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All Journal Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Jurnal Kecerdasan Buatan dan Teknologi Informasi
Marroh, Zahrotul Isti’anah
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Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Nursing

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Journal : Jurnal Kecerdasan Buatan dan Teknologi Informasi

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MACHINE LEARNING-BASED CLASSIFICATION OF SPACE TRAVEL ELIGIBILITY USING SUPPORT VECTOR MACHINE, RANDOM FOREST, AND XGBOOST Zahroni, Teguh Rizali; Imran, Bahtiar; Tahrir, Muhammad; Muh. Akshar; Marroh, Zahrotul Isti’anah
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 4 No. 2 (2025): May 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v4i2.310

Abstract

This study applies machine learning classification techniques to predict passenger displacement events based on corrupted data retrieved from a hypothetical interstellar spacecraft mission. Using a cleaned and preprocessed dataset containing demographic, behavioral, and exposure-related features, we compare the performance of three classification models: Random Forest, Support Vector Machine (SVM), and XGBoost. Each model is trained on 80% of the data and evaluated on the remaining 20% using precision, recall, f1-score, and accuracy metrics. The SVM model shows the most notable improvement after feature selection, achieving a balanced performance across metrics. Meanwhile, Random Forest and XGBoost models maintain consistent and robust accuracy above 80% on both training and testing sets. Feature importance analysis also supports the interpretability of the models, particularly in Random Forest and XGBoost. The comparative analysis demonstrates that ensemble-based methods such as Random Forest and XGBoost are more effective in handling the complexity of the dataset, making them suitable for predictive tasks in high-dimensional, partially incomplete data scenarios.
Co-Authors Achmad, Andi Edy Murdani Z Imran, Bahtiar Muh. Akshar Muhammad Fajri Nur Fuadi Rahman Tahrir, Muhammad Zahroni, Teguh Rizali