RANCANG BANGUN SISTEM DIAGNOSIS AWAL KLASIFIKASI TEKANAN DARAH MENGGUNAKAN ALGORITMA DECISION TREE

Widias Tuti, - (2024) RANCANG BANGUN SISTEM DIAGNOSIS AWAL KLASIFIKASI TEKANAN DARAH MENGGUNAKAN ALGORITMA DECISION TREE. S1 thesis, Universitas Pendidikan Indonesia.

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Official URL: https://repository.upi.edu/

Abstract

Seiring zaman yang semakin modern ini, dan meningkatnya pola hidup yang tidak sehat, semakin banyak orang yang terkena hipertensi. Badan Kesehatan Dunia (WHO) menyatakan bahwa empat faktor risiko utama yang bertanggung jawab atas peningkatan penyakit hipertensi adalah pola makan yang tidak sehat, kurangnya aktivitas fisik, merokok, dan konsumsi alkohol yang berbahaya. Berdasarkan data Kementrian Kesehatan pada tahun 2019, 63.309.620 penduduk Indonesia mengalami hipertensi dan angka kematian di Indonesia akibat hipertensi mencapai 427.218 kematian. Penelitian mempunyai tujuan untuk merancang dan membangun sistem klasifikasi tingkatan tekanan darah menggunakan algoritma Decision Tree dan terhubung dengan Thingspeak. Penelitian ini menggunakan metode ADDIE, yaitu Analisis, Design, Development, Implementasi, dan Evaluasi. Dan pada penelitian ini menggunakan sensor MPX5700AP dan Sensor Pulse sebagai fitur tambahan. Pada pengujian pemodelan Decision Tree, nilai akurasi mencapai 100% dengan jumlah data uji sebanyak 66 sampel. Pada rancangan sistem yang telah dibuat dan dilakukan pengujian sebanyak 30 pengambilan tekanan darah, hasil yang Pada pengujian perbandingan error antara alat pembanding, Sinocare BSX516, dan Omron HEM-7120, terdapat hasil nilai error alat rancangan dengan Omron HEM-7120 lebih kecil dibandingkan dengan alat rancangan dengan Sinocare BSX516. Berdasarkan BS ISO standar memnujukan hasil akurasi yang cukup baik karena memiliki akurasi perbandingan ± 5 mmHg. Untuk klasifikasi tingkatan tekanan darah mendapatkan akurasi sebesar 73% ----- As times become more modern and unhealthy lifestyles increase, more people are experiencing hypertension. The World Health Organization (WHO) states that four main risk factors responsible for the rise in hypertension are unhealthy diets, lack of physical activity, smoking, and harmful alcohol consumption. According to data from the Ministry of Health in 2019, 63,309,620 Indonesians suffered from hypertension, and the death toll in Indonesia due to hypertension reached 427,218. This research aims to design and build a blood pressure classification system using the Decision Tree algorithm and connect it with Thingspeak. The study uses the ADDIE method, which includes Analysis, Design, Development, Implementation, and Evaluation. Additionally, it employs the MPX5700AP sensor and a Pulse Sensor as supplementary features. In testing the Decision Tree model, the accuracy value reached 100% with a test sample size of 66. For the system design that was created and tested over 30 blood pressure measurements. In the error comparison test between the comparison tool, Sinocare BSX516, and Omron HEM-7120, there were results that the error value of the design tool with the Omron HEM-7120 was smaller than that of the design tool with the Sinocare BSX516. Based on BS ISO standards, these results show quite good accuracy, with a comparison accuracy of ± 5 mmHg. For blood pressure classification, an accuracy of 73% was achieved

Item Type: Thesis (S1)
Additional Information: https://scholar.google.com/citations?hl=en&user=RsgWV6MAAAAJ ID Sinta Dosen Pembimbing Ichwan Nul Ichsan: 6721201 Galura Muhammad Suranegara: 6703764
Uncontrolled Keywords: klasifikasi, Tekanan darah, Decision Tree, MXP5700AP, Akurasi, Thingspeak classification, blood pressure, Decision Tree, MXP5700AP, accuracy, Thingspeak
Subjects: T Technology > T Technology (General)
Divisions: UPI Kampus Purwakarta > S1 Sistem Telekomunikasi
Depositing User: Widias Tuti
Date Deposited: 03 Sep 2024 05:05
Last Modified: 03 Sep 2024 05:05
URI: http://repository.upi.edu/id/eprint/122220

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