Andriano, Vinson (2024) Perbandingan tingkat akurasi k-nearest neighbor dan extreme learning machine dalam diagnosis penyakit alzheimer. Bachelor thesis, Universitas Pelita Harapan.
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Abstract
Penyakit Alzheimer merupakan salah satu bentuk demensia yang paling umum dan
mempengaruhi jutaan orang di seluruh dunia. Penyakit ini bersifat degeneratif yang
menyebabkan penurunan fungsi kognitif secara bertahap, termasuk gangguan
memori, kemampuan berpikir, dan keterampilan sosial. Dengan meningkatnya
populasi lansia, prevalensi penyakit Alzheimer juga diperkirakan akan meningkat,sehingga penting untuk menemukan metode yang efektif untuk diagnosis dini, salah satunya adalah menggunakan machine learning. Pada penelitian ini, akan disajikan
perbandingan tingkat akurasi K-Nearest Neighbor dan Extreme Learning Machine
dalam diagnosis penyakit Alzheimer. Penelitian ini dimulai dari mengambil dataset
dari Kaggle, melakukan persiapan data, pelatihan model, pengujian model, hingga
visualiasi hasil pengujian model. Hasil penelitian menunjukkan bahwa Extreme
Learning Machine memiliki tingkat akurasi 94,67%, yang lebih tinggi daripada KNearest Neighbor dengan akurasi 92%. Reciever Operating Characteristic Curve
dan Area Under Curve Score sebagai metrik evaluasi menunjukkan performa
Extreme Learning Machine lebih baik dalam mendeteksi Alzheimer dibandingkan
dengan K-Nearest Neighbor. Maka dari itu, Extreme Learning Machine akan
digunakan untuk melakukan diagnosa penyakit Alzheimer. Hasil yang didapatkan
sebesar 94,37% kesesuaian hasil prediksi dengan label yang sebenarnya. /Alzheimer's disease is one of the most common forms of dementia and affects
millions of people worldwide. It is a degenerative disease that causes a gradual
decline in cognitive function, including impaired memory, thinking ability, and
social skills. With the increasing elderly population, the prevalence of Alzheimer's
disease is also expected to increase, making it important to find effective methods
for early diagnosis, one of which is using machine learning. In this study, a
comparison of the accuracy of K-Nearest Neighbor and Extreme Learning Machine
in the diagnosis of Alzheimer's disease will be presented. This research starts from
taking datasets from Kaggle, doing data preparation, model training, model testing,
to visualizing the results of model testing. The results show that Extreme Learning
Machine has an accuracy rate of 94.67%, which is higher than K-Nearest Neighbor
with an accuracy of 92%. Reciever Operating Characteristic Curve and Area
Under Curve Score as evaluation metrics show that Extreme Learning Machine
performs better in detecting Alzheimer's than K-Nearest Neighbor. Therefore,
Extreme Learning Machine will be used to diagnose Alzheimer's disease. The result
obtained is 94.37% compatibility of prediction results with the actual label.
Item Type: | Thesis (Bachelor) |
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Creators: | Creators NIM Email ORCID Andriano, Vinson NIM03081210023 vinsonandriano@gmail.com UNSPECIFIED |
Contributors: | Contribution Contributors NIDN/NIDK Email Thesis advisor Barus, Okky Putra 0127068803 okky.barus@uph.edu |
Uncontrolled Keywords: | alzheimer; machine learning; k-nearest neighbor; extreme learning machine; receiver operating characteristic curve; area under curve values |
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: | Vinson Andriano |
Date Deposited: | 20 Feb 2025 05:22 |
Last Modified: | 20 Feb 2025 05:22 |
URI: | http://repository.uph.edu/id/eprint/67090 |