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All Journal Dinamik Jurnal Masyarakat Informatika Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Abdimasku : Jurnal Pengabdian Masyarakat Journal of Soft Computing Exploration Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Community: Jurnal Pengabdian Pada Masyarakat Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Informatika Polinema (JIP) Transformasi Masyarakat : Jurnal Inovasi Sosial dan Pengabdian Scientific Journal of Informatics Kesejahteraan Bersama : Jurnal Pengabdian dan Keberlanjutan Masyarakat Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial Pelayanan Unggulan: Jurnal Pengabdian Masyarakat Terapan Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Cahaya Jatmoko
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Physics Public Health Social Sciences Other

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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.
KOMBINASI DCT DAN BEAUFORT CHIPER UNTUK PENINGKATAN KEAMANAN HAK CIPTA CITRA DIGITAL Setiadi, De Rosal Ignatius Moses; Jatmoko, Cahaya; Rachmawanto, Eko Hari; Sari, Christy Atika
JST (Jurnal Sains dan Teknologi) Vol. 7 No. 2 (2018)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v7i2.13795

Abstract

Informasi penting seperti hak cipta tentunya perlu diamankan, terlebih saat era digital saat ini yang semakin canggih. Pengamanan informasi dapat dilakukan dengan teknik kriptografi atau penyandian. Sedangkan untuk pengamanan hak cipta dapat dilakukan dengan teknik watermarking. Penelitian ini mengkombinasi teknik kriptografi dan watermarking. Sebelum watermark disisipkan watermark disandikan terlebih dahulu. Metode watermarking yang diusulkan adalah DCT dan metode kriptografi yang diusulkan adalah Beaufort cipher. DCT dipilih karena merupakan transformasi domain yang tahan terhadap macam-macam manipulasi, cukup ringan dalam kalkulasi dan menghasilkan watermarking yang impercept. Sedangkan Beaufort cipher merupakan algoritma yang sederhana tapi sangat aman untuk pengamanan data. Alat ukur yang digunakan  dalam eksperimen adalah SSIM, CC dan analisis histogram. Berdasarkan pengukuran terhadap hasil eksperimen dari metode yang diusulkan didapatkan hasil watermarking yang tahan terhadap serangan, impercept, dan aman.
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%.
PREDIKSI PENYAKIT MATA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Jatmoko, Cahaya; Lestiawan, Heru
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.v8i01.7129

Abstract

Penyakit mata merupakan sebuah penyakit yang sangat berbahaya dan memiliki dampak yang dapat menghambat aktivitas kita sebagai manusia. Oleh karena itu, kita perlu melakukan proses identifikasi dan diagnosis terlebih dahulu untuk dapat mengetahui gejala yang terjadi pada penyakit mata. Pada penelitian ini, akan dilakukan proses klasifikasi penyakit mata dengan menggunakan metode CNN. Dataset yang digunakan pada penelitian ini yaitu merupakan dataset penyakit mata yang memiliki total data citra sebanyak 4217 citra dengan 4 kelas yaitu cataract, diabetic retinopathy, glaucoma dan normal. Pada penelitian ini, akan menggunakan metode Convolutional Neural Network untuk melakukan proses klasifikasi. Hasil yang didapatkan steelah dilakukannya pengujian pada penelitian ini yaitu mendapatkan akurasi pengujian yaitu sebesar 75.27%.
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.
Pelatihan Pemanfaatan Google Sites Untuk Pembuatan Media Pembelajaran Berbasis Website Untuk Guru Dan Dosen Pada Perkumpulanprofesi Multimedia Dan Teknologi Informasi (PPMULTINDO) Jatmoko, Cahaya; Rakasiwi, Sindhu; Widya Laksana, Deddi Award; Erawan, Lalang; Rizqa, Ifan; Astuti, Erna Zuni
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.v4i2.535

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 Sites is a service owned by the Google company that can be used for e-learning. 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.
Pelatihan Desain Layout Buku Monograf dengan Canva untuk Guru dan Dosen pada PPMULTINDO Cahaya Jatmoko; Sindhu Rakasiwi; Feri Agustina; Daurat Sinaga; Heru Lestiawan
Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial Vol. 2 No. 4 (2025): November : Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/karya.v2i4.2362

Abstract

In the midst of the demand to actively publish scientific papers, the ability to design is a significant added value for teachers and lecturers. This report outlines a monograph book layout design training with Canva held for academics at PPMULTINDO. The main purpose of this activity is to provide practical skills so that participants can independently produce professional book layouts. This training uses an interactive workshop method, where participants are guided from the introduction of Canva's features, the application of design principles, to the practice of preparing layout chapters by chapter. As a result, participants demonstrated a significant improvement in their ability to operate Canva for publication design needs. They are able to produce a structured, consistent, and visually appealing layout. Thus, this training has succeeded in becoming a practical solution for academics to efficiently improve the visual quality of their monograph books
Pelatihan Membangun Media Evaluasi Pembelajaran Menggunakan Quizziz pada Guru dan Dosen Perkumpulan Profesi Multimedia dan Teknologi Informasi (PPMULTINDO) Sindhu Rakasiwi; Cahaya Jatmoko; Candra Irawan; Lalang Erawan; Suprayogi Suprayogi; Deddy Award Widya Laksana
Pelayanan Unggulan : Jurnal Pengabdian Masyarakat Terapan Vol. 2 No. 4 (2025): November: Pelayanan Unggulan : Jurnal Pengabdian Masyarakat Terapan
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/unggulan.v2i4.2365

Abstract

This community service activity aims to provide insight and training to teachers and lecturers who are members of the Multimedia and Information Technology Professional Association (PPMULTINDO) regarding the use of the Quizizz application as a learning evaluation medium. The background of this activity is that there is a challenge for educators to continue to innovate in the digital era, but there are still obstacles in the form of a lack of introduction and insight into Quizizz, as well as the assumption that the development of technology-based evaluation media is complicated. This training was carried out online through the Zoom application with tutorial, guidance, and consultation methods. The results achieved are that the trainees gain basic understanding and skills in using Quizizz to support a more effective and interactive learning process. This activity is expected to motivate teachers and lecturers to develop technology-based evaluation media and contribute to the advancement of education in the future.
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 Agus Setiawan Agustina, Feri Akrom, Ahmad Alfany, Fauzan Maulana Alvin Faiz Kurniawan Award Widya Laksana, Deddy Candra Irawan Candra Irawan Christy Atika Sari Danu Hartanto Daurat Sinaga Daurat Sinaga De Rosal Ignatius Moses Setiadi Deddy Award Widya Laksana Doheir, Mohamed Edi Sugiarto Edi Sugiarto Edi Sugiarto Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erna Zuni Astuti Feri Agustina Feri Agustina Florentina Esti Nilawati Guruh Fajar Shidik Hadi, Heru Pramono Hedriyanto, Novi Heribertus Himawan Heru Lestiawan Heru Lestiawan Ifan Rizqa L. Budi Handoko Laksana, Deddy Award Widya Lalang Erawan Lalang Erawan Marjuni, Aris Noor Ageng Setiyanto, Noor Ageng Nova Rijati Nur Rokhman Nur Rokhman Pulung Nurtantio Andono Saifullah, Zidan Setia Astuti Sinaga, Daurat Sinaga, Daurat Sindhu Rakasiwi Solichul Huda, Solichul suprayogi Suprayogi Suprayogi Suprayogi Suprayogi Suprayogi Suprayogi Wellia Shinta Sari Widya Laksana, Deddi Award Yaacob, Noorayisahbe Mohd