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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of Mathematics, Computation and Statistics (JMATHCOS)
Andi Illa Erviani Nensi
IPB University
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.
Performance Analysis of ARIMA, LSTM, and Hybrid ARIMA-LSTM in Forecasting the Composite Stock Price Index Andi Illa Erviani Nensi; Mahda Al Maida; Khairil Anwar Notodiputro; Yenni Angraini; Laily Nissa Atul Mualifah
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33379

Abstract

This study evaluates the performance of ARIMA, LSTM, and hybrid ARIMA-LSTM models in predicting the closing and opening prices of the Indonesia Stock Exchange Composite Index (IHSG) over various periods (2007-2020, 2007-2022, and 2007-2024). For the LSTM model, a lag of 1 was chosen based on MAPE analysis, showing strong dependence on the previous day’s price. Different learning rates (0.01, 0.001, 0.0001) and batch sizes (16, 32) were tested on various network architectures. Results indicate that while ARIMA effectively captures linear patterns, LSTM consistently outperforms with lower MAPE values—2.27% for closing and 2.02% for opening prices—especially with a simple (1-50-1) architecture and a learning rate of 0.001. The hybrid ARIMA(0,1,1)-LSTM(1-50-1) model showed competitive results, achieving MAPE of 2.00% for closing and 1.74% for opening prices using batch size 16. However, its success depends on ARIMA’s ability to model linear components. Key findings emphasize LSTM’s dominance in accuracy, the importance of parameter tuning, and the effectiveness of simple network structures. The hybrid approach holds promise when linear and nonlinear data components are clearly separable. This research offers methodological insights for optimizing stock price prediction models and practical guidance for model configuration, contributing to the advancement of financial market forecasting.
Co-Authors Budi Susetyo Dela Gustiara Laily Nissa Atul Mualifah Mahda Al Maida Shalshabilla Shafa Yenni Angraini