Rancang Program Pendeteksi Abnormalitas Detak Jantung Beserta Resiko Penyakit Jantung Menggunakan Metode Deep Learning

Prana, Satria Surya (2026) Rancang Program Pendeteksi Abnormalitas Detak Jantung Beserta Resiko Penyakit Jantung Menggunakan Metode Deep Learning. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Penyakit jantung merupakan salah satu penyebab utama tingginya angka kematian, sehingga diperlukan sistem berbasis teknologi informasi yang mampu mendeteksi abnormalitas detak jantung secara dini dan akurat. Seiring dengan perkembangan kecerdasan buatan, metode deep learning menjadi solusi yang efektif dalam pengolahan dan analisis data sinyal detak jantung. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem pendeteksi abnormalitas detak jantung serta prediksi risiko penyakit jantung menggunakan metode deep learning dari sudut pandang keilmuan Informatika. Sistem yang dikembangkan memanfaatkan data sinyal detak jantung sebagai input, yang diproses melalui tahapan praproses data meliputi normalisasi, segmentasi, dan reduksi noise. Selanjutnya, data tersebut digunakan untuk melatih model deep learning dalam melakukan klasifikasi detak jantung normal dan abnormal, serta memprediksi tingkat risiko penyakit jantung berdasarkan pola yang teridentifikasi. Perancangan sistem mencakup arsitektur model, proses pelatihan, serta integrasi model ke dalam aplikasi berbasis komputer sebagai sistem pendukung keputusan. Evaluasi kinerja sistem dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score untuk mengukur performa model secara komputasional. Hasil penelitian menunjukkan bahwa model deep learning mampu mengklasifikasikan abnormalitas detak jantung dengan baik serta memberikan prediksi risiko penyakit jantung secara konsisten. Dengan demikian, sistem yang dirancang diharapkan dapat menjadi solusi berbasis Informatika dalam pengembangan aplikasi pendukung diagnosis dini penyakit jantung.
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Heart disease is one of the leading causes of death worldwide, highlighting the need for technology-based systems capable of performing early and accurate detection of heart rate abnormalities. Along with the rapid development of artificial intelligence, deep learning methods have proven to be effective in processing and analyzing heart rate signal data. This research aims to design and implement a system for detecting heart rate abnormalities and predicting the risk of heart disease using deep learning from an Informatics perspective. The proposed system utilizes heart rate signal data as input, which undergoes several preprocessing stages including normalization, segmentation, and noise reduction. The processed data are then used to train a deep learning model to classify normal and abnormal heartbeats and to predict the level of heart disease risk based on identified patterns. The system design covers model architecture development, training processes, and integration of the trained model into a computer-based application as a decision support system. System performance is evaluated using accuracy, precision, recall, and F1-score to measure the computational effectiveness of the proposed model. The experimental results indicate that the deep learning model is capable of accurately classifying heart rate abnormalities and consistently predicting heart disease risk. Therefore, the developed system is expected to serve as an Informatics-based solution that supports the development of intelligent applications for early heart disease detection.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	detak jantung, abnormalitas, penyakit jantung, deep learning, sistem cerdas, heart rate, abnormality detection, heart disease, deep learning, intelligent system
Subjects:	T Technology > T Technology (General) T Technology > T Technology (General) > T57.5 Data Processing
Divisions:	Faculty of Industrial Technology > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User:	Satria Surya Prana
Date Deposited:	30 Jan 2026 09:07
Last Modified:	30 Jan 2026 09:07
URI:	http://repository.its.ac.id/id/eprint/131223

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