Indonesia is one of the countries with a large number of fauna wealth. Various types of fauna that exist are scattered throughout Indonesia. One type of fauna that is owned is a type of bird animal. Birds are often bred as pets because of their characteristic facial voice and body features. In this study, using the Gray Level Co-Occurrence Matrix (GLCM) based on the k-Nearest Neighbor (K-NN) algorithm. The data used in this study were 66 images which were divided into two, namely 55 training data and 11 testing data. The calculation of the feature value used in this study is based on the value of the GLCM feature extraction such as: contrast, correlation, energy, homogeneity and entropy which will later be calculated using the k-Nearest Neighbor (K-NN) algorithm and Eucliden Distance. From the results of the classification process using k-Nearest Neighbor (K-NN), it is found that the highest accuracy results lie at the value of K = 1 and at an degree of 0 ° of 54.54%.
Abstrak-Salah satu teknik penyembunyian data yang populer adalah steganografi. Teknik ini dapat mengecoh pihak penyadap data sehingga informasi rahasia tetap aman. Steganografi dapat digunakan dengan menerapkan sejumlah algoritma dengan bantuan pemrosesan komputer. Algoritma steganografi yang sering diteliti antara lain least significant bit (LSB) dan most significant bit (MSB). LSB merupakan salah satu algoritma steganografi yang melakukan proses perhitungan bit dengan nilai paling kecil, sedangkan MSB melakukan proses yang sama namun dengan pilihan angka yang besar. LSB merupakan algoritma sederhana namun dapat digunakan pada proses steganografi, begitu pula dengan MSB. Penelitian ini membahas tentang uji performa algoritma LSB dan MSB dalam steganografi, baik dari segi kulitas hasil steganografi, dan ketahanan terhadap serangan. Alat ukur yang digunakan dalam penelitian adalah Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), dan Coefficient Correlation (CC). Berdasarkan hasil penelitian metode LSB terbukti lebih baik dari segi kulitas, sedangkan ketahanan terhadap serangan MSB lebih unggul pada jenis serangan salt and pepper. Kata kunci-Steganografi, penyembunyian pesan, LSB, MSB, uji komparasi.Abstract-One of the most popular data hiding techniques is steganography. This technique can outwit the data tapper so that the secret information remains safe. Steganography can be used by applying a number of algorithms with the help of computer processing. Steganography algorithms that are often studied include Least Significant Bit (LSB) and Most Significant Bit (MSB). LSB is one of the steganography algorithms that perform the bit calculation process with the smallest value, while the MSB perform the same process but with a large number of choices. LSB is a simple algorithm but can be used in steganography process, as well as MSB. This study discusses the performance test of LSB and MSB algorithm in steganography, both in terms of quality of steganography, and resistance to attack. The measuring instruments used in this research are Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Coefficient Correlation (CC). Based on the results of research LSB method proved better in terms of quality, whereas resistance to MSB attacks superior to the type of attack salt and pepper.
In this study, batik has been modeled using the GLCM method which will produce features of energy, contrast, correlation, homogenity and entropy. Then these features are used as input for the classification process of training data and data testing using the K-NN method by using ecludean distance search. The next classification uses 5 features that provide information on energy values, contrast, correlation, homogeneity, and entropy. Of the two classifications, which comparison will produce the best accuracy. Training data and data testing were tested using the Recognition Rate calculation for system evaluation. The results of the study produced 66% recognition rate in 50 pieces of test data and 100 pieces of training data.
Salah satu algoritma yang sering digunakan untuk melakukan deteksi pada wajah yaitu Viola-Jones. Metode ini merupakan gabungan dari 3 buah fitur yaitu integral image, adaboost dan cascade classifier. Masing-masing fitur mempunyai fungsi tersendiri dan saling melengkapi. Integral image digunakan dalam penentuan ada dan tidaknya gambar, adaboost untuk memilih dan mengatur nilai threshold, sedangkan cascade classifier untuk mengklasifikasi daerah yang akan di deteksi. Untuk memudahkan deteksi, terurtama pada bagian mata maka digunakan Haar like feature. Proses pengenalan wajah telah dilakukan pada gambar dengan satu objek dan beberapa objek. Hasil impelemntasi juga dapat mengenali objek foto lukisan dan foto tampak samping. Dari seluruh percobaan di dapatkan nilai rata-rata sebesar 65% dengan sebaran nilai akurasi tertinggi 70%, sensitivitas 55% dan spesifitas 71%.
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