Penggunaan Jaringan Syaraf Tiruan dan Wavelet Pada Citra EKG 12 Lead
DOI:
https://doi.org/10.30812/matrik.v20i2.1075Keywords:
elektrokardiogram, sym4, backpropagation, k-fold cross validation, Syaraf TiruanAbstract
Jantung sangat penting dalam sistem organ tubuh manusia. Apabila terjadi kesalahan pada fungsi jantung akibatnya sangat fatal. Oleh karenanya sangatlah penting menjaga kondisi jantung agar tetap sehat. Penelitian ini mencoba menawarkan untuk meneliti terkait kelainan jantung dengan menggunakan citra Electrocardigram (EKG) 12 lead. Data EKG yang digunakan berupa citra. Tujuan penelitian ini untuk memperoleh model yang tepat dalam mengidentifikasi kelainan jantung dengan menggunakan wavelet. Tahapan penelitian terdiri dari pre-processing, ekstraksi ciri dan klasifikasi. Tahap pre-processing menggunakan metode segmentasi (merubah data citra dari grayscale ke biner), morfologi (metode dilasi dan metode erosi) dan transformasi ke sinyal. Tahap ektraksi ciri menggunakan metode dekomposisi transformasi wavelet dengan tingkatan tiga level, dimana mother wavelet yang digunakan berupa symlet orde 4 (Sym4). Tahap klasifikasi menggunakan jaringan syaraf tiruan dengan metode backpropagation. Adapun metode validasi dan evaluasi menggunakan k-fold cross validation dan confusion matrix. Penggunaan metode k-fold cross validation, dimana k=5 dengan pembagian data training 80% dan testing 20%. Hasil yang diperoleh dari keseluruhan sistem dimana tingkat akurasi sebesar 92,94%, sensitifitas sebesar 90% dan spesifisitas sebesar 94,55%.
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References
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[2] J.-G. Yang, J.-K. Kim, U.-G. Kang, and Y. Lee, “Coronary heart disease optimization system on Adaptive-Network-based Fuzzy Inference System and Linear Discriminant Analysis (ANFIS–LDA),†Journal Personal and Ubiquitous Computing, vol. 18, no. 6, pp. 1351–1362, 2014.
[3] S. Alomari, M. Shujauddin, and V. Emamian, “EKG Signals – De-noising and Features Extraction,†Journal of Biomedical Engineering, vol. 6, no. 6, pp. 180–201, 2016.
[4] A. Surtono, T. S. Widodo, and M. Tjokronagoro, “Analisis Klasifikasi Sinyal EKG Berbasis Wavelet dan Jaringan Syaraf Tiruan,†Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), vol. 1, no. 3, pp. 60–66, 2012.
[5] R. Singh, R. Mehta, and N. Rajpal, “Efficient wavelet families for ECG classification using neural classifiers,†in Journal Procedia Computer Science, 2018, vol. 132, pp. 11–21.
[6] B. W. Putra, R. F. Isnanto, and P. Sari, “Deteksi Arritmia pada Sinyal EKG dengan Deep Neural Network,†in Seminar Nasional Sains dan Teknologi Terapan, 2020, vol. 3, no. 1, pp. 223–230.
[7] D. R. Oktaviani, W. Semarang, and I. Semarang, “Analisis Kelainan Jantung Menggunakan Dimensi Fraktal dan Transformasi Wavelet,†Jurnal Ilmiah Matematika dan Terapan, vol. 17, no. 2, pp. 230–237, 2020.
[8] S. H. El-Khafif and M. A. El-Brawany, “Artificial Neural Network-Based Automated ECG Signal Classifier,†Journal ISRN Biomedical Engineering, pp. 1–6, Jun. 2013.
[9] Darwan, S. Hartati, R. Wardoyo, and B. Y. Setianto, “The Feature Extraction to Determine the Wave ’ s Peaks in the Electrocardiogram Graphic Image,†International Journal of Image,graphics and signal processing, vol. 9, no. 6, pp. 1–13, 2017.
[10] J. Wang, P. Wang, and S. Wang, “Automated detection of atrial fibrillation in ECG signals based on wavelet packet transform and correlation function of random process,†Biomedical Signal Processing and Control, vol. 55, pp. 1–9, Jan. 2020.
[11] N. Prashar, M. Sood, and S. Jain, “Design and implementation of a robust noise removal system in ECG signals using dual-tree complex wavelet transform,†Journal Biomedical Signal Processing and Control, vol. 63, pp. 1–12, Jan. 2021.
[12] S. Sahoo, B. Kanungo, S. Behera, and S. Sabut, “Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities,†Journal Measurement, vol. 108, pp. 55–66, 2017.
[13] K.-L. Du and M. N. S. Swamy, Neural Networks and Statistical Learning. London, Inggris: Springer, 2014.
[14] E. Alpaydin, Introduction to Machine Learning, Third. The MIT Press, 2014.
[15] A. Ehret, D. Hochstuhl, D. Gianola, and G. Thaller, “Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle,†pp. 1–9, 2015.
[16] M. F. Almas and B. D. Setiawan, “Implementasi Metode Backpropagation untuk Prediksi Harga Batu Bara,†vol. 2, no. 12, pp. 6502–6511, 2018.
[17] S. P. Siregar and A. Wanto, “Analysis Accuracy of Artificial Neural Network Using Backpropagation Algorithm In Predicting Process ( Forecasting ),†vol. 1, no. 1, pp. 34–42, 2017.
[18] D. Huang and Z. Wu, “Forecasting outpatient visits using empirical mode decomposition coupled with back- propagation artificial neural networks optimized by particle swarm optimization,†pp. 1–17, 2017.
[19] A. Haris, H. Slamet, B. H. Purnomo, and D. W. Soedibyo, “Model Jaringan Syaraf Tiruan untuk Prakiraan Harga Komponen Bahan Baku Pakan Unggas di PT XYZ Model of Artificial Neural Network for Price Forecasting of Poultry Feed Components at PT XYZ,†vol. 9, no. 2, pp. 151–161, 2020.
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[21] Y. Aprizal, R. I. Zainal, U. B. Darma, J. S. Tiruan, and L. V. Quantization, “Perbandingan Metode Backpropagation and Learning Vector Quantization ( LVQ ) dalam Menggali Potensi Mahasiswa Baru di,†vol. 18, no. 2, pp. 294–301, 2019.
[22] J. Dong, Guozhu and Bailey, Contrast Data Mining : Concepts, Algorithms, and Applications. CRC Press, 2013.
[23] J. Han, Jiawei; Kamber, Micheline; Pei, Data Mining Concepts and Techniques, Third. Morgan Kaufmann Publishers, 2012.
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Published
2021-05-30
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Articles
How to Cite
[1]
D. Darwan, “Penggunaan Jaringan Syaraf Tiruan dan Wavelet Pada Citra EKG 12 Lead”, MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 20, no. 2, pp. 355–366, May 2021, doi: 10.30812/matrik.v20i2.1075.
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