Hill Cipher merupakan salah satu algoritma kriptografi yang memanfaatkan matriks sebagai kunci untuk melakukan enkripsi dan Dekripsi dan aritmatika modulo. Setiap karakter pada plaintext ataupun ciphertext dikonversikan kedalam bentuk angka. Enkripsi dilakukan dengan mengalikan matriks kunci dengan matriks plaintext, sedangkan Dekripsi dilakukan dengan mengalikan invers matriks kunci dengan matriks ciphertext. Karena itulah, Hill Cipher hanya bisa menggunakan matriks persegi sebagai matriks kuncinya. Invers semu atau pseudo invers dapat dimanfaatkan pada algoritma Hill Cipher, sehingga matriks kunci yang digunakan tidak terbatas pada matriks persegi saja. Penggunaan matriks persegi panjang menjadikan ciphertext lebih panjang dari plaintext. Hal ini tentunya membuat pesan menjadi lebih tersamarkan. Pada tulisan ini, penulis menggunakan modulo 95 artinya inputan data ada 95 simbol. Untuk mempermudah penghitungan pada saat inisialisasi matriks kunci, proses enkripsi dan proses Dekripsi menggunakan program aplikasi C++.
Social media has an important role in human life. In its implementation social media is used as a media for opinion and self-expression. One of the social media that is often used in Indonesia is Twitter. PT PLN (Persero) as a State-Owned Enterprise that is engaged in providing electricity always tries to provide optimal services. The text mining method can be used to control PT PLN (Persero) service quality by classifying Twitter data with the k-Nearest Neighbors algorithm. Text mining is used to extract information from unstructured textual data to produce useful information. Data classification is a text mining application for information retrieval. In this study the data collected will pass the preprocessing stage, using the k-Nearest Neighbors algorithm to classify data into negative, neutral, or positive classes. The data used in this study was sourced from Twitter. Data is taken from 1 December 2019 to 1 February 2020 using the Twitter API with the keyword ‘@ pln_123’. We obtained 3,000 tweet data successfully. The results are implemented in web-based applications that are built using the Python programming language. Evaluation of the k-Nearest Neighbors model produces an accuracy value of 87.41%. The classification prediction results also show that there is a tendency of the positive sentiment of 35%, the neutral sentiment of 28%, and the negative sentiment of 37%.
Sundanese language is one of the popular languages in Indonesia. Thus, research in Sundanese language becomes essential to be made. It is the reason this study was being made. The vital parts to get the high accuracy of recognition are feature extraction and classifier. The important goal of this study was to analyze the first one. Three types of feature extraction tested were Linear Predictive Coding (LPC), Mel Frequency Cepstral Coefficients (MFCC), and Human Factor Cepstral Coefficients (HFCC). The results of the three feature extraction became the input of the classifier. The study applied Hidden Markov Models as its classifier. However, before the classification was done, we need to do the quantization. In this study, it was based on clustering. Each result was compared against the number of clusters and hidden states used. The dataset came from four people who spoke digits from zero to nine as much as 60 times to do this experiments. Finally, it showed that all feature extraction produced the same performance for the corpus used.
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