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ComEngApp : Computer Engineering and Applications Journal
Published by Universitas Sriwijaya
ISSN : 22524274     EISSN : 22525459     DOI : 10.18495
Core Subject : Science, Engineering,
Computer Science & IT Engineering
ComEngApp-Journal (Collaboration between University of Sriwijaya, Kirklareli University and IAES) is an international forum for scientists and engineers involved in all aspects of computer engineering and technology to publish high quality and refereed papers. This Journal is an open access journal that provides online publication (three times a year) of articles in all areas of the subject in computer engineering and application. ComEngApp-Journal wishes to provide good chances for academic and industry professionals to discuss recent progress in various areas of computer science and computer engineering.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 6 Documents
 1 
Search results for , issue "Vol 11 No 1 (2022)" : 6 Documents clear
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (509.684 KB) | DOI: 10.18495/comengapp.v11i1.393

Abstract

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.
Query Reformulation for Indonesian Question Answering System Using Word Embedding of Word2Vec Alvi Syahrini Utami; Novi Yusliani; Mastura Diana Marieska; Abdiansah Abdiansah
Computer Engineering and Applications Journal Vol 11 No 1 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (553.716 KB) | DOI: 10.18495/comengapp.v11i1.394

Abstract

Query reformulation is one of the tasks in Information Retrieval (IR), which automatically creates new queries based on previous queries. The main challenge of query reformulation is to create a new query whose meaning or context is similar to the old query. Query reformulation can improve the search for relevant documents for Open-domain Question Answering (OpenQA). The more queries are given to the search system, and the more documents will be generated. We propose a Word Predicted and Substituted (WPS) method for query reformulation using a word embedding word2vec. We tested this method on the Indonesian Question Answering System (IQAS). The test results obtained an E-1 value of 81% and an E-2 value of 274%. These results prove that the query reformulation method with WPS and word-embedding can improve the search for potential IQAS answers.
Identification of Stunting Disease using Anthropometry Data and Long Short-Term Memory (LSTM) Model Faris Mushlihul Amin; Dian Candra Rini Novitasari
Computer Engineering and Applications Journal Vol 11 No 1 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (379.576 KB) | DOI: 10.18495/comengapp.v11i1.395

Abstract

Children with unbalanced nutrition are currently crucial health issues and under the spotlight around the world. One of the terms for malnourished children is stunting. Stunting is a disease of malnutrition found in children aged under 5 years; as many as 70% of stunting sufferers are children aged 0-23 months. There are several ways to diagnose stunting, one of which is using stunting anthropometry. Stunting anthropometry can measure the physique of children so that some of the features that characterize the presence of stunting can be identified. Features resulted from the stunting anthropometry cover age, height, weight, gender, upper arm circumference, head size, chest circumference, and hip fat measurement. The process of identifying stunting can be simplified using an intelligent system called the Computer-Aided Diagnosis (CAD) system. CAD system contains 2 main processes, namely preprocessing and classification. Preprocessing includes normalization and augmentation of data using the SMOTE method. The classification process in this study uses the LSTM method. LSTM is a modification of the Recurrent Neural Network (RNN) method by adding a memory cell so that it can store memory data for a long time and in large quantities. The results of this study compare between the results of models that apply preprocessing and the one without preprocessing. The model that only uses LSTM has the best accuracy of 78.35%; the model with normalization produces an accuracy of 81.53%; the model that uses SMOTE produces an accuracy of 81.66%; and the model that uses normalization and SMOTE produces the best accuracy of 85.79%.
Deforestation Analysis in Taba Penanjung District with NDVI Vatresia, Arie; Utama, Ferzha Putra; Rais, Rendra Regen; Prasetyo, Bimo
Computer Engineering and Applications Journal Vol 11 No 1 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (640.698 KB) | DOI: 10.18495/comengapp.v11i1.396

Abstract

Forests are cleared to expand residential areas and plantations or change the allocation of forest land to non-forest (deforestation). This study aims to create a map of the condition of the forest area and find out the areas affected by deforestation and determine the rate of change in the forest area in the Taba Penanjung District forest area. First data on Landsat 8 images geometric correction performed to position image data so that it matches the actual coordinates and performing Radiometric Correction is used to correct if an error or distortion occurs due to imperfect operation and sensors. NDVI Method is the method used for comparing the greenness of vegetation in satellite imagery, which uses band 4 (Red) and band 5 (NIR), which is processed by ArcMap software. The results of this study produced a map of the condition of forest areas and the area of land affected by deforestation. Forest turn-over rates were described in the annual trend from 2013 to 2018. Furthermore, this research shows that deforestation in the Taba Penanjung district has happened in 58% of the total area of 23,747 ha. Although the deforestation has decreasing value in 2015 by 1.6%, it showed that there were increasing values in deforestation rate in 2014, 2016, 2017 by 1.4%, 1.9%, and 9.5% respectively from the total area of 23,747 ha.
Multiclass Segmentation of Pulmonary Diseases using Convolutional Neural Network Muhammad Arnaldo; Siti Nurmaini; Hadipurnawan Satria; Muhammad Naufal Rachmatullah
Computer Engineering and Applications Journal Vol 11 No 1 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (391.064 KB) | DOI: 10.18495/comengapp.v11i1.397

Abstract

Pulmonary disease has affected tens of millions of people in the world. This disease has also become the cause of death of millions of its sufferers every year. In addition, lung disease has also become the cause of other respiratory complications, which also causes the death of the sufferer. The diagnosis of pulmonary diseases through medical imaging is a significant challenge in computer vision and medical image processing. The difficulty is due to the wide variety in infected areas' shape, dimension, and location. Another challenge is to differentiate one lung disease from the other. Discriminating pulmonary diseases is a notable concern in the diagnosis of pulmonary disease. We have adopted the deep learning convolutional neural network in this study to address these challenges. Seven models were constructed using the Mask Region-based Convolutional Neural Network (Mask-RCNN) architecture to detect and segment infected areas within the lung region from CT scan imagery. The evaluation results show that the best model obtained scores of 91.98%, 85.25%, and 93.75% for DSC, MIoU, and mAP, respectively. The segmentation results are then visualized.
Identification of Indonesian Authors Using Deep Neural Networks Firdaus Firdaus; Irvan Fahreza; Siti Nurmaini; Annisa Darmawahyuni; Ade Iriani Sapitri; Muhammad Naufal Rachmatullah; Suci Dwi Lestari; Muhammad Fachrurrozi; Mira Afrina; Bayu Wijaya Putra
Computer Engineering and Applications Journal Vol 11 No 1 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (402.465 KB) | DOI: 10.18495/comengapp.v11i1.398

Abstract

Author Name Disambiguation (AND) is a problem that occurs when a set of publications contains ambiguous names of authors, i.e. the same author may appear with different names (synonyms) in other published papers, or author (authors) who may be different who may have the same name (homonym). In this final project, we will design a model with a Deep Neural Network (DNN) classifier. The dataset used in this final project uses primary data sourced from the Scopus website. This research focuses on integrating data from Indonesian authors. Parameters accuracy, sensitivity and precision are standard benchmarks to determine the performance of the method used to solve AND problems. The best DNN classification model achieves 99.9936% Accuracy, 93.1433% Sensitivity, 94.3733% Precision. Then for the highest performance measurement, the case of Non Synonym-Homonym (SH) has 99.9967% Accuracy, 96.7388% Sensitivity, and 97.5102% Precision.

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