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All Journal DEVICE Jurnal Ilmu Komputer dan Sistem Informasi
Dedi Irawan
Universitas Muhammadiyah Sumatera Utara
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Library & Information Science Mechanical Engineering

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KLASIFIKASI FITUR WARNA LEVEL ROASTING BIJI KOPI MENGGUNAKAN ARTIFICIAL NEURAL NETWORK Tri Andre Anu; Rika Rosnelly; Dedi Irawan; Ubaidullah Hasibuan; Progresif Bulolo5
Device Vol 13 No 1 (2023): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v13i1.4094

Abstract

Abstract align="justify"Small and Medium Enterprises (SMEs) are using a manual method to notice the roasting level classification of coffee beans. However, the weaknesses in this technique are that the coffee roaster staff consumes time sorting the roasting level of the coffee beans. As a result, the coffee roaster focuses less because they take too long to sort the coffee beans—consequently, the mixed coffee beans in packages that should be elsewhere. Therefore a system is needed to help coffee roaster officers classify coffee beans using an artificial neural network. The data used are 60 coffee beans with three roasting levels: light roasted, medium roasted, and dark roasted. The classification process consists of a training stage and a testing stage. At the testing stage, using a sample of 30 coffee beans and based on the results of this study, the best results were obtained with a training value of 90%. In contrast, the testing accuracy was 66.67%.
The Implement’s Steganografi LSB dan Kriptografi AES-256 untuk Pengamanan Data Citra Digital Muhammad Haris; Dedi Irawan; Gunawan Gunawan; Mahardika Abdi Prawira Tanjung
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 2 (2025): Mei 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i2.420

Abstract

Digital data security has become a critical issue in the digital transformation era, particularly in the exchange of digital images that are vulnerable to interception and manipulation by unauthorized parties. This study aims to implement a layered data security method by combining Advanced Encryption Standard (AES) 256-bit cryptography and Least Significant Bit (LSB) steganography on PNG-format digital images. The approach used is encrypt-then-embed, where the secret message is first encrypted using AES-256 in CBC mode, then the ciphertext is embedded into the cover image pixels through LSB bit substitution. Testing was conducted on five test images with varying resolutions (256×256 to 1024×768 pixels) using Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), entropy, avalanche effect, and computation time metrics. The test results show an average PSNR of 70,58 dB and MSE of 0.11, indicating excellent visual quality of the stego images. Security analysis shows an average avalanche effect of 50.29%, approaching the ideal value of 50%, and stego image entropy approaching 7.0545 bits/pixel. The computation time for encryption and embedding processes ranges from 0,005847 to 0,006078 seconds. The combination of AES-256 and LSB has proven effective in providing layered security: AES-256 ensures data confidentiality, while LSB conceals the message existence within the image without significant visual degradation.
Co-Authors Gunawan Gunawan Muhammad Haris Progresif Bulolo5 Rika Rosnelly Tri Andre Anu Ubaidullah Hasibuan