Deteksi Copy Move Forgery Pada Citra Menggunakan Exact Match, DWT Haar dan Daubechies

Copy-Move Forgery is a special type of image forgery, in which a part of a digital image is copied and pasted to another part in the same image in order to cover an important image feature. This research developed a system to detect copy move forgery in digital image. The system is intended to help the user determine whether an image is authentic or already contained a copy move object, and if the image already contains copy move object, the system can determine in which section the copy move object is located. Copy move forgery detection system discussed in this research, was developed by using Exact Match, DWT Haar, DWT db2 and DWT db4 using blocks of 4 x 4, 8 x 8 and 16 x 16. Users can use the system by using the digital image as input. The output of the system is the information about the area detected as a copy move forgery along with areas suspected of being false match.The final result is shown in the form of accuracy, the area of the false match and execution time. Based on the test results, the accuracy of Exact Match method for blocks of 4 x 4, 8 x 8 and 16 x 16 is better than the DWT, although exact match have an bigger false match area. Accuracy of DWT Haar, DWT db2 and DWT DB4 depending on the copy move area on the image. Block 4 x 4 has a false match area larger than the block 8 x 8 and 16 x 16. The execution time depends on the size of the block, the larger the block, the longer the time of execution.

CASE BASED REASONING UNTUK MENDIAGNOSA PENYAKIT ANAK MENGGUNAKAN METODE BLOCK CITY

Metode Case Based Reasoning (CBR) adalah salah satu metode untuk membangun sebuah sistem yang bekerja dengan cara mendiagnosa kasus baru berdasarkan kasus lama yang pernah terjadi dan memberikan solusi pada kasus baru berdasarkan pada kasus lama yang memiliki nilai kemiripan tertinggi. Pada penelitian ini, penulis menerapkan CBR untuk mendiagnosis penyakit penyakit anak usia 1-12 tahun. Sumber pengetahuan sistem diperoleh dengan mengumpulkan berkas rekam medis pasien pada tahun 2014 dan 2015. Perhitungan nilai kemiripan menggunakan metode Block City fungsi Gower dengan nilai batas kewajaran adalah 70%. Sistem ini dapat mendiagnosis 10 penyakit berdasarkan 48 gejala yang ada. Keluaran sistem berupa penyakit yang dialami oleh pasien berdasarkan gejala yang diinputkan oleh tenaga medis non dokter, solusi penanganan dan presentasi kemiripan dengan kasus terdahulu untuk menunjukan tingkat kebenaran hasil diagnosis. Berdasarkan hasil pengujian menggunakan 83 kasus baru didapatkan keakuratan sistem sebesar 75,90%

ALGORITMA MD5 DAN RC5 UNTUK PENGAMANAN FILE PDF

Electronic documents are digital file types used to store the important data or information of a person or an institution. There are some of the most widely used document file formats that are. docx,. xlsx,. pptx, and. pdf. Issues that arise when a company, institution or organization that has confidential documents and important data in the form of document files can be accessed by persons or parties who have no authority. A document security solution can use cryptographic onsep. The document file types that can be encrypted are restricted to the Portable Document Format (. pdf) file. In this research is done lock security user input using the algorithm Message Digest 5 (MD5) and binary value multiplier in PDF files using Rivest Code algorithm (RC5). The test results using (i) the same key on a different PDF file indicate that the resulting binary chiperfile is different from the derived binary Plainfile, (ii) the key length of 1 to 8 characters on the same PDF file indicates that binary The chiperfile generated each key length differs from the binary plainfile taken, (iii) a change of 1 character at the beginning, in the middle, and at the end of the input key indicates that the method used is sensitive to the character changes on the key Input. It is known from the level of similarity of binary chiperfile and small/low binary plainfile based on the value of the collation generated per test is (i) 0.205795252, (ii) 0.24692765, (iii) 0.22421886.Dokumen elektronik  adalah jenis berkas digital yang digunakan untuk menyimpan data atau informasi penting seseorang atau suatu lembaga. Terdapat beberapa format file dokumen yang paling banyak digunakan yaitu .docx, .xlsx, .pptx, dan .pdf. Masalah yang muncul ketika suatu perusahaan, institusi atau organisasi yang mempunyai dokumen-dokumen rahasia dan data-data yang penting berupa file dokumen bisa diakses oleh orang atau pihak yang tidak memiliki wewenang. Solusi pengamanan dokumen dapat menggunakan konsep kriptografi. Jenis file dokumen yang dapat dienkripsi dibatasi pada file Portable Document Format(.pdf). Pada penelitian ini dilakukan pengamanan kunci inputan user menggunakan algoritma Message Digest 5 (MD5) dan pengacakan nilai binary pada file pdf menggunakan algoritma Rivest Code (RC5). Hasil pengujian menggunakan (i) kunci yang sama pada file pdf yang berbeda menunjukkan bahwa binary chiperfile yang dihasilkan berbeda dengan binary plainfile yang diambil.Hal ini diketahui dari tingkat kesamaan binary chiperfile dan binary plainfile yang kecil/rendah berdasar pada nilai kolerasi yang dihasilkan tiap pengujian adalah (i) 0,205795252, (ii) 0,24692765, (iii) 0,22421886

CASE BASED REASONINGUNTUK MENDIAGNOSIS GIZI BURUK PADA ANAKUSIA 0-5 TAHUN MENGGUNAKAN METODE COSINE SIMILARITY

Case Based Reasoning (CBR) method is one method to build a system with new case decision based on solution from previous cases by calculating similarity level. The calculation of similarity values ​​using the Cosinr Similarity with threshold 80%. This system can diagnose 3 diseases based on 23 existing symptoms.Based on the results of the test case obtained the results: the system can take back the old case is appropriate and has used the formulation of Naïve Bayes method to distribution of disease class and used the formulation of Cosine Similarity method to calculation correctly indicated with 100% accuracy, and use 122 cases. . Based on the test of 40 new cases obtained system accuracy of 80%

KAJIAN MACHINE LEARNING DENGAN KOMPARASI KLASIFIKASI PREDIKSI DATASET TENAGA KERJA NON-AKTIF

Comparative studies of machine learning are carried out with the aim of determining the best method base based on the ability to predict with true data. The study carried out on the labor dataset aims to extract information on the choice of agency employees to exit or not. The method used in the comparative study is K-Nearest Neighbors (KNN) from the basis of similarity, Naïve Bayes (NB) from the probability base, and C4.5 from the basis of the decision tree. Application design and construction is done by receiving input labor data, the dataset is divided into training data and test data, training data for training and models while the test data is used when classifying by model. The classification process is carried out using supply training scenarios and cross validation of 14,999 data. The initial hypothesis C4.5 is the best method with an accuracy measure. Proof of the initial hypothesis will be true if the best accuracy majority is owned by the C4.5 method with supply trainning scenarios and cross validation. The results of the classification data analysis found that the C4.5 accuracy was superior in each parameter of the inventory training scenario data distribution and the k-fold parameter was 3. 5. 7, and 9 of the cross validation scenario so that the best method of non-active labor classification was C4.5