Comparison of Machine Learning Techniques in the Classification of Parkinson’s Desease Sufferers


  • Titik Khotiah ITB Ahmad Dahlan Lamongan
  • David Fahmi Abdillah ITB Ahmad Dahlan Lamongan
  • Ilham Basri K ITB Ahmad Dahlan Lamongan
  • Fery Arianto ITB Ahmad Dahlan Lamongan
  • Abdul Rohman ITB Ahmad Dahlan Lamongan




Parkinson, K-NN, MLP, Linear Regression, SVM

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Parkinson's disease is a progressive and relatively common neurodegenerative  disorder in the central nervous system where sufferers can have difficulty moving. This disease has a high mortality rate in the world of around 9.3 million in 2021. Meanwhile, in Indonesia, it is estimated that as many as 12,980 people die every year due to Parkinson's cases. This increase in cases of death is due to the lack of information about the initial symptoms and dangers of the disease, besides it is important to know how to prevent it early.  Early detection of Parkinson's disease can prevent symptoms of a certain age thereby increasing life expectancy. The existence of a computer-based system for diagnosing Parkinson's disease is called a classification system where the system applies the Machine Learning method. This study aims to compare the performance of algorithms in the classification system of people with computer diseases. In this study, it used methods in  Machine Learning such as K-NN, Multi Layer Percepteron (MLP), Linear Regression and Support Vector Machine (SVM).  The data set in this study was obtained using the Weka application.  The dataset used was Parkinson's Disease data  totaling 195 rows of data taken from the UCI Machine Learning Repository Datasets.  The results  of the experiment based on the four algorithms showed that  the poor performance was the Multi Layer Percepteron approach  to regression data with an RSME value of 0.459.  Meanwhile, the k-Neural Network Algorithm  is a good classification technique forParkinson's problem with an RMSE value of 0.1895.


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Arrazaq, G. A., Magdalena, R., Atmaja, R. D., Prodi, S., Telecommunications, T., Elektro, F. T., & Telkom, U. (2017). Diagnosis of Parkinson's disease through analysis of walking patterns based on Vgrf using wavelets and Support Vector Machine Diagnosis of Parkinson's Disease Through Gait Analysis based on vgrf using wavelet and support vector machine. 4(2), 1855–1862.

Azzahra, J. F., Sumarti, H., & Kusuma, H. H. (2022). Classification of COVID-19 and SARS Cases Based on Texture Traits Using the Multi-Layer Perceptron Method. Journal of Physics, 12(1), 16–27.

Bismi, W., & Harafani, H. (2022). Comparison of Deep Learning Methods in Classifying MRI Scan Images of Brain Diseases. 12(3), 177–185.

Desinaini, L. N., Mualimah, A., Novitasari, D. C. R., & Hafiyusholeh, M. (2019). Fuzzy K-Nearest Neighbor (FKNN) application to detect Parkinson's disease. InPrime: Indonesian Journal of Pure and Applied Mathematics, 1(1), 8–16.

Handayani, P. K. (2021). Application of Support Vector Machine (SVM) algorithms for classification pattern analysis in Parkinson's Dataset. Indonesian Journal of Technology, Informatics and Science (IJTIS), 3(1), 31–35.

Haryadi, D., Marini Umi Atmaja, D., Rahman Hakim, A., & Suwaryo, N. (2021). Identify the risk level of stroke using multiple linear regression algorithms. Deny Haryadi, SNTEM, 1(November), 1198–1207.

Irnawati, O., & Nuraeni, N. (2022). Decision Tree Optimization with Particle Swarm Optimization for Gender Prediction Classification of E-Commerce Users. Journal of Information Engineering and Educational Technology, 6(1), 37–41.

Johanes, R. E., & Santoso, E. (2020). Implementation of the K-Nearest Neighbor Algorithm for Disease Detection Classification in Dogs. 4(5), 1580–1583.

Mozhdehfarahbakhsh, A., Chitsazian, S., Chakrabarti, P., Chakrabarti, T., Kateb, B., & Nami, M. (2021). MRI-based Deep Learning Model for Predicting Stages of Parkinson's Disease. MedRxiv, February, 2021.02.19.21252081.

Oktariza, Y., Amalia, L., Sobaryati, S., & Kurniawati, M. Y. (2019). Evaluation of the quality of life of Parkinson's patients based on levodopa-based therapy. Indonesian Journal of Clinical Pharmacy, 8(4), 246.

Prakoso, R. D. Y., Wiriaatmadja, B. S., & Wibowo, F. W. (2020). Classification System in Parkinson's Disease Using the K-Nearest Neighbor Method. National Seminar on Computer Technology & Science (SAINTEKS), 2016, 63–68.

Sumarlinda, S., & Lestari, W. (2022). K-Nearest Neighbor (KNN) Application for Cardiovascular Disease Classification. Proceedings of the National Seminar on Technology ... , 55, 259–262.

Tempola, F., Muhammad, M., & Khairan, A. (2018). Comparison of Classifications Between KNN and Naive Bayes on Determination of Volcano Status with K-Fold Cross Validation. Journal of Information Technology and Computer Science, 5(5), 577.

Utami, A. N. (2020). Classification of REM Behaviour Disorder Sleep Disorders Based on EEG Signals using Machine Learning. Journal of Intelligent Systems, 03(02), 216–230.

Zuriel, H. P. P., & Fahrurozi, A. (2021). Implementation of the Support Vector Machine Classification Algorithm For Twitter User Sentiment Analysis Of Psbb Policy. Scientific Journal of Computer Informatics, 26(2), 149–162.



Submitted Date: 2023-01-23
Accepted Date: 2023-01-23
Published Date: 2023-01-29

How to Cite

Titik Khotiah, David Fahmi Abdillah, Ilham Basri K, Fery Arianto, & Abdul Rohman. (2023). Comparison of Machine Learning Techniques in the Classification of Parkinson’s Desease Sufferers. Journal of Computer Networks, Architecture and High Performance Computing, 5(1), 129-137.