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All Journal Jurnal Keperawatan dan Kesehatan Masyarakat CENDEKIA UTAMA IJNP (Indonesian Journal of Nursing Practices) Jurnal Penelitian Kesehatan Suara Forikes JNKI (Jurnal Ners dan Kebidanan Indonesia) (Indonesian Journal of Nursing and Midwifery) JURNAL PENDIDIKAN TAMBUSAI Sahabat Sosial: Jurnal Pengabdian Masyarakat Barongko : Jurnal Ilmu Kesehatan International Journal of Health Sciences Jurnal Berita Ilmu Keperawatan Journal of Innovative and Creativity JIMAD : Jurnal Ilmiah Multidisiplin
Mahoklory, Serly Sani
STIKes Maranatha Kupang
Religion Humanities Computer Science & IT Dentistry Economics, Econometrics & Finance Education Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Analisis Perbandingan Algoritma Klasifikasi Dalam Memprediksi Penyakit Menggunakan Data Mining Mahoklory, Serly Sani; Idris, Idris; Rosida, Rosida
JIMAD : Jurnal Ilmiah Multidisiplin Vol. 3 No. 2 (2026): JIMAD : Jurnal Ilmiah Multidisiplin (January)
Publisher : Asosiasi Guru dan Dosen Seluruh Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59585/jimad.v3i2.1017

Abstract

Advances in data mining technology enable the use of classification algorithms to predict diseases based on patient data. Selecting the right algorithm is an important factor because it affects the accuracy and effectiveness of the prediction results. This study aims to analyze the performance comparison of three classification algorithms, namely Naïve Bayes, Decision Tree (C4.5), and Support Vector Machine (SVM) in predicting diseases using a health dataset. The research method used was a quantitative experiment with stages of data pre-processing, dataset division, algorithm application, and model evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The results show that the SVM algorithm provides the highest accuracy (88%), Decision Tree produces moderate accuracy (82%) with the advantage of interpretability, while Naïve Bayes is faster but less accurate (78%). These findings confirm that algorithm selection needs to consider both accuracy and ease of interpretation in supporting medical decision-making.
Co-Authors Achmad, Viyan Septiyana Angriawan, Angriawan Antonius Rino Vanchapo Aripa, Lusyana Badaruddin Badaruddin bubun, juhelnita Cahya, Mochamad Robby Fajar Djunaedi Djunaedi Dunggio, Abdul Rivai Saleh Dwi Nur Aini, Dwi Nur Ervan, Ervan Fachry Abda El Rahman Ferdic Sukma Wahyudin Ferdinandus Suban Hoda Ferdinandus Suban Hoda Fetty Yublika Pasole Fitriani.K, Fitriani.K GONSALVES, DOMINGGOS Haedir, Haedir Hoda, Ferdinandus Suban Ibrahim, Sultan Akbar Idris Idris Irlin Falde Riti, Irlin Falde Jenita Laurensia Saranga Lewar, Emanuel Suban Bala Lili Amaliah Mahendika, Devin Medy Purwanto Megasari, Anis Laela Merlin, Ni Made Mohammad Arifin Noor Muchsin, Wachyudi Mudrika Mudrika Musaidah, Musaidah Nazara, Desman Serius Noyumala NUR ASBON Nurlaelah Nurlaelah Nursinah, Andi Okto Elferson Lusi Pannyiwi, Rahmat Pella, Yosin Herloheti Pello, Yakoba Ketzia Rahmat, Rezqiqah Aulia Rahmawati, Agustini Liviana Dwi Ramli, Rahmat Rasimin, Rosmini Rezqiqah Aulia Rahmat Rosida Rosida Safridha Kemala Putri Sahalessy, Yona C. Saputra, M. Khalid Fredy Sari, Dwi Ardyna Octa Sembiring, Endam Apulina Br Suharto Suharto Syarief, Syarifah Wahyuni Al Tondok, Santalia Banne Ulul Asmy Umar, Risa Bernadip Viyan Septiyana Achmad Wahyuni, Ria Yaroserai, Mais Yudi Muammar Yunitasari, Veronika Yusnayanti, Cici Zaenal Zaenal, Zaenal