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All Journal Jurnal Pendidikan Islam JURNAL PENDIDIKAN TAMBUSAI Jurnal Teknologi Informatika dan Komputer Jurnal Pendidikan Sains dan Komputer LEBAH Karimah Tauhid RECORD Journal of Loyality and Community Development
Uus Firdaus
Universitas Djuanda
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Analisis Kinerja Decision Tree dan Random Forest Menggunakan Dataset Breast Cancer: Performance Analysis of Decision Tree and Random Forest Using Breast Cancer Dataset Firdaus, Uus; Alfiah, Ananda; Mohdo, Lorina
Jurnal Pendidikan Sains dan Komputer Vol. 6 No. 01 (2026): Call for Papers, February 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jpsk.v6i01.7892

Abstract

Breast cancer is a disease with a high mortality rate in women worldwide, making early detection a crucial factor in increasing the chances of successful treatment and patient survival. Advances in computing technology, particularly machine learning, have created opportunities to use medical data to inform decision-making in the diagnostic process. This study aims to analyze and compare the performance of the Decision Tree and Random Forest algorithms in classifying breast cancer using the Wisconsin Breast Cancer Dataset. The dataset comprises 569 data points with 30 numeric attributes representing cancer cell characteristics and a class label indicating benign or malignant cancer. The research stages include data preprocessing, splitting the data into training and test sets (80:20), applying the classification algorithm, and evaluating model performance using accuracy, precision, recall, and F1-score metrics. The test results show that the Random Forest algorithm outperforms the Decision Tree. Random Forest achieved an accuracy of 98.68% on the training data and 95.61% on the test data, while Decision Tree achieved an accuracy of 96.92% on the training data and 91.23% on the test data. This difference indicates that Random Forest has better generalization capabilities and is more resistant to overfitting. The findings of this study indicate that Random Forest is more effective for data-based breast cancer classification than Decision Tree. Therefore, the Random Forest algorithm is recommended as a more reliable method to support decision support systems in early breast cancer detection.
Co-Authors Agung, Ari Permana Agustiani, Tia Agustina Multi Purnomo Aisah Rini Susanti Aisah Rini Susanti Alfiah, Ananda Apriansyah, Dwi Ardiansah, Ginan Asep Bayu Dani Nanyanto Azhar, Azharudin Azharudin Azharudin Azharudin, Azharudin Dani Nanyanto, Asep Bayu Dzaki, Mahesa Fachrani, Jihan Fadilah, Muhammad Risyad Falah, Muhammad Noufal Faris, Bintang Augri Faturachman Achmad Febrihartina, Siti Sarah Gunawan, Muhammad Erlangga Hakim, Haikal Ali Handayani, Mutiara Nur Haq, M. Abu Ridho Abdul Hikmatulloh , Moch Fajar Ilyasa, Muhammad Daniel Jihan Fachrani Kalam, Muhammad Jiddan Lihawa, Sanifa M R, Alim Purnomo Aji Surya M. Juroihan Ma’shum Abdul Jabbar Moch Fajar Hikmatulloh Mohdo, Lorina Muhamad Fikri, Muhamad Muhammad Encep Novrian, Risfan Ogi, Aldino Putra, Ryandra R. Siti Pupu Fauziah Rafael Nuansa Ramadhan Ramadhon, Rafael Nuansa Rayyani, Thival Rejeki, Sri Meiylani Rianto, M. Rizki Risdianto Irawan Roestamy, Martin Rusdi Abdul Gani Saripudin, Mip Siti Sarah Febrihartina Sodik, Japar Susantiq, Aisah Rini Syagara, Gheryyan Washesya Syah, Dava Alhamda Putra Washesya S, Gheryyan Zakaria, Muhammad Sultan Pasya