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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 323 Documents
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Fake News Detection Using Optimized Convolutional Neural Network and Bidirectional Long Short-Term Memory Sari, Winda Kurnia; Azhar, Iman Saladin B.; Yamani, Zaqqi; Florensia, Yesinta
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

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Abstract

The spread of fake news in the digital age threatens the integrity of online information, influences public opinion, and creates confusion. This study developed and tested a fake news detection model using an enhanced CNN-BiLSTM architecture with GloVe word embedding techniques. The WELFake dataset comprising 72,000 samples was used, with training and testing data ratios of 90:10, 80:20, and 70:30. Preprocessing involved GloVe 100-dimensional word embedding, tokenization, and stopword removal. The CNN-BiLSTM model was optimized with hyperparameter tuning, achieving an accuracy of 96%. A larger training data ratio demonstrated better performance. Results indicate the effectiveness of this model in distinguishing fake news from real news. This study shows that the CNN-BiLSTM architecture with GloVe embedding can achieve high accuracy in fake news detection, with recommendations for further research to explore preprocessing techniques and alternative model architectures for further improvement.
The Eye and Nose Identification Chip Controller-Based on Robot Vision Using Weightless Neural Network Method Zarkasi, Ahmad; Ubaya, Huda; Exaudi, Kemahyanto; Fitriyanto, Megi
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

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Abstract

Increasingly advanced image analysis in computer vision, allowing computers to interpret, identify, and analyze pictures with accuracy comparable to humans. The availability of data sources in decimal, hexadecimal, or binary forms enables researchers to take the initiative in applying their study findings. Decimal formats are typically used on traditional computers like desktops and minicomputers, whereas hexadecimal and binary formats were utilized on single-chip controllers. Weightless Neural Network is a method that can be implemented in a single chip controller. The aim of this research is to develop a facial recognition system, for eye and mouth identification, that works in a single chip controller or also called a microcontroller. The suggested method is a Weightless Neural Network with Immediate Scan approach for processing and identifying eye and nose patterns. The data will be handled in many memory locations that are specifically designed to handle massive volumes of data. The data is made up of primary face data sheets and face input data. The data sets utilized are (x,y) pixels, and frame sizes range from 90x90 pixels to 110x110 pixels. Each face shot will be processed by selecting the region of the eyes and nose and saving it as an image file. The eye and nose will identify the face frame. Next, the photos will be converted to binary format. A magazine matrix will be used to transmit binary data from a minicomputer to a microcontroller via serial connection. Based on a known pattern, the resultant similarity accuracy is 83,08% for the eye and 84,09% for the sternum. In contrast, the similarity percentage for an eye ranges from 70% to 85% for an undefined pattern.
E-mail spam filtering by a new hybrid feature selection method using IG and CNB wrapper Pourhashemi, Seyed Mostafa
Computer Engineering and Applications Journal (ComEngApp) Vol. 2 No. 3 (2013)
Publisher : Universitas Sriwijaya

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Abstract

The growing volume of spam emails has resulted in the necessity for more accurate and efficient email classification system. The purpose of this research is presenting an machine learning approach for enhancing the accuracy of automatic spam detecting and filtering and separating them from legitimate messages. In this regard, for reducing the error rate and increasing the efficiency, the hybrid architecture on feature selection has been used. Features used in these systems, are the body of text messages. Proposed system of this research has used the combination of two filtering models, Filter and Wrapper, with Information Gain (IG) filter and Complement Naïve Bayes (CNB) wrapper as feature selectors. In addition, Multinomial Naïve Bayes (MNB) classifier, Discriminative Multinomial Naïve Bayes (DMNB) classifier, Support Vector Machine (SVM) classifier and Random Forest classifier are used for classification. Finally, the output results of this classifiers and feature selection methods are examined and the best design is selected and it is compared with another similar works by considering different parameters. The optimal accuracy of the proposed system is evaluated equal to 99%.
Two phase privacy preserving data mining Gupta, Pooja; Kumar, Ashish
Computer Engineering and Applications Journal (ComEngApp) Vol. 2 No. 3 (2013)
Publisher : Universitas Sriwijaya

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Abstract

The paper proposes a framework to improve the privacy preserving data mining. The approach adopted provides security at both the ends i.e. at the data transmission time as well as in the data mining process using two phases. The secure data transmission is handled using elliptic curve cryptography (ECC) and the privacy is preserved using k-anonymity. The proposed framework ensures highly secure environment. We observed that the framework outperforms other approaches [8] discussed in the literature at both ends i.e. at security and privacy of data. Since most of the approaches have considered either secure transmission or privacy preserving data mining but very few have considered both. We have used WEKA 3.6.9 for experimentation and analysis of our approach. We have also analyzed the case of k-anonymity when the numbers of records in a group are less than k (hiding factor) by inserting fake records. The obtained results have shown the pattern that the insertion of fake records leads to more accuracy as compared to full suppression of records. Since, full suppression may hide important information in cases where records are less than k, on the other hand in the process of fake records insertion; records are available even if number of records in a group is less than k.
Design Concept of Convexity Defect Method on Hand Gestures as Password Door Lock Passarella, Rossi; Fadli, Muhammad; Sutarno
Computer Engineering and Applications Journal (ComEngApp) Vol. 2 No. 3 (2013)
Publisher : Universitas Sriwijaya

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Abstract

In this paper we purpose a several steps to implement security for locking door by using hand gestures as password. The methods considered as preprocessing image, skin detection and Convexity Defection. The main components of the system are Camera, Personal Computer (PC), Microcontroller and Motor (Lock). Bluetooth communication are applied to communicate between PC and microcontroller to open and lock door used commands character such as “O” and “C”. The results of this system show that the hand gestures can be measured, identified and quantified consistently.
Human Perception Based Color Image Segmentation Gargote, Neeta; Devaraj, Savitha; Shahapure, Shravani
Computer Engineering and Applications Journal (ComEngApp) Vol. 2 No. 3 (2013)
Publisher : Universitas Sriwijaya

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Abstract

Color image segmentation is probably the most important task in image analysis and understanding. A novel Human Perception Based Color Image Segmentation System is presented in this paper. This system uses a neural network architecture. The neurons here uses a multisigmoid activation function. The multisigmoid activation function is the key for segmentation. The number of steps ie. thresholds in the multisigmoid function are dependent on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSI color space. Here the main use of neural network is to detect the number of objects automatically from an image. It labels the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images.
Improvement and Comparison of Mean Shift Tracker using Convex Kernel Function and Motion Information Chaudhari, S.B.; Warhade, K.K.; Wadhai, V.M.; Choudhari, N.K.
Computer Engineering and Applications Journal (ComEngApp) Vol. 2 No. 3 (2013)
Publisher : Universitas Sriwijaya

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Abstract

Any tracking algorithm must be able to detect interested moving objects in its field of view and then track it from frame to frame. The tracking algorithms based on mean shift are robust and efficient. But they have limitations like inaccuracy of target localization, object being tracked must not pass by another object with similar features i.e. occlusion and fast object motion. This paper proposes and compares an improved adaptive mean shift algorithm and adaptive mean shift using a convex kernel function through motion information. Experimental results show that both methods track the object without tracking errors. Adaptive method gives less computation cost and proper target localization and Mean shift using convex kernel function shows good results for the tracking challenges like partial occlusion and fast object motion faced by basic Mean shift algorithm.
How Networking Empirically Influences the Types of Innovation?: Pardis Technology Park as a Case Study Phirouzabadi, Amir Mirzadeh; Mahmoudian, M.; Asghari, M.
Computer Engineering and Applications Journal (ComEngApp) Vol. 2 No. 3 (2013)
Publisher : Universitas Sriwijaya

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Abstract

Nowadays, Innovation can be named as one of the best practices as quality, speed, dependability, flexibility and cost which it helps organization enter to new markets, increase the existing market share and provide it with a competitive edge. In addition, organizations have moved forward from “hiding idea (Closed Innovation)” to “opening them (Open Innovation)”. Therefore, concepts such as “open innovation” and “innovation network” have become important and beneficial to both academic and market society. Therefore, this study tried to empirically study the effects of networking on innovations. In this regard, in order to empirically explore how networking influences innovations, this paper used types of innovations based on OCED definition as organizational, marketing, process and product and compared their changes before and after networking of 45 companies in the network Pardis Technology Park as a case study. The results and findings showed that all of the innovation types were increased after jointing the companies to the network. In fact, we arranged these changing proportions from the most to the least change as marketing, process, organizational and product innovation respectively. Although there were some negative growth in some measures of these innovations after jointing into the network.
Drift-Resilient IoT Energy Monitoring for Low-Cost Voltage and Current Sensors Sulaiman, Abdullahi; Ayodele Isqeel, Abdullateef; Issa , Abdulkabir Olatunji; Issa, Abdulrasheed Yinka; Agbolade, Onasanya Mobolaji
Computer Engineering and Applications Journal Vol. 15 No. 1 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i1.1325

Abstract

Low-cost voltage and current sensors such as the ZMPT101B and ACS712 are widely used in IoT-based energy monitoring due to their affordability and ease of integration. However, their outputs suffer from drift caused by thermal variation, material degradation, and electromagnetic interference, leading to cumulative errors that compromise load monitoring, forecasting, and anomaly detection. This work presents a drift-resilient framework that integrates lightweight filtering and regression-based calibration into a unified pipeline deployable on ESP32-class devices. Moving average and adaptive Kalman filters suppress noise and track drift trends, regression models align sensor outputs with reference standards, and spectrogram-based analysis detects transient drift events for adaptive correction. Experiments under realistic conditions show substantial improvements: voltage RMSE decreased by over 90% (3.45V to 0.30V), current RMSE by 92% (0.065A to 0.005A), and MAPE to below 0.5%. Signal-to-noise ratio improved by approximately 21dB, confirming significant restoration of measurement fidelity. Compared with data-intensive deep learning or AutoML frameworks, the proposed method offers a scalable, interpretable, and resource-efficient solution for long-term IoT energy monitoring. By bridging drift mitigation strategies with the practical constraints of low-cost sensors, this framework enhances the reliability of smart grid and IoT-based infrastructures.
Application of Additive Manufacturing Technology in Custom Surgery and Orthopedic Implants through 3D Bioprinting: Rapid Review Karmilah; Della Afrilliani Sutaryo; Raina Azhari Nariswari; Salma Fajrian Agustin; Riva Nurizkiah; Tanti Intan Nurhayati; Nunung Siti Sukaesih
Computer Engineering and Applications Journal Vol. 15 No. 1 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i1.1326

Abstract

Musculoskeletal disorders, which are the leading cause of global disability, require better implant reconstruction solutions, given the limitations of conventional implants in terms of anatomical fit, stability, and stress shielding risk. The objective of this rapid review is to summarize the latest evidence on the application of Additive Manufacturing (AM), 3D Printing, and 3D Bioprinting technologies in the manufacture of custom orthopedic implants. The method used was a Rapid Review with the PRISMA framework, which involved searching 3,291 articles in the PubMed and ScienceDirect databases and filtering them down to 14 selected articles. The results show that the integration of 3D imaging, 3D printing, and Artificial Intelligence (AI) significantly improves visual-spatial understanding in orthopedic education, as well as improves implant placement accuracy (e.g., in THA), reduces operating time, blood loss, and radiation exposure through the use of AI-based 3D preoperative planning, custom models, and 3D-printed surgical guides. However, challenges remain in terms of cost, preoperative production time, and lack of long-term follow-up data. In conclusion, 3D and AI technologies have revolutionized orthopedic practice by improving accuracy, efficiency, and personalization of therapy, requiring large-scale research and long-term evaluation for sustainable clinical implementation.

Page 32 of 33 | Total Record : 323

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