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ComEngApp : Computer Engineering and Applications Journal

comengapp
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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 318 Documents

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Deep Convolutional Neural Networks-Based Plants Diseases Detection Using Hybrid Features Budiarianto Suryo Kusumo; Ana Heryana; Dikdik Krisnandi; Sandra Yuwana; Vicky Zilvan; Hilman F Pardede
Computer Engineering and Applications Journal Vol 9 No 3 (2020)
Publisher : Universitas Sriwijaya

With advances in information technology, various ways have been developed to detect diseases in plants, one of which is by using Machine Learning. In machine learning, the choice of features affect the performance significantly. However, most features have limitations for plant diseases detection. For that reason, we propose the use of hybrid features for plant diseases detection in this paper. We append local descriptor and texture features, i.e. linear binary pattern (LBP) to color features. The hybrid features are then used as inputs for deep convolutional neural networks (DCNN) Support and VGG16 classifiers. Our evaluation on Based on our experiments, our proposed features achieved better performances than those of using color features only. Our results also suggest fast convergence of the proposed features as the good performance is achieved at low number of epoch.

Utilization of Support Vector Machine and Speeded up Robust Features Extraction in Classifying Fruit Imagery Muhathir Muhathir; Wahyu Hidayah; Dian Ifantiska
Computer Engineering and Applications Journal Vol 9 No 3 (2020)
Publisher : Universitas Sriwijaya

Indonesia's various types of fruits can be met by the community. Many fruits that contain a source of vitamins are very beneficial to the body, or as an economic source for farmers. It's no wonder that many experts submit discoveries to increase the amount of productivity or just want to experiment with intelligent systems. Intelligent systems are specially designed machines in certain areas to adjust the capabilities made by the creators. This article provides the latest texture classification technique called Speeded up Robust Features (SURF) with the SVM (Support Vector Machine) method. In this concept, the representation of the image data is done by capturing features in the form of keys. SURF uses the determinant of the Hessian matrix to reach the point of interest in which descriptions and classifications are performed. This method delivers superior performance compared to existing methods in terms of processing time, accuracy, and durability. The results showed that the fruit classification by using the extraction of Speeded up Robust Features (SURF) feature and SVM (Support Vector Machine) Classification method is quite maximal and accurate. Result of 3 kinds of classification with SVM kernel function, SVM Gaussian with 72% accuracy, Polynomial SVM with 69.75% accuracy, and Linear SVM with 70.25% accuracy.

Reducing Generalization Error Using Autoencoders for The Detection of Computer Worms Nelson Ochieng Odunga; Ronald Waweru Mwangi; Ismail Ateya Lukandu
Computer Engineering and Applications Journal Vol 9 No 3 (2020)
Publisher : Universitas Sriwijaya

This paper discusses computer worm detection using machine learning. More specifically, the generalization capability of autoencoders is investigated and improved using regularization and deep autoencoders. Models are constructed first without autoencoders and thereafter with autoencoders. The models with autoencoders are further improved using regularization and deep autoencoders. Results show an improved in the capability of models to generalize well to new examples.

Air Quality Classification Using Support Vector Machine Ade Silvia Handayani; Sopian Soim; Theresia Enim Agusdi; Nyayu Latifah Husni
Computer Engineering and Applications Journal Vol 10 No 1 (2021)
Publisher : Universitas Sriwijaya

Air pollution in Indonesia, especially in urban areas, becomes a serious problem that needs attention. The air pollution will impact on the environment and health. In this research, the air quality will be classified using Support Vector Machine method that obtained from the sensor readings. The sensors used in the detection of CO, CO2, HC, dust/PM10 and temperature, namely TGS-2442, TGS-2611, MG-811, GP2Y1010AU0F and DHT-11. After testing, the results obtained with classification accuracy of 95.02%. The conclusion of this research indicates that the classification using the Support Vector Machine has the ability to classify air quality data.

Evaluation of Deep Convolutional Neural Network with Residual Learning for Remote Sensing Image Super Resolution Rika Sustika
Computer Engineering and Applications Journal Vol 10 No 1 (2021)
Publisher : Universitas Sriwijaya

Remote sensing images generally have low spatial resolution because of the limitations of sensing devices, bandwidth transmission, or storage capacity. An effective way to improve spatial resolution with low cost is by using algorithm based approach, known as super resolution (SR). In recent years, deep learning is super resolution technique that received special attention because it gave better performance than traditional method. In this research, we evaluated two simple deep learning architectures and explored parameters setting of deep convolutional neural network with residual learning, to achieve the trade-off between performance and speed or computational complexity, for implementation on remote sensing image super resolution. Results from the experiment show that deeper network with smaller number of filter gives faster model than shallow network with bigger number of filter, without sacrificing the performance.

The Artificial Intelligence Readiness for Pandemic Outbreak COVID-19: Case of Limitations and Challenges in Indonesia Siti Nurmaini
Computer Engineering and Applications Journal Vol 10 No 1 (2021)
Publisher : Universitas Sriwijaya

Artificial intelligence (AI) technologies continue to play significant roles during the Coronavirus 2019 (COVID-19) pandemic in the world. However, health is an area where the rules are stringent and inflexible. This can be justified because it deals with human life. Nevertheless, at the same time, a large number of tests, certifications, and panels will lead to innovations in AI for healthcare that are longer, more complex, and difficult to incorporate into real-world applications. Indonesia has a lot of AI research, which is challenging to commercialize in medicine. These researches are not yet effective due to several limitations in terms of (i) the readiness of a skilled workforce to develop and use AI, (ii) the readiness of regulations that regulate the ethics of using and utilizing responsibly, (iii) the readiness of computational infrastructure and supporting data for AI modeling, and (iv) readiness industry and the public sector in adopting AI innovations. In pandemic outbreak COVID-19, AI technology should help the medical industry more significantly, caused by such limitations, and it has not yet been impactful against COVID-19 in Indonesia. In the future, AI technology exists as a complementary facility to increase the productivity of medical personnel and acts as a disease prevention facility.

A Computer Security System for Cloud Computing Based on Encryption Technique Aliu Daniel; Muyideen Omuya Momoh
Computer Engineering and Applications Journal Vol 10 No 1 (2021)
Publisher : Universitas Sriwijaya

In recent years, progressively data proprietors have embraced cloud storage service, by which they will subcontract their data to the cloud server to significantly reduce the local storage overhead, due to the rapid growth in the cloud computing market and development. Cloud computing is the delivery of hosting services that are provided to clients over the web. It is quite common, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that may be rapidly provisioned and released with minimal management effort or service provider interaction. Sensitive information on the cloud is developing unexpectedly and bringing up several challenges and massive security concerns of the modern-day world. The cloud data and services reside in massively scalable data centers and may be accessed ubiquitously. Some issues concern in accessing this data is the security and confidentiality of consumer data in phrases of its location, relocation, availability, and security. Numerous users are surfing the Cloud for various purposes, therefore, they have highly safe and protracted services. The long run of the cloud, especially in expanding the range of applications, involves away the deeper degree of privacy, and authentication. Because of the safety concern associated with cloud computing, this paper presents a Computer Security System for Cloud Computing by employing a simple data protection model where data is encrypted using Advanced Encryption Standard (AES) technique before it is launched to the cloud, thus ensuring data confidentiality and security which is implemented with packet tracer.

Cloud-based ECG Interpretation of Atrial Fibrillation Condition with Deep Learning Technique Bambang Tutuko; Rossi Passarella; Firdaus Firdaus; Muhammad Naufal Rachmatullah; Annisa Darmawahyuni; Ade Iriani Sapitri; Siti Nurmaini
Computer Engineering and Applications Journal Vol 10 No 1 (2021)
Publisher : Universitas Sriwijaya

The prevalent type of arrhythmia associated with an increased risk of stroke and mortality is atrial fibrillation (AF). It is a known priority to identify AF before the first complication occurs. No previous studies have explored the feasibility of conducting AF screening using a deep learning (DL) algorithm (integrated cloud-computing) telehealth surveillance system. Hence, we address this problem. The goal of this research was to determine the feasibility of AF screening using an embedded cloud-computing algorithm in nonmetropolitan areas using a telehealth surveillance system. By using a single-lead electrocardiogram (ECG) recorder, we performed a prospective AF screening study. Both ECG measurements were evaluated and interpreted by the cloud-computing algorithm and a cardiologist on the telehealth monitoring system. The proposed cloud-computing based on Convolutional Neural Network (CNN) algorithm for AF detection had an accuracy of 99% sensitivity of 98%, and specificity of 99%. The overall satisfaction performance for the process of AF screening, and it is feasible to conduct AF screening by using a telehealth monitoring system containing an embedded cloud-computing algorithm.

Detection of Atrial Fibrillation Based on Long Short-Term Memory Ghina Auliya; Jannes Effendi
Computer Engineering and Applications Journal Vol 10 No 1 (2021)
Publisher : Universitas Sriwijaya

Atrial fibrillation is a quivering or irregular heartbeat (arrhythmia) that can lead to blood clots, stroke, heart failure, and even sudden cardiac death. This study used several public datasets of electrocardiogram (ECG) signals, including MIT-BIH Atrial Fibrillation, China Physiological Signal Challenge 2018, MIT-BIH Normal Sinus Rhythm based on QT-Database, and Fantasia Database. All datasets were divided into 3 cases with the experiment windows size 10, 5, and 2 seconds for two classes, namely Normal and Atrial Fibrillation. The recurrent neural networks method is appropriate for processing sequential data such as ECG signals, and k-fold Cross-Validation can help evaluate models effectively to achieve high performance. Overall, LSTM performance achieved accuracy, sensitivity, specificity, precision, F1-score, is 94.56% 94.67%, 94.67%, 94.43%, and 94.51%.

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

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.

Page 14 of 32 | Total Record : 318

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Siti Nurmaini

Contact Email
comengappjournal@unsri.ac.id

Phone
+6285268048092

Journal Mail Official
comengappjournal@unsri.ac.id

Editorial Address
Jurusan Sistem Komputer, Fakultas Ilmu Komputer, Universtas Sriwijaya, KampusUnsri Bukit Besar, Palembang

Location
Kab. ogan ilir,

Sumatera selatan

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Siti Nurmaini

Contact Email comengappjournal@unsri.ac.id

Phone +6285268048092

Journal Mail Official comengappjournal@unsri.ac.id

Editorial Address Jurusan Sistem Komputer, Fakultas Ilmu Komputer, Universtas Sriwijaya, KampusUnsri Bukit Besar, Palembang

Location Kab. ogan ilir, Sumatera selatan INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Siti Nurmaini

Contact Email
comengappjournal@unsri.ac.id

Phone
+6285268048092

Journal Mail Official
comengappjournal@unsri.ac.id

Editorial Address
Jurusan Sistem Komputer, Fakultas Ilmu Komputer, Universtas Sriwijaya, KampusUnsri Bukit Besar, Palembang

Location
Kab. ogan ilir,

Sumatera selatan

INDONESIA