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All Journal FORUM STATISTIKA DAN KOMPUTASI CAUCHY: Jurnal Matematika Murni dan Aplikasi Desimal: Jurnal Matematika Xplore: Journal of Statistics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications
Aam Alamudi
Department Of Statistics, Bogor Agricultural University (IPB)
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Mathematics Social Sciences Other

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Journal : Journal of Mathematics, Computation and Statistics (JMATHCOS)

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Performance Evaluation of Multinomial Logistic Regression, Random Forest, and XGBoost Methods in Data Classification Mega Maulina; Hiola, Yani Prihantini; Indahwati; Aam Alamudi
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.8459

Abstract

The development of data volume and complexity in the digital era increases the need for effective classification methods to support decision-making. Decision-making in classification tasks often requires methods that are well-suited to the data, along with the ability to produce accurate and reliable predictions. As scientific knowledge continues to advance, a wide range of classification methods have been developed. This study aims to analyze the performance of three commonly used classification methods Multinomial Logistic Regression, Random Forest, and XGBoost, in handling diverse data characteristics. Ten varied public datasets were used in this research, with differences in the number of classes, features, instances, balanced and imbalanced data conditions. Evaluation was conducted based on accuracy, F1-score, precision, and recall. The analysis results show that Random Forest consistently delivers the best performance particularly on imbalanced data. XGBoost demonstrates superiority on more complex datasets, while Multinomial Logistic Regression proves more effective on relatively small datasets. This research provides valuable insights into selecting appropriate classification methods based on data characteristics and highlights the effectiveness of ensemble-based approaches in handling diverse data. Based on the findings, it is recommended that the selection of classification algorithms be tailored to the characteristics of the dataset. Random Forest is preferable for handling imbalanced data, while XGBoost is ideal for complex datasets requiring robust hyperparameter tuning. Multinomial Logistic Regression remains a viable option for simpler datasets with fewer observations and features. Future research could explore hybrid models that combine these approaches to further optimize classification performance across various domains.
Co-Authors . Aunuddin Agus M Soleh Agus M Soleh Agus Soleh Akmala Firdausi Anik Djuraidah Aulia Dwi Oktavia Ayoe Indria Winuri Azira Irawan Bagus Sartono Budi Susetyo Dhika Firmansyah Dian Kusumaningrum Else Virdiani Eria, Raisa Nida Erlinda Widya Widjanarko Farit Mochamad Afendi Fitrianto, Anwar Hanifatun Nufusia Hiola, Yani Prihantini Indahwati Indonesian Journal of Statistics and Its Applications IJSA Iqbal Hanif Itasia Dina Sulvianti Kusman Sadik Mahdiyah Riaesnianda Maya Deanti Mega Maulina Muhammad Farhan Zahid Nadhif Nursyahban Ni Kadek Manik Dewantari Nisa Nur Aisyah Pika Silvianti Pika Silvianti Putri, Oktaviani Aisyah Rahardiantoro, Septian Rahma Anisa Rahma Anisa Resty Fanny Riansyah, Boy Silmi Annisa Rizki Manaf Tania Amalia Darsono Utami Dyah Syafitri Yasmin Erika Faridhan Yenni Angraini