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All Journal Indonesian Journal of Applied Research (IJAR) Pattimura International Journal of Mathematics (PIJMath)
Nurwan Nurwan
Departement Of Mathematics, Universitas Negeri Gorontalo
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Mathematics

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Comparison of Feature Selection Based on Computation Time and Classification Accuracy Using Support Vector Machine Salmun K Nasib; Fadilah Istiqomah Pammus; Nurwan; La Ode Nashar
Indonesian Journal of Applied Research (IJAR) Vol. 4 No. 1 (2023): Indonesian Journal of Applied Research (IJAR)
Publisher : Universitas Djuanda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30997/ijar.v4i1.252

Abstract

The goal of this research to compare Chi-Square feature selection with Mutual Information feature selection based on computation time and classification accuracy. In this research, people's comments on Twitter are classified based on positive, negative, and neutral sentiments using the Support Vector Machine method. Sentiment classification has the disadvantage that it has many features that are used, therefore feature selection is needed to optimize a sentiment classification performance. Chi-square feature selection and mutual information feature selection are feature selections that both can improve the accuracy of sentiment classification. How to collect the data on twitter taken using the IDE application from python. The results of this study indicate that sentiment classification using Chi-Square feature selection produces a computation time of 0.4375 seconds with an accuracy of 78% while sentiment classification using Mutual Information feature selection produces an accuracy of 80% with a required computation time of 252.75 seconds. So that the conclusion are obtained based on the computational time aspect, the Chi-Square feature selection is superior to the Mutual Information feature selection, while based on the classification accuracy aspect, the Mutual Information feature selection is more accurate than the Chi-Square feature selection. The recommendations for further research can use mutual information feature selection to get high accuracy results on sentiment classification
A Generalization of Chio’s Condensation Method Any Muanalifah; Yuli Sagita; Nurwan Nurwan; Aini Fitriyah; Rosalio Artes Jr
Pattimura International Journal of Mathematics (PIJMath) Vol 3 No 1 (2024): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol3iss1pp15-22

Abstract

The Chio condensation method is a method to compute the determinant of a matrix A where by reducing the order of the matrix to a matrix. In this paper, we will generalize the condition where can be equal to zero. To compute the determinant, we can choose any element of matrix A that is not equal to zero as a pivot element.
Wind Speed Category Characteristics in Bone Bolango Regency: A Markov Chain Approach Using the Beaufort Scale and Metropolis-Hastings Algorithm Saiful Pomahiya; Nurwan Nurwan; Nisky Imansyah Yahya; Salmun K. Nasib; Isran K. Hasan; Asriadi Asriadi
Pattimura International Journal of Mathematics (PIJMath) Vol 3 No 2 (2024): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol3iss2pp63-68

Abstract

This study models daily wind speed transitions in the Bone Bolango Regency using the Markov Chain Monte Carlo (MCMC) method and the Metropolis-Hastings algorithm, employing the Beaufort scale for wind speed classification. The research aims to predict the steady-state distribution of wind speeds and evaluate their temporal stability. Daily wind speed data from 2023, provided by the Meteorology, Climatology, and Geophysics Agency (BMKG), were categorized into three levels: calm, light breeze, and fresh breeze, based on the Beaufort scale. Transition probabilities were estimated using the Beta distribution, and simulations via the Metropolis-Hastings algorithm yielded the steady-state distribution. Results show a significant tendency for transitions from calm and light breeze categories to fresh breezes, with varying probabilities. Notably, calm conditions exhibit a 69% likelihood of transitioning to a light breeze. This research contributes to improving wind speed prediction models by integrating statistical algorithms with meteorological classifications. The findings have implications for enhancing short-term weather forecasts and developing predictive systems for regions with similar weather patterns.
Co-Authors Aini Fitriyah Any Muanalifah Asriadi Asriadi Fadilah Istiqomah Pammus Isran K. Hasan La Ode Nashar Nisky Imansyah Yahya Rosalio Artes Jr Saiful Pomahiya Salmun K. Nasib Salmun K. Nasib Yuli Sagita