Software Development Life Cycle (SDLC) adalah siklus yang digunakan dalam pembuatan atau pengembangan sistem informasi untuk menghasilkan sistem berkualitas tinggi yang sesuai dengan keinginan pelanggan atau tujuan dibuatnya sistem tersebut. Penelitian ini bertujuan untuk menganalisis model SDLC yang digunakan untuk mengembangkan sistem informasi berbasis website dengan menggunakan data dari beberapa jurnal tahun 2022 terkait topik tersebut. Metode yang digunakan dalam penelitian ini adalah metode Systematic Literature Review (SLR). Metode SLR digunakan untuk mengidentifikasi, mengkaji, mengevaluasi, dan menafsirkan semua penelitian yang tersedia dengan bidang topik fenomena yang menarik, dengan pertanyaan penelitian tertentu yang relevan. Dari penelitian ini didapat kesimpulan bahwa paper penelitian pada tahun 2022 banyak menggunakan metode waterfall dalam pembuatan atau pengembangan sistem informasi berbasis website. Sedangkan fokus bidang yang diterapkan paling banyak adalah bidang bisnis.
Steganalysis method is used to detect the presence or absence of steganography files or can be referred to anti-steganography. Steganalysis can be used for positive purposes, which is to know the weaknesses of a steganography method, so that improvements can be made. One category of steganalysis is blind steganalysis, which is a way to detect secret files without knowing what steganography method is used. Blind steganalysis is difficult to implement, but then machine learning techniques emerged that could be used to create a detection model using experimental data, one of which is Convolutional Neural Networks (CNN). A study proposes that the CNN method can detect steganography files using the latest method with a low error probability value compared to other methods, CNN Yedroudj-net. As one of the steganalysis methods with the latest machine learning steganalysis techniques, an experiment is needed to find out whether Yedroudj-net can be a steganalysis for the output of many tools commonly used for steganography applications. Knowing the performance of CNN Yedroudj-net on several steganography tools is very important, to measure the level of ability in terms of steganalysis of some of these tools. Especially so far, machine learning performance is still doubtful in blind steganalysis. Plus some previous research only focused on certain methods to prove the performance of the proposed technique, including Yedroudj-net. This study will use five tools that are Hide In Picture (HIP), OpenStego, SilentEye, Steg and S-Tools, which are not known exactly what steganography methods are used on the tools. Yedroudj-net method will be implemented in the steganography file from the output of the five tools. Then a comparison with the popular steganalysis tool is used, StegSpy. The results show that Yedroudj-net is quite capable of detecting the presence of steganography files, slightly better than StegSpy.
The method of steganography commonly used to hide data or information is Least Significant Bit (LSB) method. One of the relevant research is LSB using sequential Encoding - Decoding by David Pipkorn and Preston Weisbrot. In this research, an analysis of the LSB method using Sequential Encoding - Decoding by doing some testing. The tests are on the aspect of message security using tools StegSpy and enhanced LSB algorithm, testing on image quality by calculating the Peak Signal to Noise Ratio (PSNR) value and see the image histogram, testing on robustness of message by doing some image processing operations on stego image, like cropping, rotating, and etc, and then testing on capacity to check size of cover image and stego image and calculates the maximum size of data that can be hidden. From the testing process, we know that there are deficiencies in the aspects of security, robustness and capacity of a message. And then in this research we try to change the location of messages that are hidden in the image bits, which previous research used the 8th bit of each bytes, changed to the 7th bit. To be able to correct deficiencies in the security aspect. After repairing and testing like before, obtained better results in the security aspect. This can be seen from the image of the enhanced LSB algorithm process, the message is not detected, but unfortunately the image quality is reduced, with the low PSNR value generated.
<p>Steganalisis digunakan untuk mendeteksi ada atau tidaknya file steganografi. Salah satu kategori steganalisis adalah blind steganalisis, yaitu cara untuk mendeteksi file rahasia tanpa mengetahui metode steganografi apa yang digunakan. Sebuah penelitian mengusulkan bahwa metode Convolutional Neural Networks (CNN) dapat mendeteksi file steganografi menggunakan metode terbaru dengan nilai probabilitas kesalahan rendah dibandingkan metode lain, yaitu CNN Yedroudj-net. Sebagai metode steganalisis Machine Learning terbaru, diperlukan eksperimen untuk mengetahui apakah Yedroudj-net dapat menjadi steganalisis untuk keluaran dari tools steganografi yang biasa digunakan. Mengetahui kinerja CNN Yedroudj-net sangat penting, untuk mengukur tingkat kemampuannya dalam hal steganalisis dari beberapa tools. Apalagi sejauh ini, kinerja Machine Learning masih diragukan dalam blind steganalisis. Ditambah beberapa penelitian sebelumnya hanya berfokus pada metode tertentu untuk membuktikan kinerja teknik yang diusulkan, termasuk Yedroudj-net. Penelitian ini akan menggunakan lima alat yang cukup baik dalam hal steganografi, yaitu Hide In Picture (HIP), OpenStego, SilentEye, Steg dan S-Tools, yang tidak diketahui secara pasti metode steganografi apa yang digunakan pada alat tersebut. Metode Yedroudj-net akan diimplementasikan dalam file steganografi dari output lima alat. Kemudian perbandingan dengan tools steganalisis lain, yaitu StegSpy. Hasil penelitian menunjukkan bahwa Yedroudj-net bisa mendeteksi keberadaan file steganografi. Namun, jika dibandingkan dengan StegSpy hasil gambar yang tidak terdeteksi lebih tinggi.</p><p><em><strong><br /></strong></em></p><p><em><strong>Abstract</strong></em></p><p><em>Steganalysis is used to detect the presence or absence of steganograpy files. One category of steganalysis is blind steganalysis, which is a way to detect secret files without knowing what steganography method is used. A study proposes that the Convolutional Neural Networks (CNN) method can detect steganographic files using the latest method with a low error probability value compared to other methods, namely CNN Yedroudj-net. As the latest Machine Learning steganalysis method, an experiment is needed to find out whether Yedroudj-net can be a steganalysis for the output of commonly used steganography tools. Knowing the performance of CNN Yedroudj-net is very important, to measure the level of ability in terms of steganalysis from several tools. Especially so far, Machine Learning performance is still doubtful in blind steganalysis. Plus some previous research only focused on certain methods to prove the performance of the proposed technique, including Yedroudj-net. This research will use five tools that are good enough in terms of steganography, namely Hide In Picture (HIP), OpenStego, SilentEye, Steg and S-Tools, which is not known exactly what steganography methods are used on the tool. The Yedroudj-net method will be implemented in a steganographic file from the output of five tools. Then compare with other steganalysis tools, namely StegSpy. The results showed that Yedroudj-net could detect the presence of steganographic files. However, when compared with StegSpy the results of undetected images are higher.</em></p>
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