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All Journal Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Journal of Mathematics, Computation and Statistics (JMATHCOS)
Shalshabilla Shafa
IPB University
Computer Science & IT Mathematics

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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.
Evaluasi Perbandingan Model XGBoost, Random Forest, LightGBM, dan Artificial Neural Network dalam Klasifikasi Kerawanan Pangan Mardatunnisa Isnaini; Dela Gustiara; Rizqi Annafi Muhadi; Shalshabilla Shafa; Bagus Sartono; Aulia Rizki Firdawanti; Budi Susetyo; Gerry Alfa Dito
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.36227

Abstract

Food insecurity remains a serious household-level issue, particularly in densely populated regions such as West Java, highlighting the need for analytical approaches capable of accurately identifying vulnerable groups. Machine learning algorithms offer the potential to improve the accuracy and precision of food insecurity classification based on survey data. This study aims to compare the predictive performance and variable importance identification of four machine learning algorithms—Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN)—in predicting household food insecurity status. The analysis employs SUSENAS 2023 data covering 26,012 households with 14 predictor variables, and food insecurity is classified using the Food Insecurity Experience Scale (FIES). Class imbalance is addressed using the Synthetic Minority Over-sampling Technique (SMOTE) within a 10-fold cross-validation framework. The results show that XGBoost achieves the highest accuracy of 71%, while Random Forest provides the best balanced accuracy under the SMOTE scenario. Moreover, all algorithms consistently identify the Wealth Index as the most influential predictor based on their respective Variable Importance measures, followed by variables related to water access and food assistance. Accordingly, XGBoost is recommended in terms of accuracy, whereas Random Forest demonstrates superior balanced accuracy and prediction stability.
Co-Authors Andi Illa Erviani Nensi Aulia Rizki Firdawanti Bagus Sartono Budi Susetyo Dela Gustiara Gerry Alfa Dito Mardatunnisa Isnaini Rizqi Annafi Muhadi