARD COEFFICIENT

robustness, especially in handling STEP 5: WEIGHTED SCORE FUNCTION

indeterminacy. This leads to inaccurate

decision-making in real-world STEP 3: WEIGHT MATRIX

applications like candidate hiring and FOR KNOWN WEIGHT FOR UNKNOWN WEIGHT

project evaluation, where uncertainty

is high.

RESULTS & DISCUSSION

OBJECTIVE

To formulate an optimized score function for RANKING RESULTS FOR

SVNS. CASE STUDY 1

To apply the score function in:

-Selecting a software engineer using known

weights.

-Selecting an investment project with

unknown weights. RANKING RESULTS FOR

CASE STUDY 2

CONCLUSION RECOMMENDATION

Develop user-friendly software or web-based

The new score function improved tools using the proposed method.

decision-making accuracy under Expand applications into domains like finance,

uncertainty. It produced more healthcare, and environmental planning.

reliable and distinguishable Integrate with intelligent systems or AI for

rankings than previous methods. adaptive and dynamic decision-making.

The model successfully addresses Conduct training for decision-makers to

limitations of current SVNS-based understand neutrosophic reasoning.

evaluation techniques.