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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Journal of ICT Research and Applications Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Proceeding of the Electrical Engineering Computer Science and Informatics JADECS (Journal of Art, Design, Art Education and Culture Studies) Jurnal Teknologi dan Sistem Komputer SISFOTENIKA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Information System Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Journal of Fuzzy Systems and Control (JFSC) Journal of Information System and Application Development Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
De Rosal Ignatius Moses Setiadi
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences Other

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Journal : Journal of Computing Theories and Applications

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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): JCTA 1(1) 2023
Publisher : 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
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): JCTA 1(1) 2023
Publisher : 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 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): JCTA 1(2) 2023
Publisher : 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
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): JCTA 1(1) 2023
Publisher : 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 Mustofa, Fachrul; Safriandono, Achmad Nuruddin; Muslikh, Ahmad Rofiqul; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications Vol. 1 No. 1 (2023): JCTA 1(1) 2023
Publisher : 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.
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): JCTA 1(2) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9443

Abstract

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 InceptionV3 model and combine it with three data augmentations to recognize up to 100 butterfly species. To curb overfitting, this study employs a series of data augmentation techniques. In parallel, we refine the InceptionV3 model by reducing the number of layers and integrating four new layers. The test results demonstrate that our proposed model achieves an impressive accuracy of 99.43% for 15 classes with only 10 epochs, exceeding prior models by approximately 5%. When extended to 100 classes, the model maintains a high accuracy rate of 98.49% with 50 epochs. The proposed model surpasses the performance of standard pre-trained models, including VGG16, ResNet50, and InceptionV3, illustrating its potential for broader application.
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): JCTA 1(2) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9547

Abstract

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 measurements such as Quantum Bit Error Rate (QBER), Polarization Error Rate (PER), Quantum Fidelity (QF), Eavesdropping Detection (ED), and Entanglement-based detection (EDB), as well as classical cryptographic analysis such as Bit Error Ratio (BER), Entropy, Histogram Analysis, and Normalized Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI). As a result, the proposed method obtained satisfactory results, especially perfect QF and BER, and EBD, which reached 0.999.
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): JCTA 1(3) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.9655

Abstract

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 duration as GTZAN, namely 30 seconds. Preprocessing operations by removing silent parts and stretching are also performed at the preprocessing stage to obtain normalized input. Based on the test results, the performance of the proposed method is able to produce accuracy on testing data of 93.10% for GTZAN and 93.69% for the ISMIR2004 dataset.
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): JCTA 1(3) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.9929

Abstract

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 high-complexity environments. DQN simulations were conducted across four scenarios, allowing the agent to operate at speeds ranging from 60 to nearly 100 km/h. The simulations featured a variable number of vehicles/obstacles, ranging from 20 to 80, and each simulation had a duration of 40 seconds within the Highway-Env simulator. Based on the test results, the DQN method exhibited excellent performance, achieving the highest reward value in the first scenario, 35.6117 out of a maximum of 40, and a success rate of 90.075%.
Dataset Analysis and Feature Characteristics to Predict Rice Production based on eXtreme Gradient Boosting Wijayanti, Ella Budi; Setiadi, De Rosal Ignatius Moses; Setyoko, Bimo Haryo
Journal of Computing Theories and Applications Vol. 1 No. 3 (2024): JCTA 1(3) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10057

Abstract

Rice plays a vital role as the main food source for almost half of the global population, contributing more than 21% of the total calories humans need. Production predictions are important for determining import-export policies. This research proposes the XGBoost method to predict rice harvests globally using FAO and World Bank datasets. Feature analysis, removal of duplicate data, and parameter tuning were carried out to support the performance of the XGBoost method. The results showed excellent performance based on which reached 0.99. Evaluation of model performance using metrics such as MSE, and MAE measured by k-fold validation show that XGBoost has a high ability to predict crop yields accurately compared to other regression methods such as Random Forest (RF), Gradient Boost (GB), Bagging Regressor (BR) and K-Nearest Neighbor (KNN). Apart from that, an ablation study was also carried out by comparing the performance of each model with various features and state-of-the-art. The results prove the superiority of the proposed XGBoost method. Where results are consistent, and performance is better, this model can effectively support agricultural sustainability, especially rice production.
Co-Authors Abdul Syukur Abdussalam Abdussalam Abdussalam Abdussalam Abdussalam Abere, Reuben Akporube Abugor Okpako, Ejaita Achmad Nuruddin Safriandono Adhitya Nugraha Adigwe, Wilfred Adimabua Ojugo, Arnold Adityawan, Harish Trio Afotanwo, Anderson Afridiansyah, Rahmanda Aghaunor, Tabitha Chukwudi Aghware, Fidelis Obukohwo Agustina, Feri Ahmad Salafuddin Ajib Susanto Akazue, Maureen Ifeanyi Akbar Aji Nugroho Akbar, Ismail Akhmad Dahlan Ako, Rita Erhovwo Akrom, Muhamad Alvin Faiz Kurniawan Amir Musthofa Anak Agung Gede Sugianthara Andik Setyono Antonio Ciputra Antonius Erick Handoyo Aprilah, Thania Araaf, Mamet Adil Arya Kusuma Binitie, Amaka Patience Budi Widjajanto Budi, Setyo Cahaya Jatmoko Chaerul Umam Chaerul Umam Christian, Henry Christy Atika Sari Chukwudi Aghaunor, Tabitha Cinantya Paramita Ciputra, Antonio Danu Hartanto Daurat Sinaga Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Devi Purnamasari Dhendra Marutho Dian Kristiawan Nugroho Dumebi Okpor, Margaret Dutta, Pushan Kumar Dwi Puji Prabowo Dwi, Bernadetta Sri Endah Eboka, Andrew Okonji Edy Winarno Egia Rosi Subhiyakto Ejeh, Patrick Ogholuwarami Eko Hari Rachmanto Eko Hari Rachmawanto Eko Septyasari Elkaf Rahmawan Pramudya Eluemnor Anazia, Kizito Emordi, Frances Uche Enadona Oweimeito, Amanda Erhovwo Ako, Rita Erlin Dolphina Erna Zuni Astuti Etika Kartikadarma Farah Zakiyah Rahmanti Farooq, Omar Farroq, Omar Ferda Ernawan Firnando, Fadel Muhamad Fittria Shofrotun Ni'mah Florentina Esti Nilawati Florentina Esti Nilawati Gan, Hong-Seng Geteloma, Victor Ochuko Ghosal, Sudipta Kr Giovani Ardiansyah Gomiasti, Fita Sheila Hanny Haryanto Henry Christian Herowati, Wise Heru Agus Santoso Ibnu Gemaputra Ramadhan Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibor, Ayei Egu Ihya Ulumuddin, Dimas Irawan Imanuel Harkespan Imanulloh, Satrio Bagus Indra Gamayanto Islam, Hussain Md Mehedul Isworo Nugroho Iwan Setiawan Wibisono Jutono Gondohanindijo, Jutono Kartikadarma , Etika Kusuma, Edi Jaya Kusuma, Muh Galuh Surya Putra L. Budi Handoko Lalang Erawan M. Dalvin Marno Putra Md Kamruzzaman Sarker Md Kamruzzaman Sarker Minh T. Nguyen, Minh T. Mohammad Rizal, Mohammad Muchamad Akbar Nurul Adzan Muhamada, Keny Mulyono, Ibnu Utomo Wahyu Musfiqur Rahman Sazal Muslikh, Ahmad Rofiqul Mustofa, Fachrul Nartriani, Yulian Dwi Noor Ageng Setiyanto Noor Ageng Setiyanto, Noor Ageng Nova Rijati Nugroho, Sandy Ochuko Geteloma, Victor Octara Pribadi Odiakaose , Christopher Chukwufunaya Odiakaose, Chris Chukwufunaya Odiakaose, Christopher Chukwufunaya Ojugo, Arnold Adimabua Okpor, Margaret Dumebi Omar Farooq Omoruwou, Felix Pradana, Akbar Ganang Prajanto Wahyu Adi, Prajanto Wahyu Pratama, Nizar Rafi Purnamasari, Devi Rachman, Rahadian Kristiyanto Ramadhan, Pramudia Ricardus Anggi Pramunendar Robet Robet Ruri Suko Basuki Sahu, Aditya Kumar Santoso, Siane Sarker, Md Kamruzzaman Sasono Wibowo Setiawan, Marcell Adi Setyoko, Bimo Haryo Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Stefanus Santosa Sudibyo, Usman Sukamto, Titien S Sunarjo, Macellino Setyaji Suyud Widiono Syahputra, Zulfikar Adi Syahroni Wahyu Iriananda, Syahroni Wahyu T Sutojo T. Sutojo Tan Samuel Permana Tan Samuel Permana Titien S sukamto Trisnapradika, Gustina Alfa Ugbotu, Eferhire Valentine Valentine Ugbotu, Eferhire Warto - Wellia Shinta Sari Wellia Shinta Sari Wibowo, Mochammad Abdurrochman Ari Wijaya, Nantalira Niar Wijayanti, Ella Budi Yoro, Rume Elizabeth Yusianto Rindra Zuama, Leygian Reyhan