KOMPARASI ALGORITMA DECISION TREE DAN KNN DENGAN OPTIMASI FEATURE SELECTION MENGGUNAKAN GENETIC ALGORITHM DALAM MEMPREDIKSI RISIKO HIPERTENSI

MARZUKI, ARENGA PINNATA (2025) KOMPARASI ALGORITMA DECISION TREE DAN KNN DENGAN OPTIMASI FEATURE SELECTION MENGGUNAKAN GENETIC ALGORITHM DALAM MEMPREDIKSI RISIKO HIPERTENSI. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Hypertension is a condition characterized by elevated blood pressure levels, with systolic pressure ≥ 140 mmHg and diastolic pressure ≥ 90 mmHg. According to the World Health Organization (WHO) in 2023, approximately 1.28 billion adults aged 30-79 worldwide suffer from hypertension, primarily in developing countries, with 46% of them unaware of their condition. This study aims to predict hypertension risk using KNN and Decision Tree algorithms, optimized through feature selection with Genetic Algorithm. The dataset used consists of 12 features and 4,240 records. The results show that the Decision Tree algorithm achieved the best performance with an accuracy of 90.6%, precision of 92.3%, recall of 89.2%, and F1-Score of 90.8%. Feature optimization successfully improved the accuracy of both algorithms, demonstrating the effectiveness of Genetic Algorithm in feature selection for hypertension risk prediction. Keywords: Genetic Algorithm, Decision Tree, K-Nearest Neighbours, Optimization, Hypertension, Classification. Hipertensi adalah kondisi peningkatan tekanan darah di atas batas normal, dengan tekanan sistolik ≥ 140 mmHg dan diastolik ≥ 90 mmHg. Menurut data World Health Organization (WHO) tahun 2023, sekitar 1,28 miliar orang dewasa berusia 30-79 tahun di dunia mengidap hipertensi, terutama di negara berkembang, dengan 46% penderita tidak menyadari kondisinya. Penelitian ini bertujuan memprediksi risiko hipertensi menggunakan algoritma KNN dan Decision Tree dengan optimasi feature selection menggunakan Genetic Algorithm. Dataset yang digunakan terdiri dari 12 fitur dan 4240 data. Hasil penelitian menunjukkan bahwa Decision Tree memberikan performa terbaik dengan akurasi 90.6%, precision 92.3%, recall 89.2%, dan F1-Score 90.8%. Optimasi fitur berhasil meningkatkan akurasi kedua algoritma, membuktikan efektivitas Genetic Algorithm dalam seleksi fitur untuk prediksi risiko hipertensi. Kata Kunci: Genetic Algorithm, Decision Tree, K-Nearest Neighbors, Hipertensi, Optimasi, Klasifikasi.

Item Type:	Thesis (S1)
Call Number CD:	FIK/SI. 25 015
NIM/NIDN Creators:	41821010014
Uncontrolled Keywords:	Genetic Algorithm, Decision Tree, K-Nearest Neighbors, Hipertensi, Optimasi, Klasifikasi.
Subjects:	100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 153 Conscious Mental Process and Intelligence/Intelegensia, Kecerdasan Proses Intelektual dan Mental > 153.8 Will, Volition/Kemauan > 153.83 Choice and Decision/Pilihan dan Keputusan 300 Social Science/Ilmu-ilmu Sosial > 300. Social Science/Ilmu-ilmu Sosial > 304 Factors Affecting Social Behaviour/Faktor-faktor yang Mempengaruhi Tingkah Laku Sosial > 304.5 Genetic Factors/Faktor Genetika Tingkah Laku Sosial 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma 600 Technology/Teknologi > 610 Medical, Medicine, and Health Sciences/Ilmu Kedokteran, Ilmu Pengobatan dan Ilmu Kesehatan > 616 Diseases/Penyakit
Divisions:	Fakultas Ilmu Komputer > Sistem Informasi
Depositing User:	khalimah
Date Deposited:	12 Feb 2025 04:32
Last Modified:	12 Feb 2025 04:32
URI:	http://repository.mercubuana.ac.id/id/eprint/94144

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