APPLICATION OF RANDOM FOREST FOR EARLY DETECTION OF PARKINSON’S DISEASE WITH SOUND FREQUENCY DATA
DOI:
https://doi.org/10.35457/quateknika.v15i02.4563Keywords:
Parkinson’s disease, Random Forest, voice frequency, machine learning, early detectionAbstract
Parkinson’s Disease is a progressive neurological disorder that affects motor functions and verbal communication of the patients. Early detection of this disease is crucial to improving patients’ quality of life. This study aims to develop an early detection system for Parkinson’s Disease by utilizing sound frequency as the primary feature. The algorithm employed in this research is Random Forest, with the analysis process following the CRISP-DM approach, which includes six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Based on the test results, the developed model achieved an accuracy of 94.92% on the dataset used. These findings indicate that the Random Forest algorithm can be effectively implemented as an early detection system for Parkinson’s Disease using sound frequency data.
References
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[2] S. Alia et al., “Penyakit Parkinson: Tinjauan Tentang Salah Satu Penyakit Neurodegeneratif yang Paling Umum,” Jurnal Aksona, vol. 1, no. 2, 2021.
[3] L. Judijanto, A. Amin, and L. Nurhakim, “Implementasi Teknologi Artificial Intelligence dan Machine Learning dalam Praktik Akuntansi dan Audit: Sebuah Revolusi atau Evolusi,” COSMOS: Jurnal Ilmu Pendidikan, Ekonomi dan Teknologi, vol. 1, no. 6, pp. 3046–4846, 2024.
[4] K. A. Wulandari, A. Nugraha, A. Luthfiarta, and L. R. Nisa, “Peningkatan Akurasi Deteksi Dini Penyakit Parkinson melalui Pendekatan Ensemble Learning dan Seleksi Fitur Optimal,” Edumatic: Jurnal Pendidikan Informatika, vol. 8, no. 2, pp. 575–584, Dec. 2024, doi: 10.29408/edumatic.v8i2.27788.
[5] Y. Sulistyo Nugroho and dan Nova Emiliyawati, “Sistem Klasifikasi Variabel Tingkat Penerimaan Konsumen Terhadap Mobil Menggunakan Metode Random Forest,” Jurnal Teknik Elektro, 2017, [Online]. Available: http://archive.ics.uci.edu/ml/
[6] H. Tantyoko, D. Kartika Sari, and A. R. Wijaya, “PREDIKSI POTENSIAL GEMPA BUMI INDONESIA MENGGUNAKAN METODE RANDOM FOREST DAN FEATURE SELECTION,” 2023. [Online]. Available: http://jom.fti.budiluhur.ac.id/index.php/IDEALIS/indexHenriTantyoko|http://jom.fti.budiluhur.ac.id/index.php/IDEALIS/index|
[7] A. Nugroho, A. Z. Fanani, and G. F. Shidik, “Evaluation of Feature Selection Using Wrapper For Numeric Dataset With Random Forest Algorithm,” International Seminar on Application for Technology of Information and Communication (iSemantic), 2021, doi: 10.1109/iSemantic52711.2021.9573249.
[8] H. Akbar and W. K. Sanjaya, “Kajian Performa Metode Class Weight Random Forest pada Klasifikasi Imbalance Data Kelas Curah Hujan,” Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi, vol. 3, no. 1, Dec. 2023, doi: 10.20885/snati.v3i1.30.
[9] A. M. Elshewey, M. Y. Shams, N. El-Rashidy, A. M. Elhady, S. M. Shohieb, and Z. Tarek, “Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification,” Sensors, vol. 23, no. 4, Feb. 2023, doi: 10.3390/s23042085.
[10] D. Astuti, A. Rahmat Iskandar, and A. Febrianti, “Penentuan Strategi Promosi Usaha Mikro Kecil Dan Menengah (UMKM) Menggunakan Metode CRISP-DM dengan Algoritma K-Means Clustering,” Journal of Informatics, Information System, Software Engineering and Applications, vol. 1, no. 2, pp. 60–072, 2019, doi: 10.20895/INISTA.V1I2.
[11] S. Sucipto, D. Dwi Prasetya, and T. Widiyaningtyas, “Educational Data Mining: Multiple Choice Question Classification in Vocational School,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 2, pp. 379–388, Mar. 2024, doi: 10.30812/matrik.v23i2.3499.
[12] N. Cholifah Sastya and D. I. Nugraha, “Penerapan Metode CRISP-DM dalam Menganalisis Data untuk Menentukan Customer Behavior di MeatSolution,” JURNAL PENDIDIKAN DAN APLIKASI INDUSTRI, 2023, [Online]. Available: http://ejournal.unis.ac.id/index.php/UNISTEK
[13] A. P. Fadillah, “Penerapan Metode CRISP-DM untuk Prediksi Kelulusan Studi Mahasiswa Menempuh Mata Kuliah (Studi Kasus Universitas XYZ),” 2015.
[14] C. Schröer, F. Kruse, and J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 526–534. doi: 10.1016/j.procs.2021.01.199.
[15] J. Brzozowska, J. Pizoń, G. Baytikenova, A. Gola, A. Zakimova, and K. Piotrowska, “DATA ENGINEERING IN CRISP-DM PROCESS PRODUCTION DATA – CASE STUDY,” Applied Computer Science, vol. 19, no. 3, pp. 83–95, 2023, doi: 10.35784/acs-2023-26.
[16] Y. Sun, S. J. Chun, and Y. Lee, “Learned Semantic Index Structure Using Knowledge Graph Embedding and Density-Based Spatial Clustering Techniques,” Applied Sciences (Switzerland), vol. 12, no. 13, Jul. 2022, doi: 10.3390/app12136713.
[17] G. Airlangga, “Analysis of Machine Learning Classifiers for Speaker Identification: A Study on SVM, Random Forest, KNN, and Decision Tree,” Journal of Computer Networks, Architecture and High Performance Computing, vol. 6, no. 1, pp. 430–438, Jan. 2024, doi: 10.47709/cnahpc.v6i1.3487.
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APPLICATION OF RANDOM FOREST FOR EARLY DETECTION OF PARKINSON’S DISEASE WITH SOUND FREQUENCY DATA. (2025). Jurnal Qua Teknika, 15(02), 25-37. https://doi.org/10.35457/quateknika.v15i02.4563
