Development of the era had a considerable impact on the existence of a language. To overcome this there are some efforts to be made, one of which is to create a dictionary, a dictionary that was made to be practical and quick in use. Dictionary in question is a dictionary based on Android. To create a dictionary-based android can use string matching algorithm, one of the string matching algorithm is the Rabin-Karp algorithm, Rabin-Karp algorithm perform string matching hash value based on the text and the pattern hash value. The study produced an android based dictionary application which the base number is used to generate a hash value greatly affects the speed of search words. Average running time of 10 attempts to search for words is 14.9 ms.

Abstract

The development of sending messages from one place to another can be done regardless of distance and time. However, the delivery of these messages is hampered by problems of confidentiality and message security. Especially if the data contains important and confidential information that not just anyone is allowed to read and find out about it. In overcoming this problem, steganography techniques can be used with the Modified Least Significant Bit algorithm, where the determination of the embedding index is based on random numbers generated by the Pseudo-Random Number Generator with the Multiply with Carry algorithm. In addition to security, data size is also an important factor in data transmission. The larger the size the more time it will take to transmit the data. Therefore, the Run Length Encoding algorithm is needed to compress the data size, which will shorten the time to transmit the data. In the message extraction process, a stego key is needed to generate random numbers. Based on the testing of the extraction process with an arbitrary key, it is obtained that the message tested is not the original message that has been embedded previously. In the results of the embedding and extraction process, it is obtained that the average value of PSNR is 63.61498 dB, which means the quality of the stego object produced is quite good. Whereas the measurement of file compression performance results with an average value of Compression Ratio at 1.00113, Space Savings at 0.1133%, and Bitrate at 584025.33 bits/sample. These results indicate that RLE algorithm compression is not efficient to compress file sizes.

Penelitian ini dilakukan untuk menganalisis perbandingan hasil kompresi dan dekompresi file audio*.mp3 dan *.wav. Kompresi dilakukan dengan mengurangi jumlah bit yang diperlukan untuk menyimpan atau mengirim file tersebut. Pada penelitian ini penulis menggunakan algoritma Huffman dan Run Length Encoding yang merupakan salah satu teknik kompresi yang bersifat lossless.Algoritma Huffman memiliki tiga tahapan untuk mengkompres data, yaitu pembentukan pohon, encoding dan decodingdan berkerja berdasarkan karakter per karakter. Sedangkan teknik run length ini bekerja berdasarkan sederetan karakter yang berurutan, yaitu hanya memindahkan pengulangan byte yang sama berturut-turut secara terus-menerus. Implementasi algoritma Huffman dan Run Length Encoding ini bertujuan untuk mengkompresi file audio *.mp3 dan *.wav sehingga ukuran file hasil kompresi lebih kecil dibandingkan file asli dimana parameter yang digunakan untuk mengukur kinerja algoritma ini adalah rasio kompresi, kompleksitas yang dihasilkan. Rasio kompresi file audio *.mp3 menggunakan Algoritma Huffman memiliki rata-rata 1.204% sedangkan RLE -94.44%, dan rasio kompresi file audio *.wav memiliki rata-rata 28.954 % sedangkan RLE -45.91%. This research was conducted to analyze the comparison of the results of compression and decompression of *.mp3 and *.wav audio files. Compression was completed by reducing the number of bits needed to save or send the file. In this study, the researcher used the Huffman algorithm and Run Length Encoding which is one of the lossless compression techniques. The Huffman algorithm has three stages to compress data, namely tree formation, encoding and decoding which work based on characters per character. On the other hand, the run length technique works based on a sequence of sequential characters that only move the repetitions of the same byte in succession continuously. The implementation of the Huffman algorithm and Run Length Encoding aimed to compress audio files *.mp3 and *.wav so that the size of the compressed file was smaller than the original file where the parameter used to measure the performance of this algorithm was the compression ratio, and the resulting complexity.*.Mp3 audio file compression ratio using Huffman Algorithm had an average of 1.204% while RLE -94.44%, and compression ratio *.wav audio files had an average of 28.954% while RLE -45.91%.