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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Masyarakat Informatika FIKI 2013 Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Pseudocode International Journal of Advances in Intelligent Informatics Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Moneter : Jurnal Keuangan dan Perbankan Community: Jurnal Pengabdian Pada Masyarakat
Ika Novita Dewi
Universitas Dian Nuswantoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : Jurnal Masyarakat Informatika

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Comparative Evaluation of Machine Learning Algorithms with Data Balancing Approach and Hyperparameter Tuning in Predicting Thyroid Disorder Recurrence Darnell Ignasius; Rhyan David Levandra; Ramadhan Rakhmat Sani; Ika Novita Dewi
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): November 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.75073

Abstract

This research evaluates and compares the performance of five machine learning algorithms (Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosting) in predicting thyroid disease recurrence using patient data. The analysis was conducted on the Thyroid Disease Dataset from the UCI Machine Learning Repository. The methodology includes data preprocessing, normalization, and class balancing with the Synthetic Minority Over-sampling Technique (SMOTE). Additionally, hyperparameter tuning was conducted using GridSearchCV to optimize model performance. The results demonstrate that ensemble-based models, specifically Random Forest and Gradient Boosting, consistently outperform the other algorithms in terms of accuracy and robustness. These models achieve 95–96% accuracy across various scenarios.A key finding is that SMOTE significantly improves recall for minority classes, highlighting its value in imbalanced medical datasets.
Co-Authors Abas Setiawan Abdul Syukur Abdul Syukur Abu Salam Adhitya Nugraha Adriani, Mira Riezky Agung Priyo Utomo, Rino Agustin, Kristina Alzami, Farrikh Ardytha Luthfiarta Arifin, Muhammad Farhan Arry Maulana Syarif, Arry Maulana Arunia, Aurelya Prameswari Asih Rohmani, Asih Atha Rohmatullah, Fawwaz Ayuningsih, Dewi Putri Azhari Azhari Bramantyo, Satrio Bisma Candra Irawan Catur Supriyanto Darnell Ignasius Diana Aqmala Dwi Puji Prabowo, Dwi Puji Dzaki, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erika Devi Udayanti Erwin Yudi Hidayat Erwin Yudi Hidayat Fahri Firdausillah Fajar Agung Nugroho Fitriyani, Shelomita Hafiizhudin, Lutfi Azis Handayani, Sri Haresta, Alif Agsakli Hasan Asari Heribertus Himawan Ifan Rizqa Indrayani, Heni Irawan, Enrico Irvan Muzakkir Irvan Muzakkir Isworo, Slamet Junta Zeniarja Khafiizh Hastuti Khariroh, Shofiyatul Kurniawan, Defri Laurent, Feby Lisa Mardiana Marjuni, Aris Megantara, Rama Aria Muljono Muljono Mumtaz, Najma Amira MY. Teguh Sulistyono Norman, Maria Bernadette Chayeenee Octaviani, Dhita Aulia Priyo Utomo, Rino Agung Puri Sulistiyawati Pusung, Elvanro Marthen Ramadhan Rakhmat Sani Reza, Ivan Muhammad Rhyan David Levandra Ricardus Anggi Pramunendar Rifamuthia, Titis Ritzkal, Ritzkal Safira, Almira Zuhrotus Salsabilla, Annisa Ratna Saputra, Filmada Ocky Sholikun, Sholikun Sindhu Rakasiwi Sri Winarno Subowo, Moh Hadi Sulistyono, Teguh Suyatno, Revalina Syarifah, Ulima Muna Utomo, Danang Wahyu Wellia Shinta Sari Wibowo, Isro' Rizky Yanuaresta, Dianna Zainal Arifin Hasibuan