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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik Jurnal Ilmu Komputer dan Informasi Jurnal Masyarakat Informatika 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 JUTI: Jurnal Ilmiah Teknologi Informasi Prosiding SNATIF Journal of ICT Research and Applications Teknika: Jurnal Sains dan Teknologi Jurnal Informatika dan Teknik Elektro Terapan 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 Jurnal Teknologi dan Sistem Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika SISFOTENIKA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer English Language and Literature International Conference (ELLiC) Proceedings Infotekmesin Jurnal Mnemonic Abdimasku : Jurnal Pengabdian Masyarakat SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Journal of Soft Computing Exploration Advance Sustainable Science, Engineering and Technology (ASSET) Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Scientific Journal of Informatics LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Advance Sustainable Science, Engineering and Technology (ASSET) INOVTEK Polbeng - Seri Informatika
Christy Atika Sari
Teknik Informatika S1, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Social Sciences Other

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Real-time detection of indonesian sign language (ISL) gestures based on long short-term memory Sari, Christy Atika; Rachmawanto, Eko Hari; Saifullah, Zidan; Jatmoko, Cahaya; Sinaga, Daurat
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.452

Abstract

eaf people often encounter communication challenges, and sign language serves as a crucial tool for those who cannot speak. In Indonesia, Indonesian Sign Language (ISL) or Sistem Isyarat Bahasa Indonesia (SIBI) is officially recognized by the government and is taught in Special Schools (Sekolah Luar Biasa - SLB). The sign language dictionary comprises 3483 words, facilitating communication and participation in daily life for the deaf community. This research aims to convert ISL gestures within SIBI into understandable text, employing the Long-Short-Term Memory (LSTM) method as the primary approach. The study conducted experiments with two models: Model 1, using a smaller dataset, and Model 2, employing a larger dataset and implementing the k-fold method. The results indicate that Model 2 with k-fold accuracy achieved an accuracy of 98%, while Model 1 reached an accuracy of 85%. Nevertheless, challenges persist in these models, particularly in detecting words with similar gestures, such as’maaf’ (sorry) and 'cinta' (love), which may still be misidentified. Despite these challenges, this research contributes positively to the development of assistive technology for the deaf community, enabling more effective communication through sign language.
Klasifikasi Citra Mengkudu Berdasarkan Perhitungan Jarak Piksel pada Algoritma K-Nearest Neighbour Candra Irawan; Eko Hari Rachmawanto; Christy Atika Sari; Raisul Umah Nur
Infotekmesin Vol 14 No 2 (2023): Infotekmesin: Juli, 2023
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v14i2.1827

Abstract

Noni fruit is included in exported food commodities in Indonesia. The size of noni fruit, based on human vision, generally has varied shapes with distinctive textures and various patterns, so that the process of filtering fruit based on color and shape can be done in large quantities. In this study, K-Nearest Neighbor (KNN) has been implemented as a classification algorithm because it has advantages in classifying images and is resistant to noise. Noni imagery is a personal image taken from a noni garden in the morning and undergoes a background subtraction process. The imagery quality improvement technique uses the Hue Saturation Value (HSV) color feature and the Gray Level Co-Occurrence Matrix (GLCM) characteristic feature. KNN accuracy without features is lower than using HSV and GLCM features. From the experimental results, the highest accuracy was obtained using HSV-GLCM at K is 1 and d is 1, namely 95%, while the lowest accuracy was 55% using KNN only at K is 5 and d is 8.
Javanese Character Recognition Based on K-Nearest Neighbor and Linear Binary Pattern Features Susanto, Ajib; Mulyono, Ibnu Utomo Wahyu; Sari, Christy Atika; Rachmawanto, Eko Hari; Ali, Rabei Raad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1491

Abstract

Javanese script (Hanacaraka) is one of the cultures owned by Indonesia. Javanese script is found in temples, inscriptions, cultural and prehistoric sites, ancient Javanese manuscripts, Gulden series banknotes, street signage, and palace documents. Javanese script has a form with an article, and the use of reading above the script is a factor that affects the character detection process. Punctuation marks, clothing, Swara script, vowels, and consonants are parts of the script that are often found in Javanetest scripts. Preserving Javanese script in the digital era, of course, must use technology that can support the digitization of Javanese script through the script detection process. The concept of script image is the image of Javanese script in ancient manuscripts. The process of character detection using certain techniques can be carried out to extract characters so that they can be read. Detection of Javanese characters can be found by finding a testing image. Here, we had been used 10 words images consisting of 3 to 5 syllables with the vowel aiu. Dataset process by Linear Binary Pattern (LBP) feature extraction, which is used to characterize images and describe image textures locally. LBP has been used in r=4 and preprocessing is also done by thresholding with d=0.3. This process can be done using the K-Nearest Neighbor algorithm. In 10 datasets of Javanese script words, an average accuracy value of 90.5% was obtained. The accuracy value of 100% is the highest and 50% is the lowest.
Visual Analysis Based on CMY and RGB Image Cryptography Using Vigenere and Beaufort Cipher Atika Sari, Christy; Utomo, Danang Wahyu; Doheir, Mohamed A S
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 2, May 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i2.1664

Abstract

The achievement of visual aspects and image security often cannot meet visibility standards, for example the acquisition of PSNR and UACI NPCR values. To increase security, this research has implemented a combination of the Vigenere cipher and Beaufort and the use of Fibonacci as a randomizer. The combination of the Vigenere Cipher and Beaufort Cipher substitution algorithms with the Fibonacci technique can be applied to encrypt color images in RGB and CMY, with a size of 256x256 pixels and in .bmp format. The Fibonacci cut-off value used in this study is 10000. The highest entropy value of the cipher image peppers.bmp is 7,991. The lowest PSNR cipher image value is accordion.bmp where for RGB it is 5,439 dB and for CMY it is 5,403 dB. accordion.bmp's highest UACI value is 44.018% for RGB and 44.312% for CMY. The NPCR value in the airplane.bmp image has the highest value in RGB of 99.792% and for CMY the highest value is in splash.bmp with a value of 99.798%. Evaluation of the decryption results shows that the decryption process can run perfectly as indicated by the values of MSE=0, PSNR=inf, UACI and NPCR=0%. Therefore, encrypt and decrypt was proven that the results obtained in the visual aspect are very good.
Testing Data Security Using a Vigenere Cipher Based on the QR Code Rachmawanto, Eko Hari; Gumelar, Rizky Syah; Nabila, Qotrunnada; Sari, Christy Atika; Ali, Rabei Raad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 4, November 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i4`.1734

Abstract

Data, especially personal data, is sensitive and if misused, it can become a source of threats and crimes for ourselves or for others. Therefore, data security is very important. Cryptography is a way to secure data that aims to safeguard the information that contained in data, so the information contained is not known by unauthorized parties. Vigenere Cipher is a cryptographic method used to hide data with steganography. In the process, the Vigenere cipher converts information called plain text into ciphertext or text that has been steganographed. In this research, process of encryption was carried out on the text based on the given key. The results of the text encryption were stored in the form of a QR-Code which can later be decrypted from the QR-Code using the key, so that the text contained in the QR-Code can be identified.
Discrete Cosine Transform and Singular Value Decomposition Based on Canny Edge Detection for Image Watermarking Astuti, Erna Zuni; Sari, Christy Atika; Rachmawanto, Eko Hari; Astuti, Yani Parti; Oktaridha, Harwinanda; Isinkaye, Folasade Olubusola
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 4, November 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i4`.1768

Abstract

The development of an increasingly sophisticated internet allows for the distribution of digital images that can be done easily. However, with the development of increasingly sophisticated internet networks, it becomes an opportunity for some irresponsible people to misuse digital images, such as taking copyrights, modification and duplicating digital images. Watermarking is an information embedding technique to show ownership descriptions that can be conveyed into text, video, audio, and digital images. There are 2 groups of watermarking based on their working domain, namely the spatial domain and the transformation domain. In this study, three domain transformation techniques were used, namely Singular Value Descomposition (SVD), Discrete Cosine Transform (DCT) and Canny Edge Detection Techniques. The proposed attacks are rotation, gaussian blurness, salt and pepper, histogram equalization, and cropping. The results of the experiment after inserting the watermark image were measured by the Peak Signal to Noise Ratio (PSNR). The results of the image robustness test were measured by the Correlation Coefficient (Corr) and Normalized Correlation (NC). The analysis and experimental results show that the results of image extraction are good with PSNR values from watermarked images above 50dB and Corr values reaching 0.95. The NC value obtained is also high, reaching 0.98. Some of the extracted images are of fairly good quality and are similar with the original image.
An optimum hyperparameters of restnet-50 for orchid classification based on convolutional neural network Alvian Ideastari, Nukat; Atika Sari, Christy; Faisal, Edi; Arifin, Zaenal; Danang Krismawan, Andi; Muslih, Muslih
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.297

Abstract

There are many types of orchids in Indonesia, such as Phalaenopsis Amabilis (Moon Orchid), Cattleya, etc. Because the shape and color of each orchid flower looks the same, a system is needed that can classify orchid flowers. In this research, we will use a system using a Convolutional Neural Network with ResNet50 architecture to classify orchid species. There are 4 types of orchids that will be used, namely Moon Orchids, xDoritaenopsis Orchids, Cattleya Orchids, and Coelogyne Pandurata Orchids (1000 datasets for each type). The aim of this research is to implement deep learning using the Convolutional Neural Network method combined with the ResNet50 architecture and identifying the types of orchid flowers and calculating accuracy when identifying orchid flower types. This research uses 4000 orchid image datasets, with a data split of 80:20 so that 800 images are used as training data and 200 as test data. ResNet50 uses a confusion matrix evaluation, namely Accuracy, Precision, Recall, Specificity and F1-score with epochs 10, 20, 30, 40. From the research that has been carried out, it produces the highest accuracy on Test Data with the 30th epoch, reaching 98.87%. and the lowest accuracy on Test Data with the 10th epochs which produces an accuracy of 97.75%.
Breast tumor classification using adam and optuna model optimization based on CNN architecture Sari, Christy Atika; Rachmawanto, Eko Hari; Daniati, Erna; Setiawan, Fachruddin Ari; Hyperastuty, Agoes Santika; Mintorini, Ery
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.373

Abstract

Breast cancer presents a significant challenge due to its complexity and the urgency of the intervention required to prevent metastasis and potential fatality. This article highlights the innovative application of Convolutional Neural Networks (CNN) in breast tumor classification, marking substantial progress in the field. The key to this advancement is the collaboration among medical professionals, scientists, and artificial intelligence experts, which maximizes the potential of technology. The research involved three phases of training with varying proportions of training data. The first training phase achieved the highest accuracy rate of 99.72%, with an average accuracy of 99.05% in all three phases. Metrics such as precision, recall, and F1 score were also highly satisfactory, underscoring the model's efficacy in accurately classifying breast tumors. Future research aims to develop more complex and precise predictive models by incorporating larger and more representative datasets. This progression promises to improve understanding, prevention, and management of breast cancer, offering hope for significant advances in 2024 and beyond.
Eye disease classification using deep learning convolutional neural networks Rachmawanto, Eko Hari; Sari, Christy Atika; Krismawan, Andi Danang; Erawan, Lalang; Sari, Wellia Shinta; Laksana, Deddy Award Widya; Adi, Sumarni; Yaacob, Noorayisahbe Mohd
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.493

Abstract

This study begins with the analysis of the growing challenge of accurately diagnosing eye diseases, which can lead to severe visual impairment if not identified early. To address this issue, we propose a solution using Deep Learning Convolutional Neural Networks (CNNs) enhanced by transfer learning techniques. The dataset utilized in this study comprises 4,217 images of eye diseases, categorized into four classes: Normal (1,074 images), Glaucoma (1,007 images), Cataract (1,038 images), and Diabetic Retinopathy (1,098 images). We implemented a CNN model using TensorFlow to effectively learn and classify these diseases. The evaluation results demonstrate a high accuracy of 95%, with precision and recall rates significantly varying across classes, particularly achieving 100% for Diabetic Retinopathy. These findings highlight the potential of CNNs to improve diagnostic accuracy in ophthalmology, facilitating timely interventions and enhancing patient outcomes. For future research, expanding the dataset to include a wider variety of ocular diseases and employing more sophisticated deep learning techniques could further enhance the model's performance. Integrating this model into clinical practice could significantly aid ophthalmologists in the early detection and management of eye diseases, ultimately improving patient care and reducing the burden of ocular disorders.
A Comparison of Convolutional Neural Network (CNN) and Transfer Learning MobileNetV2 Performance on Spices Images Classification Velarati, Khoirizqi; Sari, Christy Atika; Rachmawanto, Eko Hari
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8622

Abstract

This research was conducted to analyze the performance of the CNN algorithm without transfer learning in classifying spice images and compare it with the CNN algorithm using transfer learning on the MobileNetV2 architecture. This comparison aims to evaluate both methods' accuracy, efficiency, and overall performance and analyze the impact of transfer learning on classification results in the context of spices. The dataset consists of 1500 spice images divided into 10 classes, with each class of 150 images. In the first experiment, CNN without transfer learning resulted in 93% accuracy performance. For the second experiment using MobileNetV2, there was an increase in accuracy, reaching a value of 99% for all spice classes. The results of this study confirm that MobileNetV2 architecture significantly improves the accuracy and performance of spice classification compared to CNN without transfer learning, which can be recommended for spice image classification.
Co-Authors AA Sudharmawan, AA Abdussalam Abdussalam Abdussalam Abdussalam, Abdussalam Abiyyi, Ryandhika Bintang Ahmad Salafuddin Ajib Susanto Akbar, Fadhilah Aditya Akbar, Ilham Januar Alfany, Fauzan Maulana Ali, Rabei Raad Alifia Salwa Salsabila Alvian Ideastari, Nukat Alvin Faiz Kurniawan Anak Agung Gede Sugianthara Andi Danang Krismawan Anggraeny, Tiara Anidya Nur Latifa Annisa Sulistyaningsih Anny Yuniarti Antonius Erick Handoyo Arditya Prayogi Ardyani, Salma Shafira Fatya Arfian, Aldi Azmi Ariska, Ratih Aristides Bima Wintaka Aryanta, Muhammad Syifa Aryaputra, Firman Naufal Astuti, Yani Parti Auni, Amelia Gizzela Sheehan Azzahra, Fidela Bambang Sugiarto Briliantino Abhista Prabandanu Budi Harjo Cahaya Jatmoko Cahyo, Nur Ryan Dwi Candra Irawan Candra Irawan Castaka Agus Sugianto Chaerul Umam Chaerul Umam Cinantya Paramita D.R.I.M. Setiadi Danang Krismawan, Andi Danang Wahyu Utomo Danar Bayu Adi Saputra Danu Hartanto Daurat Sinaga Daurat Sinaga De Rosal Ignatius Moses Setiadi Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Didik Hermanto Doheir, Mohamed Doheir, Mohamed Doheir, Mohamed A S Dwi Puji Prabowo Edi Faisal Egia Rosi Subhiyakto Egia Rosi Subhiyakto Eko Hari Rachmanto Eko Hari Rachmawanto Eko Septyasari Elkaf Rahmawan Pramudya Ericsson Dhimas Niagara Erika Devi Udayanti Erlin Dolphina Erna Daniati Erna Zuni Astuti Ery Mintorini Etika Kartikadarma Farrel Athaillah Putra Feri Agustina Fidela Azzahra Florentina Esti Nilawati Florentina Esti Nilawati Florentina Esti Nilawati Folasade Olubusola Isinkaye Folasade Olubusola Isinkaye Giovani Ardiansyah Gumelar, Rizky Syah Guruh Fajar Shidik Gusta, Muhammad Bima Hadi, Heru Pramono Haqikal, Hafidz Hartono, Matthew Raymond Haryanto, Christanto Antonius Haryanto, Christanto Antonius Hasbi, Hanif Maulana Hayu Wikan Kinasih Heru Lestiawan Himawan, Reyshano Adhyarta Hussain Md Mehedul Islam Hyperastuty, Agoes Santika Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ifan Rizqa Ihya Ulumuddin, Dimas Irawan Ikhsanuddin, Rohmatulloh Muhamad Imam Prayogo Pujiono Inzaghi, Reza Bayu Ahmad Iqtait, Musab Isinkaye, Folasade Olubusola Islam, Hussain Md Mehedul Istiqomah, Annisa Ayu Ivan Stepheng Kamila, Izza Putri Kas Raygaputra Ilaga Krismawan, Andi Danang Kumala, Raffa Adhi Kurniawan, Nicholas Alfandhy Kusuma, Edi Jaya Kusuma, Mohammad Roni Kusumawati, Yupie L. Budi Handoko Laksana, Deddy Award Widya Lalang Erawan Liya Umaroh Liya Umaroh, Liya Lucky Arif Rahman Hakim Mabina, Ibnu Farid Maulana Malik Ibrahim Al-Ghiffary Md Kamruzzaman Sarker Md Kamruzzaman Sarker Md Kamruzzaman Sarker Mehta Pradnyatama Meitantya, Mutiara Dolla Mohamed Doheir Mohamed Doheir Mohammad Rizal, Mohammad Mohd Yaacob, Noorayisahbe Muchamad Akbar Nurul Adzan Muhammad Rikzam Kamal Mulyono, Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Wahyu Munis Zulhusni Musfiqur Rahman Sazal Muslih Muslih Nabila, Qotrunnada Naufal, Muhammad Khanif Neni Kurniawati Ningrum, Amanda Prawita Nisa, Yuha Aulia Noor Ageng Setiyanto Noor Ageng Setiyanto, Noor Ageng Noorayisahbe Mohd Yacoob Nova Rijati Nugroho, Widhi Bagus Nur Ryan Dwi Cahyo Oktaridha, Harwinanda Oktayaessofa, Eqania Ozagastra Caluella Prambudi parti astuti, yani Parti Astuti, Yani Parti Astuti, Yani Parti Astuti1, Yani Parti Astuti1, Yani Permana langgeng wicaksono ellwid putra Pradana, Luthfiyana Hamidah Sherly Pradana, Rizky Putra Praskatama, Vincentius Pratama, Zudha Pratiwi, Saniya Rahma Pulung Nurtantio Andono Purwanto Purwanto Puspa, Silfi Andriana Putri Mega Arum Wijayanti Rabei Raad Ali Rabei Raad Ali Rahmalan, Hidayah Raisul Umah Nur Ramadhan Rakhmat Sani Ratih Ariska Rizky Damara Ardy Robert Setyawan Sabilillah, Ferris Tita Saifullah, Zidan Salma Shafira Fatya Ardyani Salsabila, Alifia Salwa Sania, Wulida Rizki Santoso, Bagus Raffi Saputra, Danar Bayu Adi Sari, Wellia Shinta Sari Shinta Sarker, Md Kamruzzaman Sarker, Md. Kamruzzaman Setiarso, Ichwan Setiawan, Fachruddin Ari Shelomita, Viki Ari Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Sofyan, Ega Adiasa Solichul Huda, Solichul Sudibyo, Usman Sudibyo, Usman Sudibyo, Usman Sumarni Adi, Sumarni Suprayogi Suprayogi Suprayogi Suprayogi Sutrisno, Hendra Syabilla, Mutiara Tan Samuel Permana Tan Samuel Permana Tiara Anggraeny Titien Suhartini Sukamto Umah Nur, Raisul Umaroh, Liya Umaroh, Liya Utomo, Danang Wahyu Velarati, Khoirizqi Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Yaacob, Noorayisahbe Mohd Yani Parti Astuti Yupie Kusumawati Zaenal Arifin Zahra Ghina Syafira Zulhusni, Munis