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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 10 No 2 (2021)" : 6 Documents clear
Literature Review Recommendation System Using Hybrid Method (Collaborative Filtering & Content-Based Filtering) by Utilizing Social Media as Marketing Ni Wayan Priscila Yuni Praditya
Computer Engineering and Applications Journal Vol 10 No 2 (2021)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (158.591 KB) | DOI: 10.18495/comengapp.v10i2.368

Abstract

The recommendation system is an application model based on observations of the circumstances and customer desires. In the recommendation system, several methods are used to support how the system works in producing information. One method of recommendation system that is quite popular is the method Hybrid. Several researchers have successfully applied this method in developing a tourism recommendation system, therefore to achieve the goal of implementing a tourism recommendation, it is better to take advantage of a marketing technique such as promotion in order to increase sales and attract more comprehensive customers. Therefore, a literature review on the method hybrid (Collaborative Filtering & Content-Based Filtering) of this travel recommendation system is carried out to collaborate between methods, algorithms, and a tool or media marketing applied in a recommendation system.
Application of the Relief-f Algorithm for Feature Selection in the Prediction of the Relevance Education Background with the Graduate Employment of the Universitas Sriwijaya Sugandi Yahdin; Anita Desiani; Nuni Gofar; Kerenila Agustin
Computer Engineering and Applications Journal Vol 10 No 2 (2021)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (228.509 KB) | DOI: 10.18495/comengapp.v10i2.369

Abstract

Career Development Center (CDC) at Universitas Sriwijaya provided a tracer study dataset for graduates. The data contained feature questions about the relevance of background education and graduate employment, namely about lectures, research projects experience, internships experience, English skill, internet knowledge, computer skill and others. the data was filled in by graduates in 2014, 2015, and 2016. Applying the Relief-f algorithm was to select the pattern features that most influence the relevance of education background and graduate employment. This study used Naive Bayes and KNN methods to measure the success rate of the Relief-f algorithm. The results of the accuracy of the data before the feature selection process for the naïve Bayes method were 73.43% and the KNN method was 66.24%, after the feature selection process the accuracy obtained in both methods increased to 74.38% for the Naive Bayes method and 72.22% for the KNN method. The best pattern features selected were 8 features: department relationship with work, the competence of education background, English skill, research projects experience, extracurricular activities, the competence of education background, internships experience, and communication skills. Based on the accuracy obtained, it was concluded that the Relief-f algorithm worked well in the feature selection and improved the accuracy.
Traffic Violation Detection System Using Image Processing Buhari Ugbede Umar; Olayemi Mikail Olaniyi; James Agajo; Omeiza Rabiu Isah
Computer Engineering and Applications Journal Vol 10 No 2 (2021)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (714.819 KB) | DOI: 10.18495/comengapp.v10i2.371

Abstract

Over the last three decades, the global population of human beings has increased at an exponential rate, resulting in an equal rise in the number of vehicles owned and used globally. Vehicle traffic is a major economic component in both urban and rural areas, and it requires proper management and monitoring to ensure that this mass of vehicles coexists as smoothly as possible. The amount of vehicular traffic on roads around the world, with Nigeria as a case study, results in varying degrees of traffic rule violations, especially red light jumping. To arrest offenders and resolve the weaknesses and failures of human traffic operators who cannot be everywhere at once, efficient traffic violation and number plate recognition systems are needed. There are several methods for reading characters, which can be alphabets, numbers, or alphanumeric. To minimize processing time and computational load on the machine, this research proposed k-Nearest Neighbour for plate number character recognition. The system was developed and evaluated. From the result, the localization of license plate regions within an image was 92 percent accurate, and character recognition was 73 percent accurate.
Aorta Detection with Fetal Echocardiography Images Using Faster Regional Convolutional Neural Network (R-CNNs) Ade Iriani Sapitri; Annisa Darmawahyuni
Computer Engineering and Applications Journal Vol 10 No 2 (2021)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (682.97 KB) | DOI: 10.18495/comengapp.v10i2.375

Abstract

The fetal heart structure has an important role in analyzing the location of abnormalities in the heart. The aorta is one of the fetal heart structures, which has an essential part in exploring how the fetal heart is structured. To see the fetal heart structure can be seen with the help of an echocardiography tool in the form of ultrasound to see ultrasound images of the fetal heart. In ultrasound image data, detection is challenging because of its low image features, shadows, and contrast levels. So that is the first to do it yourself in one of the points of the culture in the culture in the aorta. The approach in this study uses deep learning in cases using Faster Regional Convolutional Neural Network (R-CNNs) with the R-CNNs mask method. The proposed approach has been applied to 151 ultrasound images of the fetal heart for the aortic region. The evaluation results were tested by evaluating metrics on the detection object with an mAP value of 83.71%.
Wireless Controlling for Garbage Robot (G-Bot) Nyayu Latifah Husni; Robi Robi; Ekawati Prihatini; Ade Silvia Handayani; Sabilal Rasyad; Firdaus Firdaus
Computer Engineering and Applications Journal Vol 10 No 2 (2021)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (719.313 KB) | DOI: 10.18495/comengapp.v10i2.376

Abstract

This paper presents one of the solutions in overcoming the garbage problems. The people sometimes feel too lazy to throw the garbage into proper place due to their habit that has been grown since little kids. In this research, A G-Bot, a robot that has function as the garbage container is offered. By using an Internet of Things (IoT) application, the users can control the motion of the G-Bot wirelessly, so that it can move to the users’ desired location. In addition, the covers of the G-Bot can also be opened using smart phones that connected to the G-Bot. A Blynk that acts as the IoT Application is used in order to set up the G-Bot communication. From the experimental result, it can be concluded that the proposed research has been successful to be implemented. The users can move the G-Bot to the targeted location wirelessly, and they can also open and close the G-Bot’s lids wirelessly trough the mobile phones.
Classification of Finger Spelling American Sign Language Using Convolutional Neural Network Anna Dwi Marjusalinah; Samsuryadi Samsuryadi; Muhammad Ali Buchari
Computer Engineering and Applications Journal Vol 10 No 2 (2021)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.454 KB) | DOI: 10.18495/comengapp.v10i2.377

Abstract

Sign language is a combination of complex hand movements, body postures, and facial expressions. However, only a limited number of people can understand and use it. A computer aid sign language recognition with finger spelling style utilizing a convolutional neural network (CNN) is proposed to reduce the burden. We compared two CNN architectures such as Resnet 50, and DenseNet 121 to classify the American sign language dataset. Several data splitting proportions were also tested. From the experimental result, it is shown that the Resnet 50 architecture with 80:20 data splitting for training and testing indicates the best performance with an accuracy of 0.999913, sensitivity 0.998966, precision 0.998958, specificity 0.999955, F1-score 0.999913, and error 0.0000898.

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