PREDIKSI HIPERTENSI DENGAN ALGORITMA MACHINE LEARNING MENGGUNAKAN K-NEAREST NEIGHBORS (K-NN)

ABDILLAH, FADLY LUTFIAN (2024) PREDIKSI HIPERTENSI DENGAN ALGORITMA MACHINE LEARNING MENGGUNAKAN K-NEAREST NEIGHBORS (K-NN). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Hypertension, or high blood pressure, is a global non-communicable health issue that can lead to serious complications such as heart attacks, strokes, and kidney failure if not properly managed and identified. The risk of hypertension is caused by several contributing factors such as age, dietary patterns, height and weight, physical activity, and others. Therefore, early prediction of hypertension is crucial in the efforts to prevent and manage this condition. One proven approach to predict the risk of hypertension is the use of Machine Learning algorithms, specifically the K-Nearest Neighbors (K-NN) algorithm. The K-NN algorithm is a Machine Learning method that focuses on clustering data based on feature similarity. In the context of hypertension prediction, patient data, including information such as age, gender, address, educational status, occupation, marital status, blood type, smoking habits, physical inactivity, excess sugar intake, excess salt intake, excess fat consumption, lack of fruit consumption, alcohol consumption, systolic and diastolic blood pressure, height, weight, waist circumference, glucose examination, hospital referral, diagnosis 1, and other health parameters, are used as features to make predictions. The K-NN algorithm works by finding the k-nearest neighbors of the test data point in the feature space, where k is the number of nearest neighbors to be considered. Hypertension prediction for new patients is based on the majority class of hypertension from the K-NN. Keywords : Machine Learning, K-NN, Hypertension, Prediction, Hipertensi atau tekanan darah tinggi, merupakan salah satu masalah kesehatan global yang tidak menular namun dapat menyebabkan komplikasi serius seperti serangan jantung, stroke dan gagal ginjal jika tidak dikelola dan diidentifikasi dengan baik. Resiko hipertensi disebabkan oleh beberapa faktor penyebab seperti usia, pola makan, tinggi badan dan berat badan, olahraga dan lain lain. Oleh karena itu, prediksi dini hipertensi menjadi hal yang penting dalam upaya pencegahan dan pengelolaan penyakit ini. Salah satu pendekatan yang telah terbukti efektif dalam memprediksi risiko hipertensi adalah penggunaan algoritma Machine Learning, khususnya algoritma K-Nearest Neighbors (K-NN). Algoritma K-NN adalah metode Machine Learning yang berfokus pada pengelompokan data berdasarkan kemiripan fitur. Dalam konteks prediksi hipertensi, data pasien yang mencakup informasi seperti usia, Pasien, Alamat, Status Pendidikan, Pekerjaan, Status Perkawinan, Golongan Darah, Merokok, Kurang Aktivitas Fisik, Kelebihan Gula, Kelebihan Garam, Kelebihan Lemak, Kurang Makan Buah, Konsumsi Alkohol, Sistol, Diastol, Tinggi Badan, Berat Badan, Lingkar Perut, Pemeriksaan Gula, Rujukan Rumah Sakit, Diagnosa 1, dan parameter kesehatan lainnya digunakan sebagai fitur untuk melakukan prediksi. Algoritma K-NN bekerja dengan cara mencari k-nearest neighbors dari data uji dalam ruang fitur, di mana k adalah jumlah terdekat yang akan dipertimbangkan. Prediksi hipertensi untuk pasien yang baru didasarkan pada mayoritas kelas hipertensi dari K-NN. Kata Kunci : Machine Learning, K-NN, Hipertensi, Prediksi

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 24 103
Call Number:	SIK/15/24/069
NIM/NIDN Creators:	41519010051
Uncontrolled Keywords:	Machine Learning, K-NN, Hipertensi, Prediksi
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	27 Jun 2024 02:56
Last Modified:	27 Jun 2024 02:56
URI:	http://repository.mercubuana.ac.id/id/eprint/89209

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