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All Journal Data Science: Journal of Computing and Applied Informatics Indonesian Journal of Education, Social Sciences and Research (IJESSR)
Dani Gunawan
Universitas Sumatera Utara
Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Acute Coronary Syndrome Disorders Classification Based On ECG Images Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Sharfina Faza; Meryatul Husna; Janferson Panggabean; Romi Fadillah Rahmat; Dani Gunawan
Indonesian Journal of Education, Social Sciences and Research (IJESSR) Vol 2, No 1 (2021)
Publisher : Indonesian Journal of Education, Social Sciences and Research (IJESSR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/ijessr.v2i2.7148

Abstract

The heart is one of the most vital human organs that acts as a blood-pumping tool to supply oxygen and essential nutrients throughout the human body. Abnormalities in the heart greatly affect the work of the heart which results in the heart not being able to carry out its duties properly. Heart defect is one of the most common causes of death in many countries, including Indonesia. Electrocardiogram (ECG) is one of the most important examination models used to diagnose various abnormal heart rhythms. An ECG records the electrical activity of the heart by showing waveforms on a monitor or printing them on paper to classify cardiac abnormalities from the electrocardiogram image using image processing and artificial neural networks. The method used for the classification is the Adaptive Neuro-Fuzzy Inference System (ANFIS) and using the Chain code to take the value of the ECG feature. There were 92 ECG images to be used which were partitioned to 70 images for training data 22 images for test data with 3 types of abnormalities, namely coronary heart disease, angina, and myocardial infarction. The test was carried out using 4 choices of ANFIS functions. The parameters used to classify coronary heart disease, angina pectoris, and myocardial infarction reached 95.23% (DR), and 29.41% (DER), using the GBell function with the number of MFs (3) and epoch (100).
Improving Data Collection on Article Clustering by Using Distributed Focused Crawler Dani Gunawan; Amalia Amalia; Atras Najwan
Data Science: Journal of Computing and Applied Informatics Vol. 1 No. 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (435.726 KB) | DOI: 10.32734/jocai.v1.i1-82

Abstract

Collecting or harvesting data from the Internet is often done by using web crawler. General web crawler is developed to be more focus on certain topic. The type of this web crawler called focused crawler. To improve the datacollection performance, creating focused crawler is not enough as the focused crawler makes efficient usage of network bandwidth and storage capacity. This research proposes a distributed focused crawler in order to improve the web crawler performance which also efficient in network bandwidth and storage capacity. This distributed focused crawler implements crawling scheduling, site ordering to determine URL queue, and focused crawler by using Naïve Bayes. This research also tests the web crawling performance by conducting multithreaded, then observe the CPU and memory utilization. The conclusion is the web crawling performance will be decrease when too many threads are used. As the consequences, the CPU and memory utilization will be very high, meanwhile performance of the distributed focused crawler will be low.
Co-Authors Amalia Amalia Atras Najwan Janferson Panggabean Meryatul Husna Romi Fadillah Rahmat Sharfina Faza