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All Journal ComEngApp : Computer Engineering and Applications Journal JSI: Jurnal Sistem Informasi (E-Journal) Annual Research Seminar Jurnal Generic ABDIKOM

Sutarno Sutarno

Universitas Sriwijaya

Author-ID : 2892415

Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science Other

Published : 13 Documents Claim Missing Document

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Articles

Title

Implementation Color Filtering and Harris Corner Method on Pattern Recognition System Ahmad Zarkasi; Sutarno Sutarno; Huda Ubaya; Muhammad Fajar
Computer Engineering and Applications Journal Vol 6 No 3 (2017)
Publisher : Universitas Sriwijaya

Color recognition and angle detection of underwater objects can be done with the help of underwater robots (ROV) with image processing applications. The processing of the object's image is recognizing various shapes and colors of objects in the water. In this research, the color filtering and Harris corner method will be designed, studied, tested and implemented. The color filtering method is used to recognize object color patterns, while the Harris Corner method is used to detect angles of underwater objects. Then classify images to get data on environmental pattern recognition. The color patterns tested include red, green, yellow and blue. the results obtained are all color patterns can be recognized well. while the shape of the object being tested includes cubes, triangles, rectangles, pentagons, and hexagons. the results of testing some of the shapes can be detected with a good angle and others still have errors. This is because testing the form of objects is done in various positions, such as from the front, right, left, up and below.Â

Inter Patient Atrial Fibrillation Classification Using One Dimensional Convolution Neural Network Ahmad Rifai; Muhammad Naufal Rachmamtullah; Sutarno Sutarno; Bambang Tutuko
Computer Engineering and Applications Journal Vol 11 No 1 (2022)
Publisher : Universitas Sriwijaya

Atrial fibrillation is the most common type of arrhythmia. The process of detecting AF disease is quite difficult. This is because it is necessary to detect the presence or absence of a P signal wave in the ECG signal. However, this method requires special expertise from a cardiologist. Several literatures have proposed an automatic ECG classification system. However, the intra-patient paradigm does not simulate real-world scenarios. One of the challenges in the inter-patient paradigm is the morphological differences between one subject and another. In order to overcome the problems that arise in the automatic classification of ECG signal patterns a deep learning approach was proposed. This study proposes the classification process of atrial fibrillation in the inter-patient paradigm using a one-dimensional convolutional neural network architecture. The test is divided into two cases: two labels (Normal and AF) and three labels (Normal, AF and Non-AF). In the case of two test labels with an inter-patient scheme, the performance was 100% for all test metrics (accuracy, sensitivity, precision, and F1-Score). However, in the three-label case, the model's performance decreased to 85.95, 70.02, 72.50, 71.19 for accuracy, sensitivity, precision and F1-Score, respectively.

Co-Authors Abdurahman Ahmad Fali Oklilas Ahmad Heryanto Ahmad Rifai Ahmad Zarkasi Ayu Nurul Asti Bambang Tutuko Dedy Kurniawan Denny Wahyudi Marzuk Deris Stiawan Eka Fasilah Erwin, Erwin Hadi Purnawan Satria Huda Ubaya Kemahyanto Exaudi M. Rudi Sanjaya Muhammad Fajar Muhammad Naufal Rachmamtullah Muhammad Nawar Athalaza Pia Ardhya Garini Primanita, Anggina Purwita Sari Rahmad Fadli Isnanto Reza Firsandaya Malik Rossi Passarela Rossi Passarella Rouzan Fiqri Abdullah Sarmayanta Sembiring Syamsul Arifin

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