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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Pertanian Indonesia FORUM STATISTIKA DAN KOMPUTASI Media Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi Disease Prevention and Public Health Journal JURNAL PETERNAKAN Sosioinforma JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) JURNAL ILMIAH PETERNAKAN TERPADU J Statistika : Jurnal Ilmiah Teori Dan Aplikasi Statistika Zero : Jurnal Sains, Matematika, dan Terapan Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications J-ADIMAS (Jurnal Pengabdian kepada Masyarakat) JURNAL AKUNTANSI KEUANGAN DAN MANAJEMEN Inferensi Jurnal Pendidikan dan Kebudayaan Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Statistika dan Aplikasinya JURNAL ILMIAH GLOBAL EDUCATION Jurnal Indonesia Sosial Sains Jurnal Natural Xplore: Journal of Statistics Jurnal Pengabdian Masyarakat dan Lingkungan Parameter: Jurnal Matematika, Statistika dan Terapannya PRAGMATIS Jurnal Manajemen dan Bisnis Jurnal Medika Veterinaria Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications Jurnal Info Kesehatan Limits: Journal of Mathematics and Its Applications
Indahwati
Department Of Statistics, School Of Data Science, Mathematics And Informatics, IPB University, Campus IPB University, Bogor 16680, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Transportation Veterinary Other

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Performance Evaluation of Multinomial Logistic Regression, Random Forest, and XGBoost Methods in Data Classification Mega Maulina; Yani Prihantini Hiola; 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.
A Fuzzy K-Prototypes Ensemble SMOTE-NC SVM Framework for Open Unemployment Classification Setyo Wahyudi; Indahwati Indahwati; Made Sumertajaya
Journal of Mathematics, Computations and Statistics Vol. 9 No. 3 (2026): Volume 09 Issue 03 (September 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/39jhr427

Abstract

Open unemployment classification using labor force survey data presents a significant challenge because data are highly imbalanced and consist of both numerical and categorical variables. Conventional classification models often achieve high overall accuracy but exhibit limited capability in identifying unemployed individuals, who represent the minority class. This study proposes a hybrid classification framework Fuzzy K-Prototype clustering, Synthetic Minority Over-sampling Technique for Nominal and Continuous variables (SMOTE-NC), support vector Machine, and weighted ensemble learning to improve minority-class identification in mixed-type labor force survey data. Unlike previous clustering-based ensemble approaches that primarily employ K-Means and conventional SMOTE, the proposed framework utilized Fuzzy K-Prototypes to accommodate mixed-type variables and SMOTE-NC to generate synthetic minority observations within homogenous clusters. The model was evaluated using the 2024 National Labor Survey (Sakernas) data from West Java Province, Indonesia. Experimental result indicate that proposed framework increased Recall (Sensitivity) from 73.93% to 77.94%, demonstrating an improved ability to identify unemployed individuals. Although Accuracy, Precision, Specificity, F1-score, and Balanced Accuracy decreased compared with the conventional SVM model, the proposed framework provides a more appropriate classification strategy for highly imbalance labor force datasets, where accurate identification of unemployed individuals is more important than maximizing overall classification accuracy, thereby offering valuable support for labor market analysis and employment policy development.
Co-Authors A. A., Muftih Aam Alamudi Aditya Ramadhan Agus Mohamad Soleh Agustini , Ni Ketut Yulia Agustini, Ni Ketut Yulia Aji Hamim Wigena Akbar Rizki Alahmad, Ali Omar Alfi Indah Nurrizqi Aliu, Mufthi Alwi ALIU, MUFTIH ALWI Amelia, Reni Amin, Yudi Fathul Anang Kurnia Andi Harismahyanti A. Anik Djuraidah annida marsa salsabila Antonius Benny Setyawan Ardi Surya Ari Handayani Arie Anggreyani Aristawidya, Rafika ASEP SAEFUDDIN Assyifa Lala Pratiwi Hamid Aunuddin . Bagus Sartono Baiq Dian Arianingsih Budi Susetyo Bukhari, Ari Shobri Cahya, Septa Dwi Cahyani Oktarina Chrisinta, Debora Dairul Fuhron Daswati, Oktaviyani Dea Fisyahri Akhilah Putri Dian Kusumaningrum Erfiani Erfiani Erfiani Erfiani Etis Sunandi Eva Wany, Eva Evita Purnaningrum Farit Mochamad Afendi Farit Mohamad Afendi Fatimah Fatimah Fira Nurahmah Al Aminy Fitrianto, Anwar Fulazzaky, Tahira Ghina Fauziah Hanifa Izzati Hanifa Izzati Hari Wijayanto Hasanah, Lailatul I Gusti Putu Purnaba I Made Sumertajaya Iin Maena Ilham Azagi Indah, Yunna Mentari Intan Juliana Panjaitan Irawan Irawan Jasmin Nur Hanifa Jaya, Eddy Santosa Julianti, Elisa D Kamil, Farid Ikram Karunia, Nia Kayla Fakhriyya Jasmine Kefi Amtiran, Chandraone Putra Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Kholidiah, Kholidiah Khusnia N. K. Khusnia Nurul Khikmah Kristorio, Kevin Kusman Sadik Laily Nissa Atul Mualifah Latifah, Leli Lestari, Nila Lili Puspita Rahayu Logananta Puja Kusuma Made Sumertajaya Maghfiroh, Firda Aulia Mega Maulina Miranti, Ita Miranti, Ita Mohammad Masjkur Mualifah, Laily Nissa Muftih Alwi Aliu Muhammad Nur Aidi Naima Rakhsyanda Narindria, Yasmin Nadhiva Nurul Fadhilah Panjaitan, Intan Juliana Pika Silvianti Puput Cahya Ambarwati Putra, Stefanus Morgan Setyadi Perdana Putri, Christiana Anggraeni Ramdani, Indri Rasyid, Baharun Ray Sastri Regan, Regan Reni Amelia Reni Amelia Reza, Charolina Therezia Rifki Hamdani Rindy Anggun Pertiwi Rizka Annisa Mingka Salvina Salvina Setyo Wahyudi Silmi Annisa Rizki Manaf Siti Hafsah Siwi Haryu Pramesti Tina Aris Perhati Titin Agustina Titin Suhartini Titin Suhartini, Titin Titin Yuniarty Yuniarty Tri Wahyuni Utami Dyah Syafitri Vera Maya Santi Via Sulviana Vitona, Desi Wahyudi Setyo Wandee Wanishsakpong Widya Putri Nurmawati Yani Prihantini Hiola Yenni Angraini Yuniarty, Titin Yunna Mentari Indah Zulkarnain, Rizky _ Aunuddin