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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 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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Optimasi Keamanan Watermarking pada Daubechies Transform Berbasis Arnold Cat Map Abdussalam Abdussalam; Eko Hari Rachmawanto; Noor Ageng Setiyanto; De Rosal Ignatius Moses Setiadi; Christy Atika Sari
Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 1 (2019): JPIT, Januari 2019
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v4i1.911

Abstract

Digital image security using Transform Domain algorithms such as Discrete Wavelet Transform (DWT) has been widely used. To improve the security of the DWT algorithm needs to randomize the pixel coefficient, namely Arnold Cat Map (ACM). Computing ACM as one of the chaos functions is known to be fast and fits with Transform Domain. DWT has been implemented in the Daubechies filter which is the development of the Haar filer. In this paper, we proposed the message insertion model using a combination of DWT and ACM on a 512x512 piskel grayscale image and a 64x64 pixel message on the LL subband. The experiments were performed on 2 different images to determine the ability produced by the combined algroithm. The ability test for message insertion process is done through Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and comparation between original image histogram and image insertion histogram. While in the process of message extraction, algorithmic capability test is done by calculating Normalized Cross Correlation (NCC) and its correlation. The highest MSE result is 2.9502 and the highest PSNR is 43.4323 dB, while the NCC value is 237.3584 with correlation 0.7181.
Klasifikasi Jeruk Nipis Terhadap Tingkat Kematangan Buah Berdasarkan Fitur Warna Menggunakan K-Nearest Neighbor Cinantya Paramita; Eko Hari Rachmawanto; Christy Atika Sari; De Rosal Ignatius Moses Setiadi
Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 1 (2019): JPIT, Januari 2019
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v4i1.1267

Abstract

In the process of classification of lime fruit previously done manually using the human eye is a very difficult thing to do. This is proven by being inconsistent and subjective, causing a low level of accuracy. Sometimes there are also differences of opinion from the human eye to one another. Therefore, to increase the level of accuracy and reduce the subjectivity of the human eye, this study proposes the K-Nearest Neighbor algorithm to classify the maturity level of lime based on the skin color of the lime. In this study, the K values used were 1, 3, 5, 7 and 9 to test the search for Euclidean distance and cityblock distance distances on images with pixel sizes of 512x512, 256x256 and 128x128. In the prerosesing stage, the extraction feature process uses mean RGB. The research that has been done proves that with Euclidean distance distance k = 3 and k = 7 has a percentage value of 92% and the cityblock distance distance k = 1 and k = 3 has a percentage value of 88%. Based on the level of accuracy possessed, the color feature k = 3 shows the best k value in the classification of the maturity level of the lime fruit.
Deteksi Tumor Otak Dengan Metode Convolutional Neural Network Dwi, Bernadetta Sri Endah; Setiadi, De Rosal Ignatius Moses
Jurnal Eksplora Informatika Vol 13 No 2 (2024): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v13i2.971

Abstract

Tumor otak merupakan salah satu penyakit mematikan di dunia. Menurut data Global Cancer Observatory, kasus tumor otak di Indonesia pada tahun 2021 mencapai 5.964 kasus serta tingkat kematian berada pada posisi 12 dengan 5298 kasus. Diagnosa cepat dan lebih dini tentu akan mampu menekan tingkat kematian tumor otak. Penelitian ini mengusulkan metode Convolutional Neural Network (CNN) untuk deteksi otak berdasarkan pencitraan medis. Model CNN didesain secara khusus terdiri dari 14 layer. Berdasarkan hasil pengujian model CNN yang dihasilkan memiliki akurasi tinggi yaitu 99%. Selain itu berdasarkan hasil komparasi dengan dataset yang sama, model yang diusulkan 5% lebih unggul dari metode sebelumnya yang menggunakan pre-trained model MobileNetV2.
Improved Javanese script recognition using custom model of convolution neural network Susanto, Ajib; Mulyono, Ibnu Utomo Wahyu; Sari, Christy Atika; Rachmawanto, Eko Hari; Setiadi, De Rosal Ignatius Moses; Sarker, Md Kamruzzaman
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6629-6636

Abstract

Handwriting recognition in Javanese script is not widely developed with deep learning (DL). Previous DL and machine learning (ML) research is generally limited to basic characters (Carakan) only. This study proposes a deep learning model using a custom-built convolutional neural network to improve recognition accuracy performance and reduce computational costs. The main features of handwritten objects are textures, edges, lines, and shapes, so convolution layers are not designed in large numbers. This research maximizes optimization of other layers such as pooling, activation function, fully connected layer, optimizer, and parameter settings such as dropout and learning rate. There are eleven main layers used in the proposed custom convolutional neural network (CNN) model, namely four convolution layers+activation function, four pooling layers, two fully connected layers, and a softmax classifier. Based on the test results on the Javanese script handwritten image dataset with 120 classes consisting of 20 basic character classes and 100 compound character classes, the resulting accuracy is 97.29%.
Enhanced multi-lingual Twitter sentiment analysis using hyperparameter tuning k-nearest neighbors Nugroho, Kristiawan; Winarno, Edy; Setiadi, De Rosal Ignatius Moses; Farooq, Omar
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7265

Abstract

Social media is a medium that is often used by someone to express themselves. These various problems on social media have encouraged research in sentiment analysis to become one of the most popular research fields. Various methods are used in sentiment analysis research, ranging from classic machine learning (ML) to deep learning. Researchers nowadays often use deep learning methods in sentiment analysis research because they have advantages in processing large amounts of data and providing high accuracy. However, deep learning also has limitations on the longer computational side due to the complexity of its network architecture. K-nearest neighbor (KNN) is a robust ML method but does not yet provide high-accuracy results in multi-lingual sentiment analysis research, so a hyperparameter tuning KNN approach is proposed. The results showed that using the proposed method, the accuracy level improved to 98.37%, and the classification error (CE) improved to 1.63%. The model performed better than other ML and even deep learning methods. The results of this study indicate that KNN using hyperparameter tuning is a method that contributes to the sentiment analysis classification model using the Twitter dataset.
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.
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
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.
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
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 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 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 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