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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 7 Documents
 1 
Search results for , issue "Vol 9 No 2 (2020)" : 7 Documents clear
LWT-CLAHE Based Color Image Enhancement Technique: An Improved Design Muyideen Omuya Momoh
Computer Engineering and Applications Journal Vol 9 No 2 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (504.89 KB) | DOI: 10.18495/comengapp.v9i2.329

Abstract

Color image enhancement is one of important process and actually a vital precursory stage to other stages in the field of digital image processing. This is due to the fact that the effectiveness of processes in this stage on the output determines the success of other stages for a quality overall performance. This paper presents a color image enhancement technique using lifting wavelet transform (LWT) and contrast limited adaptive histogram equalization (CLAHE) to overcome the issue of noise amplification, over and under-enhancement in exiting enhancement techniques. Test images from Computer Vision Database were used for the proposed technique and the performance was evaluated using PSNR and SSIM. Result obtained shows an average improvement of 56.4% and 20.98% in terms of PSNR and SSIM respectively.
Ultra-Wideband Spectrum Hole Identification Using Principal Components and Eigen Value Decomposition Joseph Emeshili
Computer Engineering and Applications Journal Vol 9 No 2 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1130.894 KB) | DOI: 10.18495/comengapp.v9i2.334

Abstract

Ultra-Wideband Spectrum Hole identification using Principal Components and Eigen Value Decomposition evolve a method of detecting spectrum hole from complex and corrupted wide band spectrum signal, due to the effect of noise spectrum hole detection is usually a challenge in wideband signal, as the presence of noise give rise to error alert, that is, noise can be misconstrued for signal. Dimensionality reduction was first used as the first level of denoising technique, Principal component Analysis (PCA) was used in dimensioning Wide Band Spectrum Data; this was able to reduce the noise level in the signal which made it convenient for Fast Fourier Transform (FFT) to act on it. FFT was used to decompose the signal to 64 sub band channels and on further reduction using principal Component Analysis (PCA), a 32 Level sub-band decomposition was carried out. Eigen Value generated shows that the magnitude of the signal to Noise ratio between Eigen Value 1 to 19 was high enough to show the that there exist a signal, while between 20 to 32 shows no signal by implication it indicates that these areas have high possibility of unoccupied spectrum holes.
Author Matching Classification with Anomaly Detection Approach for Bibliomethric Repository Data Zaqqi Yamani; Siti Nurmaini; Dian Palupi Rini
Computer Engineering and Applications Journal Vol 9 No 2 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (564.294 KB) | DOI: 10.18495/comengapp.v9i2.335

Abstract

Authors name disambiguation (AND) is a complex problem in the process of identifying an author in a digital library (DL). The AND data classification process is very much determined by the grouping process and data processing techniques before entering the classifier algorithm. In general, the data pre-processing technique used is pairwise and similarity to do author matching. In a large enough data set scale, the pairwise technique used in this study is to do a combination of each attribute in the AND dataset and by defining a binary class for each author matching combination, where the unequal author is given a value of 0 and the same author is given a value of 1. The technique produces very high imbalance data where class 0 becomes 98.9% of the amount of data compared to 1.1% of class 1. The results bring up an analysis in which class 1 can be considered and processed as data anomaly of the whole data. Therefore, anomaly detection is the method chosen in this study using the Isolation Forest algorithm as its classifier. The results obtained are very satisfying in terms of accuracy which can reach 99.5%.
A Stochastic Modelling Approach to Student Performance Prediction on an Internet-Mediated Environment Esther Khakata; Vincent Oteke Omwenga; Simon Samwel Msanjila
Computer Engineering and Applications Journal Vol 9 No 2 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (278.493 KB) | DOI: 10.18495/comengapp.v9i2.337

Abstract

Student performance prediction presents institutions and learners with results that assist them to gauge their academic abilities within their context of learning. Performance prediction has been done using different approaches over the years. In this case, stochastic modelling is used and it takes into consideration the use of random variables in the prediction process. The random variables are generated from different scenarios in order to generate a possible output. As a result, the generated output is used to indicate the likelihood of very rare occurrence scenarios which may or may not take place at a future date. With the vast availability of educational data that is available within the learning sector, this data forms the basis of input data that is required for the prediction of student performance within internet-worked environments. This paper develops the prediction model using Stochastic Differential Equations (SDEs). This then gives way to the analysis of data collected from varied respondents within universities leading to the generation of a student performance trajectory.
The Concept of Automatic Transport System Utilizing Weight Sensor Yurni Oktarina; Tresna Dewi; Pola Risma
Computer Engineering and Applications Journal Vol 9 No 2 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (579.424 KB) | DOI: 10.18495/comengapp.v9i2.339

Abstract

The current pandemic situation insists that people find a way to create a physical distance, limiting the number of people in a closed room. The human need for commuting has led to the idea of an automatic transport system that can transport people and goods without the assistance of a driver. This idea can lead to a new "normal" and reduced cost of manufacturing in the industry. The paper discussed the concept of an automatic transport system using a weight sensor. An automatic vehicle is designed to transport loads of different packages and be allocated automatically based on the weight of the package. The system is designed to be as simple as possible to increase the scope for implementation.
Real Time Garbage Bin Capacity Monitoring Nyayu Husni Latifah; Sitangsu Sitangsu; Sabilal Rasyad; Ade Silvia Handayani
Computer Engineering and Applications Journal Vol 9 No 2 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (246.845 KB) | DOI: 10.18495/comengapp.v9i2.340

Abstract

This paper discusses about a garbage bin that can be monitored in real time. The information of the garbage capacity can be obtained in the application that is integrated in the mobile phone. The communication between the garbage bin and the mobile phone is intended to help the garbage collector and the user to monitor the capacity of the garbage in a garbage bin. When it has been overloaded, the collector can manage the garbage by moving the garbage to the other bigger garbage bin. (landfill). This garbage bin has been tested and it could run well. It could open and close its cover as soon as it detected or did not detect the objects. It could also send the information of the garbage capacity to the mobile phone immediately with delay only 0.45-0.47 s.
Automated ECG Waveform Annotation Based on Stacked Long Short-Term Memory Annisa Darmawahyuni; Siti Nurmaini; Muhammad Naufal Rachmatullah
Computer Engineering and Applications Journal Vol 9 No 2 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (425.63 KB) | DOI: 10.18495/comengapp.v9i2.341

Abstract

The classification of electrocardiogram (ECG) waveform segmentation techniques can be difficult due to physiological variation of heart rate and different characteristics of the different ECG waves in terms of shape, frequency, amplitude, and duration. The P-wave, PR-segment, QRS-complex, ST-segment, and T-wave are extracted as the feature for classification algorithm to diagnose specified cardiac disorders. This requires the implementation of algorithms that identify specific points within the ECG wave. Some previous computational algorithms for automatic classification of ECG segmentation are proposed to overcome limitations of manual inspection of the ECG. This study presents new insight into the ECG semantic segmentation problem is surmounted by a deep learning approach for automatic ECG wave-form. Long short-term memory (LSTM) is proposed for this task. This experimental study has been performed for six different waveforms of ECG signal that represents cardiac disorders obtained from the Physionet: QT database. Overall, LSTM performance achieved accuracy, sensitivity, specificity, precision, F1-score, is 93.36%, 86.85%, 95.78%, 81.79%, and 83.09%, respectively.

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