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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Simantec POSITIF Sistemasi: Jurnal Sistem Informasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Teknologi dan Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Vokasi Journal of Artificial Intelligence and Engineering Applications (JAIEA) Proceeding Applied Business and Engineering Conference Journal Of Artificial Intelligence And Software Engineering Jurnal INFOTEL Jurnal Sistem Informasi dan Manajemen eProceeding of TIK Jurnal Teknologi Rekayasa Informasi dan Komputer
Arhami, Muhammad
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Journal : ComEngApp : Computer Engineering and Applications Journal

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PCA-Based on Feature Extraction and Compressed Sensing for Dimensionality Reduction Desiani, Anita; Maiyanti, Sri Indra; Miraswan, Kandak Januar; Arhami, muhammad
Computer Engineering and Applications Journal Vol 8 No 2 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (334.541 KB) | DOI: 10.18495/comengapp.v8i2.281

Abstract

Compressive sensing reduces the number of samples required to achieve acceptable reconstruction for medical diagnostics, therefore this research will implement dimensional reduction algorithms through compressed sensing for electrocardiogram signals (EKG). dimensional reduction is performed based on the fact that ECG signals can be reconstructed with linear combination coefficients with a bumpy base of small measurements with high accuracy. This study will use PCA for feature extraction on ECG signals. The data used are the ECG patient records on the website page www.physionet.org as many as 1200 with each attribute as many as 256 attributes. The total data dimension used is 1200x256, which means the data has 1200 rows and has as many as 256 columns. To show the accuracy of the dimensional reduction result, so it is performed classification on data using KNN and Naive Bayes. The classification results show that KKN can classify well with 84,02% accuracy rate and the Naive Bayes accuracy is 65,78%. for 100 dimensions The conclusion is those dimensional reductions for ECG data that have large dimensions, it still able to provide valid information like it uses the original data. Principle Component Analysis is a good method for reducing data dimensions by selecting certain features, so the dimensions of the data become smaller but still able to provide good accuracy to the reader.
Co-Authors ., Mursyidah Abdi, Musta’inul Adhimullah, Adhimullah Adriana Adriana Affandi, Azhar Kholiq Agustin, Riya Sri Al Fath, Muhammad Fajar Ali Amran Amirullah -, Amirullah Andhini, Shania Putri Anita Desiani Anwar Anwar Aulia, Annisa Rizka Azhar Azhar Davi, Muhammad Des Alwine Zayanti, Des Alwine Dian Cahyawati Fajriaty, Siti Fauzani, Lia Fitri, Intan Ginting, Rachel Ardana Putra Hendrawati Hendrawati Hendrawaty Hendrawaty, Hendrawaty Henisaniyya, Nabila Hidayat, Hari Toha Husaini Husaini Huzaeni, Huzaeni Indrawati Indrawati Insan, Jamalul Irmeilyana Isnani Isnani Kanda Januar Miraswan Khadafi, M. Khairunnas, Muhammad Fadil Kurniawati, Devy Mahdi Mahdi, Mahdi Mahmudah, Rifa’atun Masyitha, Masyitha Maulana, Muhammad Andra Meilisa, Dinda Meilvinasvita, Dwi Mesti, Mesti Mortara, Alda Amalia Muakhir, Muakhir Mufida, Nabila Muhammad Arifai Muhammad Davi Muhammad Nasir Muhammad Reza Zulman Muhammad Rizka, Muhammad Mulyadi Mulyadi Muzammil, Muzammil Nahar, Nahar Narti Narti, Narti Nasution, Siti Aisyah Novi Rustiana Dewi Nurakmalinda, Nurakmalinda Permatasari, Mitta Pertiwi, Citra Purnahar, Fadhil Rahmadhani, Syiva Rahmadita, Suristhia Raiyan, Muhammad Ramayanti, Indri Rifkie Primartha Rizqillah, Rizqillah Rudi F, Fachri Yanuar Safriadi Safriadi Safriani, Yuni Salahuddin Salahuddin Salahuddin Salahuddin Salnadila, Salnadila Sari, Suci Indah Sasongko, Muhammad Aditya Sitorus, Dina Suzzete Sri Indra Maiyanti Sugandi Yahdin Sugeng, Santoso Sukma, Melati Dian Yadi Utama Yassir Yassir Yuli Andriani Yuliana Yuliana Zahara, Fitria