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Menampilkan 11–19 dari 19 artikel
Exploring DQN-Based Reinforcement Learning in Autonomous Highway Navigation Performance Under High-Traffic Conditions
Nugroho, Sandy
; Setiadi, De Rosal Ignatius Moses
; Islam, Hussain Md Mehedul
Journal of Computing Theories and Applications
Vol 1
, No 3
(2024)
Driving in a straight line is one of the fundamental tasks for autonomous vehicles, but it can become complex and challenging, especially when dealing with high-speed highways and dense traffic conditions. This research aims to explore the Deep-Q Networking (DQN) model, which is one of the reinforcement learning (RL) methods, in a highway environment. DQN was chosen due to its proficiency in handling complex data through integrated neural network approximations, making it capable of addressing h...
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Music-Genre Classification using Bidirectional Long Short-Term Memory and Mel-Frequency Cepstral Coefficients
Wijaya, Nantalira Niar
; Setiadi, De Rosal Ignatius Moses
; Muslikh, Ahmad Rofiqul
Journal of Computing Theories and Applications
Vol 1
, No 3
(2024)
Music genre classification is one part of the music recommendation process, which is a challenging job. This research proposes the classification of music genres using Bidirectional Long Short-Term Memory (BiLSTM) and Mel-Frequency Cepstral Coefficients (MFCC) extraction features. This method was tested on the GTZAN and ISMIR2004 datasets, specifically on the IS-MIR2004 dataset, a duration cutting operation was carried out, which was only taken from seconds 31 to 60 so that it had the same durat...
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27 Sitasi
Hybrid Quantum Key Distribution Protocol with Chaotic System for Securing Data Transmission
Setiadi, De Rosal Ignatius Moses
; Akrom, Muhamad
Journal of Computing Theories and Applications
Vol 1
, No 2
(2023)
This research proposes a combination of Quantum Key Distribution (QKD) based on the BB84 protocol with Improved Logistic Map (ILM) to improve data transmission security. This method integrates quantum key formation from BB84 with ILM encryption. This combination creates an additional layer of security, where by default, the operation on BB84 is only XOR-substitution, with the addition of ILM creating a permutation operation on quantum keys. Experiments are measured with several quantum measureme...
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9 Sitasi
Butterflies Recognition using Enhanced Transfer Learning and Data Augmentation
Adityawan, Harish Trio
; Farroq, Omar
; Santosa, Stefanus
; Islam, Hussain Md Mehedul
; Sarker, Md Kamruzzaman
; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications
Vol 1
, No 2
(2023)
Butterflies’ recognition serves a crucial role as an environmental indicator and a key factor in plant pollination. The automation of this recognition process, facilitated by Convolutional Neural Networks (CNNs), can expedite this task. Several pre-trained CNN models, such as VGG, ResNet, and Inception, have been widely used for this purpose. However, the scope of previous research has been somewhat constrained, focusing only on a maximum of 15 classes. This study proposes to modify the CNN Ince...
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14 Sitasi
Image Encryption using Half-Inverted Cascading Chaos Cipheration
Setiadi, De Rosal Ignatius Moses
; Robet, Robet
; Pribadi, Octara
; Widiono, Suyud
; Sarker, Md Kamruzzaman
Journal of Computing Theories and Applications
Vol 1
, No 2
(2023)
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...
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14 Sitasi
Plant Diseases Classification based Leaves Image using Convolutional Neural Network
Imanulloh, Satrio Bagus
; Muslikh, Ahmad Rofiqul
; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications
Vol 1
, No 1
(2023)
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 extractio...
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28 Sitasi
Dataset and Feature Analysis for Diabetes Mellitus Classification using Random Forest
Mustofa, Fachrul
; Safriandono, Achmad Nuruddin
; Muslikh, Ahmad Rofiqul
; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications
Vol 1
, No 1
(2023)
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...
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32 Sitasi
Comprehensive Analysis and Classification of Skin Diseases based on Image Texture Features using K-Nearest Neighbors Algorithm
Araaf, Mamet Adil
; Nugroho, Kristiawan
; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications
Vol 1
, No 1
(2023)
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 a...
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24 Sitasi
High-Performance Convolutional Neural Network Model to Identify COVID-19 in Medical Images
Sunarjo, Macellino Setyaji
; Gan, Hong-Seng
; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications
Vol 1
, No 1
(2023)
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...
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24 Sitasi