ANALISIS KLASIFIKASI MODEL PENYAKIT PARKINSON DENGAN FITUR AKUSTIK YANG DIREPLIKASI MENGGUNAKAN METODE XGBOOST
Abstract
Parkinson’s disease is a neurodegenerative disorder characterized by the decline of motor and non-motor functions due to damage in dopamine-producing neurons. This study aims to develop a classification model for Parkinson’s disease based on acoustic features using the Extreme Gradient Boosting (XGBoost) algorithm. The dataset was obtained from Kaggle, consisting of 195 voice recordings with 24 acoustic features. Data preprocessing included normalization, feature selection using ANOVA F-test, and class balancing using the Synthetic Minority Oversampling Technique (SMOTE). The XGBoost model was trained and tested using an 80:20 train-test split. Evaluation results showed the model achieved an accuracy of 92%, with average precision, recall, and F1-score values of 0.93, 0.92, and 0.92, respectively. These findings indicate that XGBoost provides strong classification performance in detecting Parkinson’s disease based on patients’ voice features and holds promise for further development into AI-assisted diagnostic systems.
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[2] W. A. Ridho, T. Wuryandari, and A. R. Hakim, “Perbandingan Kinerja Metode Klasifikasi K-Nearest Neighbor Dan Support Vector Machines Pada Dataset Parkinson,” J. Gaussian, vol. 12, no. 3, pp. 372–381, 2024, doi: 10.14710/j.gauss.12.3.372-381.
[3] V. No, K. A. Wulandari, A. Nugraha, A. Luthfiarta, and L. R. Nisa, “Edumatic : Jurnal Pendidikan Informatika Peningkatan Akurasi Deteksi Dini Penyakit Parkinson melalui Pendekatan Ensemble Learning dan Seleksi Fitur Optimal,” vol. 8, no. 2, pp. 575–584, 2024, doi: 10.29408/edumatic.v8i2.27788.
[4] K. R. Nainggolan, F. Purnamasari, and A. F. Pulungan, “Prediksi Penyakit Parkinson Melalui Dataset Rekam Suara Dengan Menggunakan Algoritma Deep Neural Network,” J. Minfo Polgan, vol. 12, no. 1, pp. 401–409, 2023, [Online]. Available: https://jurnal.polgan.ac.id/index.php/jmp/article/view/12985
[5] A. Desiani, I. Ramayanti, M. Arhami, P. Studi Matematika, and F. Matematika dan Ilmu Pengetahuan Alam, “Diagnosis of Parkinson’S Disease Using K-Nearest Neightbour and Decision Tree C4.5 Algorithms,” vol. 12, no. 1, pp. 47–58, 2023.
[6] Shawn Hafizh Adefrid Pietersz, Basuki Rahmat, and Eva Yulia Puspaningrum, “Perbandingan Kinerja Arsitektur Resnet-50 Dan Googlenet Pada Klasifikasi Penyakit Alzheimer Dan Parkinson Berbasis Data MRI,” Uranus J. Ilm. Tek. Elektro, Sains dan Inform., vol. 2, no. 2, pp. 27–39, 2024, doi: 10.61132/uranus.v1i2.110.
[7] A. Ghifari and B. H. Prasetio, “Sistem Deteksi Dini Penyakit Parkinson Melalui Voice Pattern Menggunakan Fitur Jitter dan Shimmer,” J. Pengemb. Teknol. Inf. …, vol. X, no. X, pp. 1–8, 2024, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/13943
[8] “Parkinson disease.” Accessed: Mar. 05, 2025. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/parkinson-disease
[9] E. P. Yudha and N. F. Muhammad, “Sistem Otomatis untuk Deteksi Penyakit Parkinson Menggunakan Fuzzy K-NN,” J. Tek. Komput., vol. 9, no. 2, pp. 96–102, 2023, doi: 10.31294/jtk.v9i2.15933.
[10] M. A. Alhafizh and B. Henryranu, “Sistem Deteksi Dini Penyakit Parkinson Melalui Speech Pattern Menggunakan Metode Recurrence Quantification Analysis,” J. Pengemb. Teknol. …, vol. 1, no. 1, pp. 1–9, 2024, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/13941
[11] Niloofar Fadavi and Nazanin Fadavi, “Early recognition of parkinson’s disease through acoustic analysis and machine learning,” World J. Adv. Res. Rev., vol. 23, no. 2, pp. 1376–1388, 2024, doi: 10.30574/wjarr.2024.23.2.2271.
[12] G. Abdurrahman and M. Sintawati, “Implementation of xgboost for classification of parkinson’s disease,” J. Phys. Conf. Ser., vol. 1538, no. 1, 2020, doi: 10.1088/1742-6596/1538/1/012024.
[13] I. Karabayir, S. M. Goldman, S. Pappu, and O. Akbilgic, “Gradient boosting for Parkinson’s disease diagnosis from voice recordings,” BMC Med. Inform. Decis. Mak., vol. 20, no. 1, pp. 1–7, 2020, doi: 10.1186/s12911-020-01250-7.
[14] Y. Yu, “Comparative analysis of machine learning in diagnosing Parkinson ’ s : Utilizing vocal characteristics,” vol. 0, pp. 1–8, 2024, doi: 10.54254/2753-8818/40/20241202.
[15] I. Naeem, A. Ditta, T. Mazhar, M. Anwar, M. M. Saeed, and H. Hamam, “Voice biomarkers as prognostic indicators for Parkinson’s disease using machine learning techniques,” Sci. Rep., vol. 15, no. 1, pp. 1–17, 2025, doi: 10.1038/s41598-025-96950-3.
[16] D. Innovation, “Machine Learning in Business Analytics: Advancing Statistical Methods for Data- Driven Innovation,” pp. 104–111, 2023, doi: 10.32996/jcsts.
[17] J. Paucar-Escalante, M. A. da Silva, B. D. L. Sanches, A. Soriano-Vargas, L. S. Moriyama, and E. L. Colombini, “Machine Learning Strategies for Parkinson Tremor Classification Using Wearable Sensor Data,” pp. 1–28, 2025, [Online]. Available: http://arxiv.org/abs/2501.18671
[18] X. Xing, N. Luo, S. Li, L. Zhou, C. Song, and J. Liu, “Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms,” Front. Neurosci., vol. 16, no. March, pp. 1–10, 2022, doi: 10.3389/fnins.2022.701632.
[19] A. Ouhmida, A. Raihani, B. Cherradi, and O. Terrada, “A Novel Approach for Parkinson’s Disease Detection Based on Voice Classification and Features Selection Techniques,” Int. J. online Biomed. Eng., vol. 17, no. 10, pp. 111–130, 2021, doi: 10.3991/ijoe.v17i10.24499.
[20] M. A. Hossain and F. Amenta, “Machine Learning-Based Classification of Parkinson’s Disease Patients Using Speech Biomarkers,” J. Parkinsons. Dis., vol. 14, no. 1, pp. 95–109, 2024, doi: 10.3233/JPD-230002.
[21] F. Amato, L. Borzì, G. Olmo, and J. R. Orozco-Arroyave, “An algorithm for Parkinson’s disease speech classification based on isolated words analysis,” Heal. Inf. Sci. Syst., vol. 9, no. 1, pp. 1–15, 2021, doi: 10.1007/s13755-021-00162-8.
[22] N. R. Yousif, H. M. Balaha, A. Y. Haikal, and E. M. El-Gendy, “A generic optimization and learning framework for Parkinson disease via speech and handwritten records,” J. Ambient Intell. Humaniz. Comput., vol. 14, no. 8, pp. 10673–10693, 2023, doi: 10.1007/s12652-022-04342-6.
[23] J. M. Templeton, C. Poellabauer, and S. Schneider, “Classification of Parkinson’s disease and its stages using machine learning,” Sci. Rep., vol. 12, no. 1, pp. 1–11, 2022, doi: 10.1038/s41598-022-18015-z.
[24] V. Mylius et al., “The Parkinson disease pain classification system: Results from an international mechanism-based classification approach,” Pain, vol. 162, no. 4, pp. 1201–1210, 2021, doi: 10.1097/j.pain.0000000000002107.
Published
2025-12-31
How to Cite
MANALU, Roy Choky Andika et al.
ANALISIS KLASIFIKASI MODEL PENYAKIT PARKINSON DENGAN FITUR AKUSTIK YANG DIREPLIKASI MENGGUNAKAN METODE XGBOOST.
Jurnal Teknik Informasi dan Komputer (Tekinkom), [S.l.], v. 8, n. 2, p. 574-584, dec. 2025.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php?journal=Tekinkom&page=article&op=view&path%5B%5D=1981>. Date accessed: 21 apr. 2026.
doi: https://doi.org/10.37600/tekinkom.v8i2.1981.
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