Hutama, Elbert (2024) Perbandingan efektivitas support vector machine dan random forest dalam identifikasi diabetes dan penyakit jantung. Bachelor thesis, Universitas Pelita Harapan.
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Abstract
Penelitian ini bertujuan untuk membandingkan efektivitas algoritma
Support Vector Machine (SVM) dan Random Forest (RF) dalam identifikasi
penyakit diabetes dan jantung. Penyakit diabetes dan jantung merupakan dua
penyakit kronis yang prevalensinya semakin meningkat dan memerlukan metode
diagnosis yang cepat dan akurat untuk pengelolaan yang lebih baik. Dalam
penelitian ini menggunakan dataset yang diperoleh dari Kaggle, yang terdiri dari
data pasien dengan atribut-atribut relevan yang digunakan untuk diagnosis
penyakit. Algoritma SVM dan RF diterapkan untuk membangun model prediksi,
dan kinerja masing-masing model dievaluasi berdasarkan metrik akurasi, presisi,
recall, dan F1-score. Hasil penelitian menunjukkan bahwa model Stack dan
Random Forest (RF) memiliki tingkat keakuratan yang tinggi dan sama, yaitu
89,4% dalam mendeteksi penyakit diabetes. Di sisi lain, model Stack dan Support
Vector Machine (SVM) memiliki tingkat keakuratan yang tinggi dan sama, yaitu
81,8% dalam mendeteksi penyakit jantung. Kedua hasil ini menunjukkan bahwa
baik model Stack-RF maupun Stack-SVM efektif dalam mendeteksi penyakit
diabetes dan jantung, meskipun dengan tingkat akurasi yang berbeda. Penelitian
ini memberikan kontribusi dalam bidang data mining dan machine learning untuk
aplikasi medis, khususnya dalam pengembangan alat diagnostik berbasis machine
learning. Dengan demikian, hasil dari penelitian ini diharapkan dapat membantu
dalam pengembangan sistem pendukung keputusan klinis yang lebih akurat dan
efisien untuk deteksi dini penyakit diabetes dan jantung.
/
This study aims to compare the effectiveness of Support Vector Machine
(SVM) and Random Forest (RF) algorithms in identifying diabetes and heart
diseases. Diabetes and heart diseases are two chronic conditions with increasing
prevalence, requiring rapid and accurate diagnostic methods for better
management. In this research utilized a dataset obtained from Kaggle, comprising
patient data with relevant attributes used for disease diagnosis. SVM and RF
algorithms were employed to build predictive models, and the performance of
each model was evaluated based on accuracy, precision, recall, and F1-score
metrics. The results indicate that both the Stack-RF and RF models exhibit a high
level of accuracy, at 89.4%, in detecting diabetes. Conversely, both the Stack-
SVM and SVM models demonstrate a similar high accuracy rate of 81.8% in
detecting heart disease. These findings suggest that both Stack-RF and Stack-SVM
models are effective in detecting diabetes and heart diseases, albeit with differing
accuracy levels. This study contributes to the field of data mining and machine
learning for medical applications, particularly in the development of machine
learning-based diagnostic tools. Thus, the findings of this research are expected
to aid in the development of more accurate and efficient clinical decision support
systems for early detection of diabetes and heart diseases.
Item Type: | Thesis (Bachelor) |
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Creators: | Creators NIM Email ORCID Hutama, Elbert NIM03081200053 elberthutama8@gmail.com UNSPECIFIED |
Contributors: | Contribution Contributors NIDN/NIDK Email Thesis advisor Haryani, Calandra NIDN0307079302 calandra.haryani@uph.edu |
Uncontrolled Keywords: | support vector machine, random forest, prediksi penyakit, diabetes, penyakit jantung, machine learning, data mining. |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | University Subject > Current > Faculty/School - UPH Medan > School of Information Science and Technology > Information Systems Current > Faculty/School - UPH Medan > School of Information Science and Technology > Information Systems |
Depositing User: | Elbert Hutama |
Date Deposited: | 09 Aug 2024 02:37 |
Last Modified: | 09 Aug 2024 02:37 |
URI: | http://repository.uph.edu/id/eprint/64760 |