1,720,963 research outputs found
Analisis Perbandingan Waktu Enkripsi Menggunakan Algoritma Message-Digest 5 (MD5) Dan Algoritma Affine Cipher Dalam Enkripsi Pesan
No full textKriptografi merupakan ilmu yang digunakan untuk menyamarkan pesan yang akan
dikirim/transmit oleh pengirim ke penerima pesan. Teknik untuk mengamankan pesan adalah
dengan mengenkripsi pesan tersebut menjadi tidak dapat dibaca secara utuh atau informasi sudah
diacak. Salah satu algoritma yang cukup banyak digunakan sampai saat ini yaitu algoritma MD5
yang hasil enkripsinya berupa hash dengan panjang 32 bit. Kemampuan algoritma MD5 diakui
sampai sekarang masih digunakan dalam melakukan enkripsi pada banyak aplikasi maupun
website. Algoritma affine cipher merupakan algoritma dengan tipe kunci simetris yang juga
sering digunakan dalam mengamankan pesan. Pada penelitian ini, penulis menganalisis kinerja
algoritma MD5 dan juga algoritma affine cipher berupa analisis waktu enkripsi berdasarkan
panjang pesan yang akan dienkripsi serta cipherteks yang dihasilkan untuk dari proses enkripsi
dari kedua algoritma tersebut
Analisis Kombinasi Message-Digest Algorithm 5 (MD5) dan Affine Block Cipher terhadap Serangan Dictionary Attack Untuk Keamanan Router Weblogin Hotspot
No full textCryptography is the science of disguising the messages so that only well known by the provider and the recipient. One of the algorithm that is quite a lot of used until this time is algorithm message-digest 5 or MD5. The output produced by the algorithm MD5 be hash. But this algorithm has many found weakness because the length of the bit is used. In this research, the authors analyze the performance of the algorithm MD5 and combine with affine algorithm block cipher for can reduce the weakness that exist on the algorithm MD5. The results obtained from this research is the affine algorithm block cipher have a good security level because it has the key length of value n of 255255255255 and have numbers relatively prima available as much as 117710117810.Kriptografi adalah ilmu yang digunakan untuk menyamarkan pesan yang akan dikirim oleh pengirim ke penerima pesan. Salah satu algoritma yang cukup banyak digunakan sampai saat ini yaitu algoritma message-digest 5 atau MD5. Output yang dihasilkan oleh algoritma MD5 berupa hash. Namun algoritma ini telah banyak ditemui kelemahannya karena panjang bit yang digunakan. Pada penelitian ini, penulis menganalisa kinerja dari algoritma MD5serta mengkombinasikan dengan algoritma affine block cipher untuk dapat mengurangi kelemahan yang ada pada algoritma MD5. Hasil yang diperoleh dari penelitian ini adalahalgoritma affine block cipher memiliki tingkat keamanan yang cukup baik karena memiliki panjang kunci yang bernilai n sebesar 255255255255 dan memiliki bilangan relatif prima yang tersedia sebanyak 117710117810.81 HalamanTesis Magiste
Islamophobia Sentiment Classification Using Support Vector Machine
No full textSentiment analysis is the process of understanding and classifying words into several categories. It is also known as opinion mining, which involves exploring opinions and emotions from text data. Sentiments can be classified into positive, negative, and neutral categories. Islam is a religion that has been in existence for centuries. Its teachings aim to foster peace and surrender to its creator, namely Allah SWT. The constructivist view of Islam has given rise to Islamophobia, which is the result of a long-standing construct that presents a negative image of Islam. Currently, Islamophobia is a growing issue that generates diverse views, especially on social media platforms. The analysis was conducted using the SVM algorithm and a dataset comprising 1000 tweets sourced from Twitter. The algorithm achieved an accuracy rate of 99.99% after testing, indicating its suitability for sentiment analysis. The error rate generated using MSE was 0.010, while the RMSE was 0.099
Sentiment Analysis on TikTok Discourse Surrounding the 2024 North Sumatra Gubernatorial Election Using Support Vector Machine Algorithm
No full textThis study aims to analyze public sentiment towards the 2024 North Sumatra gubernatorial election by leveraging social media data, specifically TikTok, which has become a major platform for political discourse in Indonesia. The two competing candidate pairs, Bobby Nasution–Surya and Edy Rahmayadi–Hasan Basri, have sparked widespread online discussions that range from enthusiastic support to harsh criticism. These interactions have a significant impact on public opinion formation and may influence electoral outcomes. To address this phenomenon, this research implements a sentiment classification model using the Support Vector Machine (SVM) algorithm with a polynomial kernel, known for its effectiveness in handling high-dimensional textual data. A total of 2,100 TikTok comments were collected using scraping techniques via Python. The data then underwent several preprocessing stages, including case folding, cleaning, normalization, tokenizing, slangword removal, stopword removal, and stemming. Feature extraction was conducted using the TF-IDF method, followed by lexicon-based sentiment labeling into positive and negative classes. The classification model achieved an accuracy of 82%, with a positive sentiment precision of 0.81, recall of 0.96, and F1-score of 0.88. For negative sentiment, the precision was 0.86, recall 0.51, and F1-score 0.64. These findings indicate that the model performs well in identifying explicit positive sentiments but faces challenges in recognizing complex negative expressions such as sarcasm or implicit criticism. The results provide valuable insights into digital political behavior and demonstrate the potential of machine learning-based sentiment analysis as a tool for monitoring public perception in real time during elections
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
No full text“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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