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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 318 Documents
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Implementation of Feature Selection for Optimizing Voice Detection Based on Gender using Random Forest Abdurahman; Vindriani, Marsella; Prasetyo, Aditya Putra Perdana; Sukemi; Buchari, M. Ali; Sembiring, Sarmayanta; Firnando, Ricy; Isnanto, Rahmat Fadli; Exaudi, Kemahyanto; Dudifa, Aldi; Riyuda, Rafki Sahasika
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

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Abstract

Gender-based voice detection is one of the machine learning applications that has various benefits in technology and services, such as virtual assistants, human-machine interaction systems, and voice data analysis. However, the use of too many features, including irrelevant features, can cause a decrease in accuracy and model performance. This research aims to optimize voice-based gender detection by applying a feature selection method to select significant features based on their correlation value to the target. Experimental results show that by using only the significant features selected through correlation analysis, the accuracy of the model is significantly improved compared to using all available features. This research confirms the importance of feature optimization to support the development of more efficient and accurate gender-based speech detection models.
An Analysis of Adaptive Approach for Document Binarization Malik, Reza Firsandaya; Saparudin; Septyliana, Intan
Computer Engineering and Applications Journal (ComEngApp) Vol. 2 No. 2 (2013)
Publisher : Universitas Sriwijaya

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Abstract

Abstract Binarization is an initial step in document image analysis for differentiate text area from background. Determination of binarization technique is really important to retrieve all the text information especially from degraded document image. This paper explains about adaptive binarization using Gatos’s method. Gatos’s method is doing preprocessing, foreground estimation using Sauvola’s method, background estimation, upsampling, final thresholding and postprocessing. In this paper, Sauvola’s method is final thresholding from Wiener filter image result and source image, and count F-Measure from both of these binary image results. By using optimum constant value on k value, n local window, Ksw and Ksw1, Gatos’s method can produced binary image better than Sauvola’s method based on F-Measure value. Sauvola’s method produces average value F=84,62%, Sauvola’s method with Wiener filter produces average value F=99.06% and Gatos’s method produces average value F=99,43%. Keyword : Degraded Document Image, Adaptive Approcah for Binarization, Gatos’s  Method, Sauvola’s MethodDOI: 10.18495/comengapp.22.185194
Proposed Developements of Blind Signature Scheme Based on ECC Amounas, F.; Kinani, E.H. El
Computer Engineering and Applications Journal (ComEngApp) Vol. 2 No. 2 (2013)
Publisher : Universitas Sriwijaya

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Abstract

Detecting and gaining clues from the crime scene plays a major role in the process of investigation. Now a days with increase of cybercrime and fraud on the internet, digital forensics gaining importance. It needs to collect information the events happened at crime place. This paper deals with event reconstruction process which is useful for digital crime scene. The process of reconstruction is based on crime event and event characteristics. It also furnishes crime rate guess using some semi-formal techniques. Keyword: Digital Investigation, Digital Forensics, Events, Attack Trees, Reconstruction
Exploration based Genetic Algorithm for Job Scheduling on Grid Computing Abdelrahman, Hanaa; Yousif, Adil; Bashir, Mohammed Bakri
Computer Engineering and Applications Journal (ComEngApp) Vol. 5 No. 3 (2016)
Publisher : Universitas Sriwijaya

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Abstract

Grid computing presents a new trend to distribute and Internet computing to coordinate large scale heterogeneous resources providing sharing and problem solving in dynamic, multi- institutional virtual organizations. Scheduling is one of the most important problems in computational grid to increase the performance. Genetic Algorithm is adaptive method that can be used to solve optimization problems, based on the genetic process of biological organisms. The objective of this research is to develop a job scheduling algorithm using genetic algorithm with high exploration processes. To evaluate the proposed scheduling algorithm this study conducted a simulation using GridSim Simulator and a number of different workload. The research found that genetic algorithm get best results when increasing the mutation and these result directly proportional with the increase in the number of job. The paper concluded that, the mutation and exploration process has a good effect on the final execution time when we have large number of jobs. However, in small number of job mutation has no effects.
Creating a Business Value while Transforming Data Assets using Machine Learning Dimitrovska, Ivana; Malinovski, Toni
Computer Engineering and Applications Journal (ComEngApp) Vol. 6 No. 2 (2017)
Publisher : Universitas Sriwijaya

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Abstract

Machine learning enables computers to learn from large amounts of data without specific programming. Besides its commercial application, companies are starting to recognize machine learning importance and possibilities in order to transform their data assets into business value. This study explores integration of machine learning into business core processes, while enabling predictive analytics that can increase business values and provide competitive advantage. It proposes machine learning algorithm based on regression analysis for a business solution in large enterprise company in Macedonia, while predicting real-value outcome from a given array of business inputs. The results show that most of the machine learning predictive values for the desired process output deviated from 0 to 15% of actual employees' decision. Hence, it verifies the appropriateness of the chosen approach, with predictive accuracy that can be meaningful in practice. As a machine learning case study in business context, it contains valuable information that can help companies understand the significance of machine learning for enterprise computing. It also points out some potential pitfalls of machine learning misuse.
Weighting Facial Features Extraction using Geometric Average Saparudin
Computer Engineering and Applications Journal (ComEngApp) Vol. 6 No. 1 (2017)
Publisher : Universitas Sriwijaya

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Abstract

Human facial feature extraction is an important process in the face recognition system. The quality of the results from the extraction of human facial features is determined by the degree of accuracy. The weighting of human facial features is used to test the accuracy of the methods used. This research produces the process of weighting the facial features automatically. The results obtained are the same as those seen by the human eyes.  
Cluster Analysis of Obesity Risk Levels Using K-Means And DBScan Methods Geovani, Dite; Umari, Zainal; Ramadini, Suci
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

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Abstract

Obesity is defined as excessive fat accumulation and abnormal accumulation of adipose tissue in the human body that poses health risks. The causes of obesity are multifactorial and include environmental and individual factors. Several factors that cause obesity include genetic, behavioral and environmental factors. Obesity causes various problems in various fields, including health, employment, demographics, economics and family. The problem of obesity has a significant impact on public health. Therefore, understanding and predicting the level of obesity risk is important in efforts to prevent and treat obesity risk. Data on eating habits, physical activity, and other factors associated with obesity levels in certain populations can provide an important basis for understanding obesity risk. This research clusters the risk of obesity to find hidden patterns in the data. The stages in this research consist of pre-processing, clustering, and analysis. The clustering methods used are K-means and DBSCAN. In clustering using the K-means method with a parameter value of k , results are obtained with the same pattern as clustering using the DBSCAN method with a parameter value of epsilon and a minimum sample . In clustering using the K-means method with a parameter value of k , Four clusters were formed which had different patterns. The clustering results obtained in this research can be used as an effort to prevent and treat the risk of obesity.
Turbofan Engine Remaining Useful Life Prediction Using 1-Dimentional Convolutional Neural Network Fauzan, Ahmad; Handayani, Lestari; Insani, Fitri; Jasril; Sanjaya, Suwanto
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

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Abstract

Turbofan engines have been the dominant type of engine in aircraft for the last forty years. Ensuring the quality of these engines is crucial for flight safety, particularly for long-distance flights. However, their performance degrades over time, impacting flight safety. To address this issue, it is essential to predict potential engine failures by estimating the Remaining Useful Life (RUL) of the engines Deep learning, especially Convolutional Neural Networks (CNNs), has demonstrated exceptional proficiency in handling intricate, non-linear data, leading to improved RUL predictionsdue to their ability to process complex and non-linear data. In this project, a 1-D CNN is used to predict RUL using the NASA C-MAPSS FD001 dataset, which consists of 3 settings and 21 sensors, though sensors with stagnant readings are excluded. The dataset is normalized using min-max and z-score methods, and then segmented into sequences for input into the 1-D CNN model. Various training scenarios were evaluated, with the best RMSE of 3.26 achieved using 10 epochs, a learning rate of 0.0001, and z-score normalization. The results indicate that feature selection can produce a lower RMSE compared to scenarios without feature selection.
Imbalanced Data NearMiss for Comparison of SVM and Naive Bayes Algorithms Gunawan , Wawan; Devianto , Yudo; Sari, Anggi Puspita
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

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Abstract

The study aims to improve the diagnosis, management, and prevention of HIV/AIDS by using classification algorithms. The dataset used consists of 707,379 records and 89 columns. Data preprocessing includes removing irrelevant attributes, handling inconsistencies, and balancing the data using the NearMiss method, resulting in a balanced proportion of reactive and non-reactive HIV cases. Once the data is balanced, it is split into several ratios: 60:40, 70:30, 80:20, and 90:10. The classification models used in this study are Naive Bayes and SVM. The models are evaluated using the metrics Accuracy, Precision, Recall, and F1-Score. The results show that the SVM model achieves the highest accuracy of 82.6% with a 90:10 data split at a 6-fold value, and 82.2% with a 60:40 data split at a 5-fold value. On the other hand, Naive Bayes achieves the highest accuracy of 61.1% with a 60:40 data split.
MRI-Based Brain Tumor Instance Segmentation Using Mask R-CNN Nasrudin, Muhammad
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

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Abstract

Brain tumor segmentation is a crucial step in medical image analysis for the accurate diagnosis and treatment of patients. Traditional methods for tumor segmentation often require extensive manual effort and are prone to variability. In this study, we propose an automated approach for brain tumor segmentation using Mask R-CNN, a state-of-the-art deep learning model for instance segmentation. Our method leverages MRI images to identify and delineate brain tumors with high precision. We trained the Mask R-CNN model on a dataset of annotated MRI images and evaluated its performance using the mean Average Precision (mAP) metric. The results demonstrate that our model achieves a high mAP of 90.3%, indicating its effectiveness in accurately segmenting brain tumors. This automated approach not only reduces the manual effort required for tumor segmentation but also provides consistent and reliable results, potentially improving clinical outcomes.

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