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Journal of Computing Theories and Applications
Published by Universitas Dian Nuswantoro
ISSN : -     EISSN : 30249104     DOI : https://doi.org/10.33633/jcta
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Journal of Computing Theories and Applications (JCTA) is a refereed, international journal that covers all aspects of foundations, theories and the practical applications of computer science. FREE OF CHARGE for submission and publication. All accepted articles will be published online and accessed for free. The review process is carried out rapidly, about two until three weeks, to get the first decision. The journal publishes only original research papers in the areas of, but not limited to: Artificial Intelligence Big Data Bioinformatics Biometrics Cloud Computing Computer Graphics Computer Vision Cryptography Data Mining Fuzzy Systems Game Technology Image Processing Information Security Internet of Things Intelligent Systems Machine Learning Mobile Computing Multimedia Technology Natural Language Processing Network Security Pattern Recognition Signal Processing Soft Computing Speech Processing Special emphasis is given to recent trends related to cutting-edge research within the domain. If you want to become an author(s) in this journal, you can start by accessing the About page. You can first read the Policies section to find out the policies determined by the JCTA. Then, if you submit an article, you can see the guidelines in the Author Guidelines or Author Guidelines section. Each journal submission will be made online and requires prospective authors to register and have an account to be able to submit manuscripts.
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 8 Documents
 1 
High-Performance Convolutional Neural Network Model to Identify COVID-19 in Medical Images Macellino Setyaji Sunarjo; Hong-Seng Gan; De Rosal Ignatius Moses Setiadi
Journal of Computing Theories and Applications Vol 1, No 1 (2023): August-September
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i1.8936

Abstract

Convolutional neural network (CNN) is a deep learning (DL) model that has significantly contributed to medical systems because it is very useful in digital image processing. However, CNN has several limitations, such as being prone to overfitting, not being properly trained if there is data duplication, and can cause unwanted results if there is an imbalance in the amount of data in each class. Data augmentation techniques are used to overcome overfitting, eliminate data duplication, and random under sampling methods to balance the amount of data in each class, to overcome these problems. In addition, if the CNN model is not designed properly, the computation is less efficient. Research has proved that data augmentation can prevent or overcome overfitting, eliminating duplicate data can make the model more stable, and balancing the amount of data makes the model unbiased and easy to learn new data as evidenced through model evaluation and testing. The results also show that the custom convolutional neural network model is the best model compared to ResNet50 and VGG19 in terms of accuracy, precision, recall, F1-score, loss performance, and computation time efficiency
Web Phishing Classification using Combined Machine Learning Methods Bambang Mahardhika Poerbo Waseso; Noor Ageng Setiyanto
Journal of Computing Theories and Applications Vol 1, No 1 (2023): August-September
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i1.8898

Abstract

Phishing is a crime that uses social engineering techniques, both in deceptive statements and technically, to steal consumers' personal identification data and financial account credentials. With the new Phishing machine learning approach, websites can be recognized in real-time. K-Nearest Neighbor(KNN) and Naïve Bayes (NB) are popular machine learning approaches. KNN and NB have their own strengths and weaknesses. By combining the two, deficiencies can be covered. So this study proposes to combine K-Nearest Neighbor with Naïve Bayes to classify phishing websites. Based on the results of the accuracy test of the combination of KNN with k=8 and Naïve Bayes, a maximum accuracy of 93.44% is produced. This result is 6.25% superior compared to using only one classifier.
Plant Diseases Classification based Leaves Image using Convolutional Neural Network Satrio Bagus Imanulloh; Ahmad Rofiqul Muslikh; De Rosal Ignatius Moses Setiadi
Journal of Computing Theories and Applications Vol 1, No 1 (2023): August-September
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i1.8877

Abstract

Plant disease is one of the problems in the world of agriculture. Early identification of plant diseases can reduce the risk of loss, so automation is needed to speed up identification. This study proposes a custom-designed convolutional neural network (CNN) model for plant disease recognition. The proposed CNN model is not complex and lightweight, so it can be implemented in model applications. The proposed CNN model consists of 12 CNN layers, which consist of eight layers for feature extraction and four layers as classifiers. Based on the experimental results of a plant disease dataset consisting of 38 classes with a total of 87,867 image records. The proposed model can get high performance and not overfitting, with 97%, 98%, 97% and 97%, respectively, for accuracy, precision, recall and f1-score. The performance of the proposed model is also better than some popular pre-trained models, such as InceptionV3 and MobileNetV2. The proposed model can also work well when implemented in mobile applications.
Image Encryption using Half-Inverted Cascading Chaos Cipheration De Rosal Ignatius Moses Setiadi; Robet Robet; Octara Pribadi; Suyud Widiono; Md Kamruzzaman Sarker
Journal of Computing Theories and Applications Vol 1, No 2 (2023): (in Process)
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9388

Abstract

This research introduces an image encryption scheme combining several permutations and substitution-based chaotic techniques, such as Arnold Chaotic Map, 2D-SLMM, 2D-LICM, and 1D-MLM. The proposed method is called Half-Inverted Cascading Chaos Cipheration (HIC3), designed to increase digital image security and confidentiality. The main problem solved is the image's degree of confusion and diffusion. Extensive testing included chi-square analysis, information entropy, NCPCR, UACI, adjacent pixel correlation, key sensitivity and space analysis, NIST randomness testing, robustness testing, and visual analysis. The results show that HIC3 effectively protects digital images from various attacks and maintains their integrity. Thus, this method successfully achieves its goal of increasing security in digital image encryption
Methodologies of the Validation of Software Architectures Amina El Murabet; Anouar Abtoy
Journal of Computing Theories and Applications Vol 1, No 2 (2023): (in Process)
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9332

Abstract

Software architecture validation is the process of assessing whether a software architecture meets its intended requirements and goals. It is an important step in the software development process, as it can help to identify and address potential problems early on before they become more costly and difficult to fix. There are a variety of different methodologies that can be used to validate software architecture. Some of the most common methodologies include Architectural evaluation methods, Architecture tests and reviews, and Model-based validation. This paper will provide an overview of the different methodologies that can be used to validate software architecture. Apart from that, it also analyzes and summarizes the strengths and weaknesses of each method so that it can guide determining the most appropriate methodology for a particular case.
Comprehensive Analysis and Classification of Skin Diseases based on Image Texture Features using K-Nearest Neighbors Algorithm Mamet Adil Araaf; Kristiawan Nugroho; De Rosal Ignatius Moses Setiadi
Journal of Computing Theories and Applications Vol 1, No 1 (2023): August-September
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i1.9185

Abstract

Skin is the largest organ in humans, it functions as the outermost protector of the organs inside. Therefore, the skin is often attacked by various diseases, especially cancer. Skin cancer is divided into two, namely benign and malignant. Malignant has the potential to spread and increase the risk of death. Skin cancer detection traditionally involves time-consuming laboratory tests to determine malignancy or benignity. Therefore, there is a demand for computer-assisted diagnosis through image analysis to expedite disease identification and classification. This study proposes to use the K-nearest neighbor (KNN) classifier and Gray Level Co-occurrence Matrix (GLCM) to classify these two types of skin cancer. Apart from that, the average filter is also used for preprocessing. The analysis was carried out comprehensively by carrying out 480 experiments on the ISIC dataset. Dataset variations were also carried out using random sampling techniques to test on smaller datasets, where experiments were carried out on 3297, 1649, 825, and 210 images. Several KNN parameters, namely the number of neighbors (k)=1 and distance (d)=1 to 3 were tested at angles 0, 45, 90, and 135. Maximum accuracy results were 79.24%, 79.39%, 83.63%, and 100% for respectively 3297, 1649, 825, and 210. These findings show that the KNN method is more effective in working on smaller datasets, besides that the use of the average filter also has a significant contribution in increasing the accuracy.
Dataset and Feature Analysis for Diabetes Mellitus Classification using Random Forest Fachrul Mustofa; Achmad Nuruddin Safriandono; Ahmad Rofiqul Muslikh; De Rosal Ignatius Moses Setiadi
Journal of Computing Theories and Applications Vol 1, No 1 (2023): August-September
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i1.9190

Abstract

Diabetes Mellitus is a hazardous disease, and according to the World Health Organization (WHO), diabetes will be one of the main causes of death by 2030. One of the most popular diabetes datasets is PIMA Indians, and this dataset has been widely tested on various machine learning (ML) methods, even deep learning (DL). But on average, ML methods are not able to produce good accuracy. The quality of the dataset and features is the most influential thing in this case, so deeper investment is needed to examine this dataset. This research will analyze and compare the PIMA Indians and Abelvikas datasets using the Random Forest (RF) method. The two datasets are imbalanced, in fact, the Abelvikas dataset is more imbalanced and has a larger number of classes so it is be more complex. The RF was chosen because it is one of the ML methods that has the best results on various diabetes datasets. Based on the test results, very contrasting results were obtained on the two datasets. Abelvikas had accuracy, precision, and recall, reaching 100%, and PIMA Indians only achieved 75% for accuracy, 87% for precision, and 80% for the best recall. Testing was done with 3, 5, 7, 10, and 15 tree number parameters. Apart from that, it was also tested with k-fold validation to get valid results. This determines that the features in the Abelvikas dataset are much better because more complete glucose features support them.
Forging a User-Trust Memetic Modular Neural Network Card Fraud Detection Ensemble: A Pilot Study Arnold Adimabua Ojugo; Maureen Ifeanyi Akazue; Patrick Ogholuwarami Ejeh; Nwanze Chukwudi Ashioba; Christopher Chukwufunaya Odiakaose; Rita Erhovwo Ako; Frances Uche Emordi
Journal of Computing Theories and Applications Vol 1, No 2 (2023): (in Process)
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9259

Abstract

The advent of the Internet as an effective means for resource sharing has consequently, led to proliferation of adversaries, with unauthorized access to network resources. Adversaries achieved fraudulent activities via carefully crafted attacks of large magnitude targeted at personal gains and rewards. With the cost of over $1.3Trillion lost globally to financial crimes and the rise in such fraudulent activities vis the use of credit-cards, financial institutions and major stakeholders must begin to explore and exploit better and improved means to secure client data and funds. Banks and financial services must harness the creative mode rendered by machine learning schemes to help effectively manage such fraud attacks and threats. We propose HyGAMoNNE – a hybrid modular genetic algorithm trained neural network ensemble to detect fraud activities. The hybrid, equipped with knowledge to altruistically detect fraud on credit card transactions. Results show that the hybrid effectively differentiates, the benign class attacks/threats from genuine credit card transaction(s) with model accuracy of 92%.

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