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All Journal Jurnal Kecerdasan Buatan dan Teknologi Informasi De Facto: Journal of international multidisciplinary sciences
Muh. Akshar
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Religion Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences

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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.
COST PERFORMANCE ANALYSIS OF INTEGRATED LABORATORY BUILDING CONSTRUCTION BASED ON BUDGET VARIANCE ANALYSIS Tahrir, Muhammad; Teguh Rizali Zahroni; Muh. Akshar; Risna Nona
DE FACTO : Journal Of International Multidisciplinary Science Vol 3 No 02 (2025): DE FACTO : Journal Of International Multidisciplinary Science
Publisher : Pusat Studi Ekonomi, Publikasi Ilmiah dan Pengembangan SDM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62668/defacto.v3i02.2399

Abstract

This study examines the cost performance of the Integrated Laboratory Building construction project by comparing the 2023 and 2025 Cost Budget Plans (Rencana Anggaran Biaya/RAB). The objective is to identify cost variances in each major work category and evaluate the efficiency of budget utilization. A descriptive quantitative approach was employed using official RAB documents from both periods as the primary data source. Variance analysis was conducted by calculating absolute and percentage differences between the estimated and contract values. The results indicate that the total budget in 2025 decreased by IDR 1.35 billion (2.91%) compared with the 2023 estimate. Significant cost increases occurred in electrical panel works (430.3%) and plumbing systems (152.7%), while the greatest efficiencies were achieved in façade works (13.6%) and air-conditioning systems (13.8%). Overall, the 2025 contract value achieved an efficiency level of 97.09% relative to the estimated budget, indicating good and efficient cost performance in project implementation.
Co-Authors Imran, Bahtiar Marroh, Zahrotul Isti’anah Risna Nona Tahrir, Muhammad Teguh Rizali Zahroni Zahroni, Teguh Rizali