IMPLEMENTASI ALGORITMA DECISION TREE DAN BINARY PARTICLE SWARM OPTIMIZATION UNTUK DIAGNOSIS PENYAKIT PARKINSON

DAIRINI, SITI (2023) IMPLEMENTASI ALGORITMA DECISION TREE DAN BINARY PARTICLE SWARM OPTIMIZATION UNTUK DIAGNOSIS PENYAKIT PARKINSON. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Parkinson's disease is a brain disorder that causes unwanted or uncontrolled movements, such as shaking, stiffness, and difficulty with balance or coordination. This disease can be detected medically through several tests such as an MRI, a CT scan, a brain ultrasound, and a PET scan. Furthermore, a neurologist will also diagnose Parkinson's disease based on medical history, signs, symptoms, and a neurological and physical examination. Symptoms of Parkinson's disease will increase with the patient's age. With the current development of information technology, the diagnosis of Parkinson's disease can also be done using machine learning. In this study, the focus will be on a comparison of the decision tree and KNN algorithms. Patient health examination data is processed using machine learning decision trees and KNN algorithms, which then produce a level of accuracy that can be used as a reference in determining Parkinson's disease. The accuracy of the Decision Tree Algorithm is 97.65% and the KNN algorithm is 63%. Keywords: Decision Tree, KNN, Clasification, Parkinson, Machine Learning, Algorithm Penyakit parkinson adalah gangguan pada otak yang menyebabkan gerakan yang tidak diinginkan atau tidak terkontrol, seperti gemetar, kaku, dan kesulitan dalam keseimbangan atau koordinasi tubuh. Penyakit ini dapat dideteksi secara medis melalui beberapa tes seperti MRI, CT Scan, USG otak, dan PET Scan. Selanjutnya, dokter ahli saraf juga akan mendiagnosis penyakit parkinson berdasarkan riwayat medis, tanda, gejala, dan pemeriksaan neurologis dan fisik. Gejala penyakit parkinson akan bertambah bersamaan dengan bertambahnya usia pasien. Dengan perkembangan teknologi informasi saat ini, diagnosis penyakit parkinson juga dapat dilakukan menggunakan machine learning. Dalam penelitian ini akan berfokus pada perbandingan algoritma Decision Tree dan KNN. Data pemerikasaan kesehatan pasien diolah menggunakan algoritma machine learning Decision Tree dan KNN, kemudian akan menghasilkan tingkat akurasi yang dapat dijadikan acuan dalam menentukan penyakit parkinson. Hasil akurasi dari Algoritma Decision Tree adalah 97,65% dan algoritma KNN adalah 63%. Kata kunci: Decision Tree, KNN, Klasifikasi, Parkinson, Machine Learning, Algoritma

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