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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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Analysis and Implementation of Blowfish and LSB Algorithm on RGB Images using SHA-512 Ashari, Ilham Firman; Praseptiawan, Mugi; Baraku, Randi; Filiana, Edinia Rosa
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 1 (2024)
Publisher : Universitas Sriwijaya

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Abstract

The growth of the internet globally keeps increasing as time goes. There's a big amount of data type saved there too. Those data need to be secured so anyone who doesn't have the right to access them can access it. The purpose of this article is to secure text information into image media using the Blowfish method for encrypting text information and securing it using the Hash function SHA-512 and then embedded it in image media using the Least Significant Bit (LSB) method. The result of implementing those methods using image media sized 138Kb and 39.85Kb with plaintext measuring 27 and 85 characters shows that integrity data is secured with SHA-512 method. The test result using PSNR method to get the score of image quality after embedding information to the image shows that the average number of PSNR’s score is 70,74 dB which means the quality is good and has less difference from the original image.
The Combination of Black Hat Transform and U-Net in Image Enhancement and Blood Vessel Segmentation in Retinal Images Darmo, Cahyo Pambudi; Kesuma, Lucky Indra; Geovani, Dite
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

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Abstract

Diabetic Retinopathy (DR) is a disorder of the eye caused by damage to blood vessels in the retina. Damage to the retinal blood vessels can be analyzed by segmenting the blood vessels on the image. This study proposes a combination of image enhancement and blood vessel segmentation in retinal images. Retinal image enhancement is carried out using the black hat transform method to obtain a detailed view of blood vessels in retinal images. Segmentation of blood vessels in retinal images is carried out using the U-Net architecture. The results of image enhancement are measured using MSE and PSNR. This study has an MSE value below 0.05 and a PSNR above 90dB. The MSE and PSNR values obtained show that the black hat transform method is very good at image enhancement. Segmentation has an accuracy value above 0.95 and a sensitivity value above 0.85. In addition, the specificity value and f1-score are above 0.8. This shows that the proposed stages of image enhancement and blood vessel segmentation are able to accurately recognize blood vessel features in retinal images.
Application of Machine Learning in Clustering Maize Producing Regions in Indonesia Eliyani; Dwiasnati, Saruni; Arif , Sutan Mohammad; Avrizal, Reza; Fatimah, Nona
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 2 (2024)
Publisher : Universitas Sriwijaya

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Abstract

Maize is considered an important commodity with promising market prospects. Given the importance of maize, there is a need to increase maize production to meet people's needs and maintain price stability. This study aims to group maize production in Indonesia by region, with the hope of finding areas that have the potential to become maize production centers to reduce dependence on imports. The data used in this research was obtained from the Central Statistics Agency, covering information from 34 provinces during the 2017-2021 period. This analysis uses the K-Means method with the Python programming language. The number of groups is determined using the Elbow Method. The results of this research show that there are three categories of maize production regions: regions with low maize production (below average), regions with medium maize production, and regions with high maize production. A total of 25 provinces are in the low production category, eight provinces are in the medium category, and only East Java is in the high production category.
Forecasting Of Intensive Care Unit Patient Heart Rate Using Long Short-Term Memory Firdaus; Fachrurrozi, Muhammad; Nurmaini, Siti; Tutuko, Bambang; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Sapitri, Ade Iriani; Islami, Anggun; Maharani, Masayu Nadila; Putra, Bayu Wijaya
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

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Abstract

Cardiac arrest remains a critical concern in Intensive Care Units (ICUs), with alarmingly low survival rates. Early prediction of cardiac arrest is challenging due to the complexity of patient data and the temporal nature of ICU care. To address this challenge, we explore the use of Deep Learning (DL) models, specifically Long ShortTerm Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), for forecasting ICU patient heart rates. We utilize a dataset extracted from the MIMIC III database, which poses the typical challenges of irregular time series data and missing values. Our research encompasses a comprehensive methodology, including data preprocessing, model development, and performance evaluation. Data preprocessing involves regularizing and imputing missing values, as well as data normalization. The dataset is partitioned into training, testing, and validation sets to facilitate model training and evaluation. Fine-tuning of hyperparameters is conducted to optimize each DL architecture's performance. Our results reveal that the GRU architecture consistently outperforms LSTM and BiLSTM in predicting heart rates, achieving the lowest RMSE and MAE values. The findings underscore the potential of DL models, particularly GRU, in enhancing the early detection of cardiac events in ICU patients.
Comparison of Naive Bayes and Support Vector Machine (SVM) Algorithms Regarding The Popularity of Presidential Candidates In The Upcoming 2024 Presidential Election Nurrizky, Fadli; Dwiasnati, Saruni
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 1 (2024)
Publisher : Universitas Sriwijaya

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Abstract

This study aims to compare the effectiveness of two classification algorithms, Naive Bayes and Support Vector Machine (SVM), in analyzing the popularity of presidential candidates for the 2024 Presidential Election (Pilpres). The popularity of presidential candidates plays a crucial role in campaign strategies and political decision-making in the modern political era. This research utilizes data from social media, encompassing public sentiment towards presidential candidates and related political issues. The research results indicate that SVM achieves an accuracy rate of 97%, while Naive Bayes achieves 95%, demonstrating the superiority of SVM in predicting the popularity of presidential candidates. In conclusion, the selection of the appropriate algorithm for analyzing complex political data has a significant impact, and the high accuracy rates of both algorithms provide valuable guidance for political decisionmakers and campaign teams in preparation for the upcoming 2024 Pilpres.
A Hybrid of Fuzzy C-Means For The Segmentation In CT Scan and X-Ray Images For Screening The COVID-19 Patients WangNo, Nitit; Pichai, Supailin; Setiadi, Raihan Mufid
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 1 (2024)
Publisher : Universitas Sriwijaya

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Abstract

In this paper, using CT scan and X-ray images, we present a hybrid approach, based on combining fuzzy C-means with k-means clustering, to evaluate and determine pneumonia infection caused by the coronavirus disease (COVID-19). To achieve this objective, we introduce a hybrid method that combines fuzzy C-means clustering with K-means clustering. This hybrid approach is designed to effectively segment object boundaries within medical images, enabling the precise identification of pneumoniarelated features. In addition to our hybrid method, we compare its performance with two other segmentation approaches: the Expectation Maximization (EM) algorithm and 2D Entropy segmentation. Which, the method we propose uses a comparison between the performances of the based on a database of medical imaging test. Experimental results showed that the proposed approach outperforms, it was found that the hybrid fuzzy C-means algorithm segmentation images methods give better performance in terms of accuracy, precision, and F-measure, which is effective in boundaries segmentation. Comparative results of the accuracy and image quality index demonstrate the robustness of AI. It also helps to improve work efficiency with accurate analysis of COVID-19 infection on CT scan and X-rays. In addition, the approach helps radiologists make clinical decisions for diagnosis, follow-up, and prognosis.
Image Classification of Traditional Indonesian Cakes Using Convolutional Neural Network (CNN) Azizah, Azkiya Nur; Budiman, Irwan; Indriani, Fatma; Faisal, M. Reza; Herteno, Rudy
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 2 (2024)
Publisher : Universitas Sriwijaya

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Abstract

Indonesia is one of the countries famous for its traditional culinary. Traditional cakes in Indonesia are traditional snacks typical of the archipelago's culture which have a variety of textures, shapes, colors that vary and some are similar so that there are still many people who do not know the name of the cake from the many types of traditional Indonesian cakes. The problem can be solved by creating a traditional cake image recognition system that can be programmed and trained to classify various types of traditional Indonesian cakes. The Convolutional Neural Network method with the AlexNet architecture model is used in this research to predict various kinds of traditional Indonesian cakes. The dataset used in this research is 1846 datasets with 8 classes of cake images. This study trained the AlexNet model with several optimizers, namely, Adam optimizer, SGD, and RMSprop. The best parameters from the model testing results are at batchsize 16, epoch 50, learning rate 0.01 for SGD optimizer and learning rate 0.001 for Adam and RMSprop optimizers. Each optimizer tested produces different accuracy, precision, recall, and f1_score values. The highest test results that have been carried out on the image dataset of typical Indonesian traditional cakes are obtained by the Adam optimizer with an accuracy value of 79%.
Augmented Reality in STEM Using Personalized Learning to Promote Students’ Understanding Erlangga; Mukhlis, Rizki; Wihardi, Yaya; Raflesia, Sarifah Putri
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 2 (2024)
Publisher : Universitas Sriwijaya

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Abstract

The current curriculum highlights the premise of self-directed learning performed by students. Additionally, technological uses in educational settings prove to be a challenging task in a sense of implementing them in learning media and materials used in the classroom. This study aims at investigating the utilization of augmented reality (AR) in STEM (Science, Mathematics, Engineering, and Technology) using personalized learning. This study employed pre-experimental research design, specifically adopting One-Group Pretest-Posttest Design. The findings highlight that students’ pretest scores on average reached 51,6 and significantly improved to 82,67 in their posttest, whereas students’ gain score reached 0,64 which is considered as moderate. Their perspectives towards the use of augmented reality with personalized learning were significantly positive with the percentage of 82,1%. It is evident that the use of augmented reality with personalized learning is a viable option when it comes to affecting the learning outcomes.
Anxiety Detection for Autism Children through Vital Signs Monitoring using a Socially Assistive Robot Prihatini, Ekawati; Damsi, Faisal; Marniati, Yessi; Muslimin, Selamat; Husni, Nyayu Latifah; Ramadhan, M. Daffa
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 1 (2025)
Publisher : Universitas Sriwijaya

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Abstract

Socially Assistive Robot (SAR) to detect anxiety levels in children with Autism Spectrum Disorder (ASD), a condition often accompanied by difficulties in recognising and expressing emotions, including anxiety. Early recognition of anxiety in children with Autism Spectrum Disorder (ASD) is crucial as it can affect their behaviour and social interactions. This SAR monitors vital signs namely blood pressure, heart rate and body temperature. This study involved children with Autism Spectrum Disorder (ASD) with two conditions, namely Asperger Syndrome and Classical Autism who interacted with a Socially Assistive Robot (SAR) equipped with a tensimeter (MPS20N0040D sensor) for blood pressure, MAX30100 sensor for heart rate, and MLX90614 sensor to measure body temperature. Results show that the Socially Assistive Robot (SAR) is able to measure vital signs with high accuracy and provide an indication of anxiety levels effectively, as vital signs correlate with anxiety levels. These findings demonstrate the potential of the Socially Assistive Robot (SAR) as a reliable tool in anxiety monitoring in children with ASD, with important implications for the development of future therapeutic interventions.
Emotion Classification in Indonesian Text Using IndoBERT Rizky, Aditya Saiful; Hidayat, Erwin Yudi
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 1 (2025)
Publisher : Universitas Sriwijaya

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Abstract

Mental health issues have become a challenge that affects many individuals around the world. A 2018 WHO report noted an increase in deaths by suicide, with a frequency of one case every 40 seconds. The Ipsos Global 2023 survey showed that 44% of respondents in 31 countries are concerned about mental health, while 30% identified stress as a major issue. In Indonesia, the mental health situation is also a serious concern. The 2022 I-NAMHS survey found that 34.9% of adolescents face mental health problems, but only 2.6% of them utilize counseling services. Emotion detection in text is challenging due to the absence of facial expressions or voice modulation. This study aims to classify emotions in Indonesian text using the IndoBERT model. The dataset used consists of 5079 tweets with five emotion labels: Angry, Fear, Joy, Love, and Sad. Parameter variations include the composition of training, validation, and test data split (80:10:10, 75:15:15, and 60:20:20), as well as the combination of learning rate (1e-2 to 1e-7) and batch size (8, 16, and 32). The model was trained for 25 epochs with the application of early stop and patience for 5 epochs. The experimental results showed that the composition of data split 80:10:10, learning rate 1e-6, and batch size 8 resulted in optimal classification. Although some experiments showed indications of overfitting, this research has important implications in the early detection of emotions and can help in mental health treatment efforts.

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