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INDONESIA
Indonesian Journal on Computing (Indo-JC)
Published by Universitas Telkom
ISSN : 24609056     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Indonesian Journal on Computing (Indo-JC) is an open access scientific journal intended to bring together researchers and practitioners dealing with the general field of computing. Indo-JC is published by School of Computing, Telkom University (Indonesia).
Arjuna Subject : -
Articles 12 Documents
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Search results for , issue "Vol. 5 No. 3 (2020): December, 2020" : 12 Documents clear
Hybrid Array List: An Efficient Dynamic Array with Linked List Structure Abu Sara, Mutaz Rasmi; Klaib, Mohammad F. J.; Hasan, Masud
Indonesian Journal on Computing (Indo-JC) Vol. 5 No. 3 (2020): December, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.3.527

Abstract

In this paper, we present an efficient dynamic array, called hybrid array list (HAL), whose structure is a linked list and each node is an array. In a HAL H, each node, called a chunk, is an array of size at most 2c, where c is an initial array size determined by the user. As the elements are added or deleted in H, it grows or shrinks by the number of nodes in the linked list as well as by the sizes of the chunks. We consider the operations append, insert and delete as well as a helping operation actual position in H. These operations run in O(1), O(m+c), O(m+c) and O(m) time, respectively, in worst case, where m is the number of chunks in H. These running times are much less than the worst case running time, which is O(n), where n is the total number of elements in H, taken by these operations in linked list or array. We implement HAL and compare these operations with similar operations in array list of Java and vector of C++. Our results show that H can perform substantially better when c is about half of the total number of elements.
Study of Machine Learning Algorithm on Phonocardiogram Signals for Detecting of Coronary Artery Disease Mandala, Satria; Pramudyo, Miftah; Rizal, Ardian; Fikry, Maurice
Indonesian Journal on Computing (Indo-JC) Vol. 5 No. 3 (2020): December, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.3.536

Abstract

Several methods of detecting coronary artery disease (CAD) have been developed, but they are expensive and generally use an invasive catheterization method. This research provides a solution to this problem by developing an inexpensive and non-invasive digital stethoscope for detecting CAD. To prove the effectiveness of this device, twenty-one subjects consisting of 11 CAD patients and 10 healthy people from Hasan Sadikin Hospital Bandung were selected as validation test participants. In addition, auscultation was carried out at four different locations around their chests, such as the aorta, pulmonary, tricuspid, and mitral. Then the phonocardiogram data taken from the stethoscope were analyzed using machine learning. To obtain optimal detection accuracy, several types of kernels such as radial basis function kernel (RBF), polynomial kernel and linear kernel of Support Vector Machine (SVM) have been analyzed. The experimental results show that the linear kernel outperforms compared to others; it provides a detection accuracy around 66%. Followed by RBF is 56% and Polynomial is 46%. In addition, the observation of phonocardiogram signals around the aorta is highly correlated with CAD, giving an average detection accuracy for the kernel of 66%; followed by 44% tricuspid and 43% pulmonary.

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