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All Journal Jurnal Masyarakat Informatika Jurnal Simetris Bulletin of Electrical Engineering and Informatics Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Soft Computing Exploration Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Sinaga, Daurat
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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 Languange, Linguistic, Communication & Media Library & Information Science 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.
Improved Chaotic Image Encryption on Grayscale Colorspace Using Elliptic Curves and 3D Lorenz System Sinaga, Daurat; Jatmoko, Cahaya; Astuti, Erna Zuni; Rachmawanto, Eko Hari; Abdussalam, Abdussalam; Pramudya, Elkaf Rahmawan; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Doheir, Mohamed
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2251

Abstract

Digital data, especially visual content, faces significant security challenges due to its susceptibility to eavesdropping, manipulation, and theft in the modern digital landscape. One effective solution to address these issues is the use of encryption techniques, such as image encryption algorithms, that ensure the confidentiality, integrity, and authenticity of digital visual content. This study addresses these concerns by introducing an advanced image encryption method that combines Elliptic Curve Cryptography (ECC) with the 3D Lorenz chaotic system to enhance both security and efficiency. The method employs pixel permutation, ECC-based encryption, and diffusion using pseudo-random numbers generated by the Lorenz 3D system. The results show superior performance, with an MSE of 3032 and a PSNR of 8.87 dB, as well as UACI and NPCR values of 33.34% and 99.64%, respectively, indicating strong resilience to pixel intensity changes. During testing, the approach demonstrated robustness, allowing only the correct key to decrypt images accurately, while incorrect or modified keys led to distorted outputs, ensuring encryption reliability. Future work could explore extending the method to color images, optimizing processing for larger datasets, and incorporating additional chaotic systems to further fortify encryption strength.
Optimized Visualization of Digital Image Steganography using Least Significant Bits and AES for Secret Key Encryption Jatmoko, Cahaya; Sinaga, Daurat; Lestiawan, Heru; Astuti, Erna Zuni; Sari, Christy Atika; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Yaacob, Noorayisahbe Mohd
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2252

Abstract

Data hiding is a technique used to embed secret information into a cover medium, such as an image, audio, or video, with minimal distortion, ensuring that the hidden data remains imperceptible to an observer. The key challenge lies in embedding secret information securely while maintaining the original quality of the host medium. In image-based data hiding, this often means ensuring the hidden data cannot be easily detected or extracted while still preserving the visual integrity of the host image. To overcome this, we propose a combination of AES (Advanced Encryption Standard) encryption and Least Significant Bit (LSB) steganography. AES encryption is used to protect the secret images, while the LSB technique is applied to embed the encrypted images into the host images, ensuring secure data transfer. The dataset includes grayscale 256x256 images, specifically "aerial.jpg," "airplane.jpg," and "boat.jpg" as host images, and "Secret1," "Secret2," and "Secret3" as the encrypted secret images. Evaluation metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Unified Average Changing Intensity (UACI), and Number of Pixels Changed Rate (NPCR) were used to assess both the image quality and security of the stego images. The results showed low MSE (0.0012 to 0.0013), high PSNR (58 dB), and consistent UACI and NPCR values, confirming both the preservation of image quality and the effectiveness of encryption for securing the secret data.
KLASIFIKASI CITRA BATIK SUMATERA MENGGUNAKAN NAÏVE BAYES BERBASIS FITUR EKSTRAKSI GLCM Sinaga, Daurat; Jatmoko, Cahaya
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 8, No 01 (2024): SEMNAS RISTEK 2024
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v8i01.7148

Abstract

Salah satu budaya Indonesia yang masih tetap bertahan hingga sekarang adalah batik. Hingga saat ini, ragam motif batik terus berkembang di nusantara seperti batik kawung, batik parang, batik sidomukti, dll. Banyaknya pola batik di Indonesia membuat identifikasi menjadi sulit dan membutuhkan suatu sistem yang dapat mengklasifikasikan motif batik. Pada penelitian ini akan menggunakan sistem dengan mengusulkan metode ekstraksi fitur tekstur menggunakan metode Gray Level Co-Occurrence Matix (GLCM) serta klasifikasi jenis motif batik dengan Naïve Bayes. Terdapat 15 motif Batik Sumatera yang digunakan dalam penelitian ini, di mana jenis batik ini meliputi daerah asal yaitu Aceh, Sumatra Utara, Sumatra Barat, Riau, Jambi, Bengkulu, Sumatera Selatan, Kepulauan Bangka Belitung dengan 100 dataset untuk setiap motifnya. Penelitian ini menggunakan 1500 dataset citra batik, dengan split data 70:30 sehingga 1050 citra digunakan sebagai Data Training dan 450 citra digunakan sebagai data testing. Parameter GLCM yang dipakai yaitu contrast, correlation, energy, entropi, dan homogeniti. Dari hasil percobaan diketahui bahwa Naïve Bayes menghasilkan akurasi hingga 96,66%.
Pelatihan Diklat Pemanfaatan Aplikasi Online Menggunakan Cek Plagiarisme Dengan Turnitin Untuk Guru Dan Dosen Pada Perkumpulan Profesi Multimedia Dan Teknologi Informasi (PPMULTINDO) Rakasiwi, Sindhu; Marjuni, Aris; Rijati, Nova; Himawan, Heribertus; Jatmoko , Cahaya; Sinaga, Daurat
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 2 (2024): Juli : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/community.v4i2.534

Abstract

Conveying appropriate information to be understood quickly and accurately is very important in various areas of life, both academic and non-academic. A teacher or lecturer is a teacher whose job is to educate and provide instruction to students or students. Data visualization is one way that can be used to present data. The advantage of this method is the availability of statistical graphics which can enrich the display of information so that the results are more interactive for the audience. Google Data Studio is an application launched by Google that can be used to convert raw data into interesting and strategic information for users. That way, the information becomes more appropriate to understand quickly and accurately.
Multi-Layer Convolutional Neural Networks for Batik Image Classification Sinaga, Daurat; Jatmoko, Cahaya; Suprayogi, Suprayogi; Hedriyanto, Novi
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3309

Abstract

Purpose: The purpose of this study is to enhance the classification of batik motifs through the implementation of a novel approach utilizing Multi-Layer Convolutional Neural Networks (CNN). Batik, a traditional Indonesian textile art form, boasts intricate motifs reflecting rich cultural heritage. However, the diverse designs often pose challenges in accurate classification. Leveraging advancements in deep learning, this research proposes a methodological framework employing Multi-Layer CNN to improve classification accuracy. Methods: The methodology integrates Multi-Layer CNN architecture with an image dataset comprising various batik motifs, meticulously collected and preprocessed for uniformity. The CNN architecture incorporates convolutional layers of different sizes (3x3, 5x5, and 7x7) to extract unique features from batik images. Training options, including the Adam optimizer and validation frequency, are optimized based on parameters to enhance model efficiency and effectiveness. Result: Results from the experimentation demonstrate significant improvements in classification accuracy, with an overall accuracy rate of 90.88%. Notably, precision and recall scores for individual batik motifs, such as Motif Cual Bangka and Motif Rumah Adat Belitung, reached remarkable levels, showcasing the efficacy of the proposed approach. Novelty: This study contributes novelty through the integration of Multi-Layer CNN in batik classification, offering a robust and efficient method for identifying intricate batik motifs. Additionally, the research presents a pioneering application of deep learning techniques in preserving and promoting traditional cultural heritage, thereby bridging the gap between tradition and modern technology.
PSNR and SSIM Performance Analysis of Schur Decomposition for Imperceptible Steganography Susanto, Ajib; Sinaga, Daurat; Mulyono, Ibnu Utomo Wahyu
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.9561

Abstract

Purpose: This research examines how well Schur decomposition-based steganography can hide data in digital images without being noticed, while also keeping the image quality high and keeping the hidden information safe. Methods: The study starts by choosing regular test images (Lena, Plane, Peppers, Cameraman, Baboon) to use for hiding messages in. The Schur decomposition is used to hide information within images in a subtle way. To test how well the new method works, we added Gaussian noise and Salt & Pepper noise after embedding. The quality of the image is determined by looking at the Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. Result: The research shows that Schur decomposition results in very good SSIM values (greater than 0.92) and high PSNR scores (as high as 90.27 dB) for various image sizes (64x64, 128x128, 256x256). This means that the quality of the images is not greatly reduced even after steganography is applied. Novelty: This research introduces a unique use of Schur decomposition for hiding data in images without affecting their quality. The study highlights how this method can securely hide information in digital media, which could be really useful for improving steganography techniques in the future. Future studies should concentrate on making improvements to Schur decomposition-based steganography, especially for bigger images. One possibility is to create adaptive methods that can change how images are hidden based on their content. This could make it harder for others to detect and analyze hidden information in the images.
Optimization of Heart Failure Classification on Imbalanced Data Using a Supervised Learning Approach Based on Logistic Regression, Random Forest, and K-Nearest Neighbor: Optimalisasi Klasifikasi Gagal Jantung pada Data Imbalanced Menggunakan Pendekatan Supervised Learning Berbasis Regresi Logistik, Random Forest, dan K-Nearest Neighbor agustina, feri; Irawan, Candra; Erawan, Lalang; Suprayogi; Award Widya Laksana, Deddy; Jatmoko, Cahaya; Sinaga, Daurat; Lestiawan, Heru
Jurnal Informatika Polinema Vol. 12 No. 1 (2025): Vol. 12 No. 1 (2025)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i1.9071

Abstract

Heart failure remains one of the leading causes of mortality worldwide, posing significant challenges for early diagnosis and patient management. One of the major obstacles in developing predictive models for heart failure is the class imbalance problem, where the number of surviving patients far exceeds those who experience death events. This imbalance often leads machine learning algorithms to bias toward the majority class, reducing sensitivity to critical minority cases. To address this issue, this study applies the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset and improve model performance. Three supervised learning algorithms, namely Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbor (KNN), were implemented and compared on the UCI Heart Failure Clinical Records dataset containing 299 patient samples with 13 clinical attributes. Experimental results show that the Random Forest model achieved the highest performance with 90% accuracy, precision, recall, and F1-score, outperforming both LR and KNN. The findings demonstrate that combining data balancing with ensemble learning effectively enhances prediction accuracy and sensitivity toward minority classes. The main contribution of this research lies in optimizing supervised models for medical data with skewed class distributions, providing a more reliable and interpretable approach for early heart failure detection. Future research may extend this work by integrating advanced ensemble or hybrid deep learning models and expanding the dataset for multi-institutional validation
Co-Authors Abdussalam Abdussalam, Abdussalam Agustina, Feri Ajib Susanto Alzami, Farrikh Award Widya Laksana, Deddy Cahaya Jatmoko Candra Irawan Chaerul Umam Christy Atika Sari De Rosal Ignatius Moses Setiadi Doheir, Mohamed Dwi Puji Prabowo, Dwi Puji Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erna Zuni Astuti Guruh Fajar Shidik Hadi, Heru Pramono Hedriyanto, Novi Heribertus Himawan Heru Lestiawan Lalang Erawan Marjuni, Aris Megantara, Rama Aria Mulyono, Ibnu Utomo Wahyu Nova Rijati Pergiwati, Dewi Pulung Nurtantio Andono Ricardus Anggi Pramunendar Saifullah, Zidan Sindhu Rakasiwi suprayogi Suprayogi Suprayogi Syamsiar, Syamsiar Yaacob, Noorayisahbe Mohd Yaacob, Noorayisahbe Mohd. Zainal Arifin Hasibuan