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All Journal SINTECH (Science and Information Technology) Journal JSiI (Jurnal Sistem Informasi) Jurnal Telematika EDUMATIC: Jurnal Pendidikan Informatika Generation Journal Jurnal UNITEK Jurnal Algoritma AL-UMRON: Jurnal Pengabdian kepada Masyarakat The Indonesian Journal of Computer Science
Sanjaya, Ucta Pradema
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Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Public Health Social Sciences

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

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Perbandingan Algoritma Random Forest, XGBoost, dan Logistic Regression untuk Prediksi Risiko Kekambuhan Kanker Tiroid Ais, Salma Rihadatul; Sanjaya, Ucta Pradema
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29664

Abstract

Thyroid cancer, although relatively rare (0.85-2.5% of all cancer cases), is of serious concern due to its higher prevalence in women and challenges in diagnosis due to limitations of conventional methods such as fine-needle aspiration biopsy and ultrasound. This study aims to predict the risk of thyroid cancer recurrence by applying random forest, XGBoost, and logistic regression methods. Classifying the recurrence of thyroid cancer using 14 dataset variables obtained from Ken Saras Hospital, which amounted to 2000 datasets. The data will be classified using 3 method models and evaluated using a confusion matrix to find the best accuracy evaluation value. Based on the evaluation results, logistic regression gets an accuracy value of 83%, and random forest and XGBoost get an accuracy of 82%. Our findings prove that machine learning approaches can serve as an effective clinical decision support system in improving diagnosis efficiency and facilitating timely medical interventions. The implementation of this in clinical practice still requires integration with comprehensive medical considerations and supervision of healthcare professionals to ensure safety. The results contribute to the development of more reliable and efficient thyroid cancer diagnostic tools.
Co-Authors Ade Pratama Ais, Salma Rihadatul Damayanti, Hevi Alvina Diah Anggraini, Diah Dirgantoro, Guruh Putro Fauziyah, Dewi Nur Fetrika Anggraini, Fetrika Heni Setyowati Hevi Alvina Damayanti Ilhamy, Rifqi Syahrul Irsyada, Rahmat Ita Aristia Sa'ida Iwan Setiawan Wibisono Nita Cahyani Prastya, Ifnu Wisma Dwi Pribadi , Teguh Sri Mujiyono Wibisono, Bagas Zakki Alawi Zayn, Afta Ramadhan