Prediksi Penyakit Parkinson Menggunakan Gradient Boosting Berbasis Orange Data Mining

Albert Adolf Angie MS; Yeheskiel Ravena Damanik; Esther Laura Rumahorbo; Elisabeth Jessica Sitanggang

doi:10.31004/riggs.v5i2.12741

Authors

Albert Adolf Angie MS Universitas HKBP Nommensen Pematangsiantar
Yeheskiel Ravena Damanik Universitas HKBP Nommensen Pematangsiantar
Esther Laura Rumahorbo Universitas HKBP Nommensen Pematangsiantar
Elisabeth Jessica Sitanggang Universitas HKBP Nommensen Pematangsiantar

DOI:

https://doi.org/10.31004/riggs.v5i2.12741

Keywords:

Gradient Boosting, Parkinson, Machine Learning, Orange Data Mining, Klasifikasi Penyakit

Abstract

Penyakit Parkinson merupakan gangguan neurodegeneratif yang memengaruhi sistem saraf dan kemampuan motorik sehingga deteksi dini sangat diperlukan untuk meningkatkan efektivitas penanganan. Penelitian ini bertujuan membangun model klasifikasi penyakit Parkinson menggunakan algoritma Gradient Boosting pada aplikasi Orange Data Mining. Dataset yang digunakan adalah Parkinson's Disease Dataset yang diperoleh dari Kaggle dan terdiri atas 195 data dengan 24 atribut, di mana atribut status digunakan sebagai target klasifikasi. Tahapan penelitian meliputi seleksi atribut, praproses data, pembangunan model, serta evaluasi menggunakan metode 5-Fold Cross Validation. Kinerja model diukur menggunakan metrik Accuracy, Area Under Curve (AUC), Precision, Recall, F1-Score, dan Matthews Correlation Coefficient (MCC). Hasil pengujian menunjukkan bahwa algoritma Gradient Boosting memperoleh nilai Accuracy sebesar 94,4%, AUC sebesar 0,980, Precision sebesar 94,4%, Recall sebesar 94,4%, F1-Score sebesar 0,942, dan MCC sebesar 0,845. Nilai tersebut menunjukkan bahwa model memiliki kemampuan yang sangat baik dalam membedakan pasien Parkinson dan individu sehat. Dengan demikian, algoritma Gradient Boosting berpotensi diterapkan sebagai sistem pendukung keputusan untuk membantu proses deteksi dini penyakit Parkinson berbasis karakteristik suara.

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