Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Mathematics, Computation and Statistics (JMATHCOS)
Shalshabilla Shafa
IPB University
Mathematics

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Analysis of the Relationship between Literacy, Numeracy and School Accreditation Rankings in Sulawesi Using Ordinal Logistic Regression and K-Nearest Neighbors Andi Illa Erviani Nensi; Dela Gustiara; Shalshabilla Shafa; Budi Susetyo
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/mka1m371

Abstract

This study aims to analyze the relationship between literacy and numeracy achievement and school accreditation rankings in the Sulawesi region and to compare the performance of two classification methods, namely ordinal logistic regression and the nearest neighbor method. The data used came from the results of the 2023 and 2024 national school assessments with response variables in the form of tiered school accreditation rankings and predictor variables in the form of literacy and numeracy scores. The analysis began with data exploration to understand the characteristics of distribution and class imbalance, then continued with modeling using two scenarios, namely without and with extreme value handling. Ordinal logistic regression was constructed using a cumulative probability approach and tested through assumption checking, parameter significance, and performance evaluation. The nearest neighbor method was applied through data normalization and parameter tuning to obtain the optimal configuration, and compared between conditions with and without class balancing. The results showed that literacy, especially in 2024, had a significant effect on increasing the probability of higher school accreditation, with an ordinal logistic regression model accuracy rate of around 58% and a balanced accuracy of around 65%. The KNN method produced higher prediction accuracy, around 66%, but had limitations in distinguishing minority classes. These findings emphasize the importance of literacy as a key indicator of school quality and provide a basis for selecting classification methods according to the analysis objectives.
Co-Authors Andi Illa Erviani Nensi Budi Susetyo Dela Gustiara