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All Journal TEKNIK INFORMATIKA Jurnal Ilmiah FIFO Akuntabel : Jurnal Ekonomi dan Keuangan Jurnal Sisfokom (Sistem Informasi dan Komputer) Martabe : Jurnal Pengabdian Kepada Masyarakat JURNAL PENDIDIKAN TAMBUSAI Jurnal Algoritma, Logika dan Komputasi Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Telematika MKOM Media Abdimas Journal Collabits Jurnal Ilmiah Universitas Satya Negara Indonesia
Sari, Yunita Sartika
Program Studi Sistem Informasi, Fakultas Ilmu Komputer Universitas Mercu Buana
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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A Data Science Approach to Cancer Patient Classification Using Support Vector Machine and Random Forest Devi Dwi Anggraini; Mutiara Rizky Salsabila; Keisya Rizkia Kamila; Yunita Sartika Sari
Journal Collabits Vol. 3 No. 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37642

Abstract

The increasing availability of healthcare data has encouraged the application of data science and machine learning techniques in medical research. Cancer patient datasets contain numerical demographic and clinical attributes that can be utilized for classification tasks; however, complex feature relationships and limited feature relevance remain key challenges. This study aims to analyze cancer patient data and compare the performance of Support Vector Machine and Random Forest algorithms for gender classification. The dataset used in this study consists of numerical features, including patient age, tumor size, number of examined lymph nodes, number of positive lymph nodes, body mass index, and survival duration measured in months. The research methodology includes data preprocessing, exploratory data analysis, model development, and performance evaluation. Feature normalization and data splitting are applied to ensure a fair comparison between models, while exploratory analysis is conducted to examine data distribution and relationships among variables. Both classification models are trained under identical experimental settings and evaluated using accuracy as the primary performance metric. The results indicate that both algorithms can classify cancer patients with satisfactory accuracy. Support Vector Machine demonstrates slightly better performance compared to Random Forest, suggesting its effectiveness in handling numerical data with complex decision boundaries. The findings highlight the importance of appropriate algorithm selection and feature utilization in healthcare data analysis.
Co-Authors Ali Hadi Cherid Anggraini, Devi Dwi Ardi Cahyadi Yudistira Ayu Oktaviani, Ayu Bagus Priambodo Cherid, Ali Hadi Danny Yudin Djahidin Deny Friyansyah Devi Dwi Anggraini Ema Utami Fadli Fairuz Ramadhan Ferry Wahyu Wibowo Firdaus, Yaumil Hadi Santoso Hidayat, Rahmat Rian Ibrahim, Maulana Ifan Prihandi Ika Agustina Imaman, Rahardian Aulia Indah Fitriana Hapsari Inge Handriani Irma Hamidah Nasution Jumaryadi, Yuwan Kaburuan, Emil Robert Kamila, Keisya Rizkia Keisya Rizkia Kamila Kristanto, Dedy Kusrini Kusumaningsih, Dewi Lukman Hakim Lukman Hakim Misbahul Fajri muhamad nashar Muhammad Nashar Mutiara Rizky Salsabila Nia Rahma Kurnianda Nia Rahma Kurnianda, Nia Rahma Nicolaus Pramono Hardosubroto Nurjanah, Amelia Purnama, Nurlela Rahmah, Nia Raihan Nur Yasin Ratna Mutu Manikam Salsabila, Mutiara Rizky Setiawan, Fredi Wellhard Halomoan Simamora Yasin, Raihan Nur Yudo Devianto