Abstrak -Tanda tangan merupakan penanda atau identitas yang ada pada suatu dokumen. Tanda tangan mempunyai peranan penting dalam memverifikasi dan melegalisasi dokumen. Tujuan dari penelitian ini menerapkan teknik pengolahan citra pada tanda tangan dan mengidentifikasi pola citra tanda tangan berdasarkan nilai entropi dan waktu perhitungan nilai entropi. Tahapan penelitian meliputi pengambilan data responden berupa tanda tangan citra analog, berikutnya akusisi citra tanda tangan digital dengan cara memindai tanda tangan tersebut, tahap selanjutnya mengkonversi citra tangan tangan digital dari true color menjadi binary. Tahap akhir melakukan perhitungan nilai entropi dan mencatat waktu perhitungan nilai entropi dengan menggunakan software matlab dan dilihat sebaran nilai entropi dari masing -masing citra tanda tangan. Sebaran nilai entropi pada tanda tangan asli mempunyai error 3,31% dari total responden (30 responden). Nilai error ini merupakan nilai entropi yang keluar dari kelompoknya. Waktu perhitungan nilai entropi pada tanda tangan palsu jika coretan atau piksel pada citra lebih besar dari citra tanda tangan asli maka waktu perhitungan nilai entropinya lebih lama dibandingkan dengan citra tanda tangan asli.Kata kunci -Tanda Tangan, Konversi citra digital, Nilai Entropi.Abstract -Signature is a token or an identity that exists on a document. Signature has an important role in the verification and legalization of a document. The purpose of this research is to apply image processing technique on signatures and to classify their patterns based on entropy values and time calculation entropy value. Stages of this research include the retrieval of respondent data in the form of signature's analog images, followed by the acquisition of digital signature images by way of scanning them and the next stage is to convert digital signature images from true color to binary. The last stage is to calculate entropy values and time calculation entropy value by using Matlab software and entropy value distribution of every signature is then reviewed. Distribution of entropy values in the original signature has an error of 3.31% out of the total respondents (30 respondents). This error value is the value of entropy that falls outside of the group. If the strokes or pixels on a forged signature are larger than those of original one then the computation time to process the forged signature will be longer than it took to process the original one.
Instagram is a social media for sharing images, photos and videos. Instagram has many active users from various circles. In addition to sharing submissions, Instagram users can also give likes and comments to other users' posts. However, the comment feature is often misused, for example it is used for cyberbullying which includes one act against the law. But until now, Instagram still does not provide a feature to detect cyberbullying. Therefore, this study aims to create a system that can classify comments whether they contain elements of cyberbullying or not. The results of the classification will be used to detect cyberbullying comments. The algorithm used for classification is Naïve Bayes Classifier. Then for each comment will pass the preprocessing and feature extraction stages with the TF-IDF method. For evaluation and testing using the K-Fold Cross Validation method. The experiment is divided into two, namely using stemming and without stemming. The training data used is 455 data. The best experimental results obtained an accuracy of 84% both with stemming, and without stemming.
Besides specification and price, smartphone reviews can affect on consumer interest buying. This study aims to use the value of smartphone review sentiment as one of the attributes/criterias in addition to specifications and prices on the calculation of Decision Support System using MOORA method to generate smartphone recommendations. Sentiment value is obtained from sentiment analysis using SentiWordNet. There are two approaches of MOORA method used in this research, Ratio System and Reference Point Approach. Testing has been done by comparing the results of smartphone recommendations between approaches on the MOORA method, with or without sentiment analysis, on smartphone rankings based on the number of smartphone fans on the GSM Arena site. The test results show that the method of MOORA with Ratio System approach without sentiment analysis has the best accuracy among other approaches. Intisari-Selain spesifikasi dan harga, review smartphone dapat memengaruhi minat beli konsumen. Makalah ini bertujuan untuk menggunakan nilai sentimen review smartphone sebagai salah satu atribut/kriteria, selain spesifikasi dan harga, pada perhitungan Sistem Pendukung Keputusan menggunakan metode MOORA untuk menghasilkan rekomendasi smartphone. Nilai sentimen diperoleh dari sentiment analysis menggunakan SentiWordNet. Terdapat dua pendekatan dari metode MOORA yang digunakan pada makalah ini, yaitu pendekatan Ratio System dan Reference Point. Pengujian dilakukan dengan membandingkan hasil rekomendasi smartphone antar pendekatan pada metode MOORA, dengan atau tanpa sentiment analysis, terhadap ranking smartphone berdasarkan jumlah fan smartphone pada situs GSM Arena. Hasil pengujian menunjukkan bahwa metode MOORA dengan pendekatan Ratio System tanpa sentiment analysis memiliki akurasi paling bagus di antara metode-metode lain.
Abstrak -Tanda tangan merupakan salah satu bukti pengesahan dokumen yang sering digunakan. Pentingnya mengenal bentuk tanda tangan seseorang diperlukan untuk melakukan verifikasi terhadap dokumen apakah benar yang memberikan tanda tangan adalah orang yang bersangkutan atau orang lain. Pada penelitian ini, penulis mendesain sistem identifikasi tanda tangan dengan fitur yang digunakan adalah nilai entropy yang diambil dari grid image (sub-citra) suatu citra tanda tangan. Model pelatihan dan pengujian menggunakan multi layer perceptron dan cross validation dengan tiga ukuran grid (4x4, 8x8, dan 16x16) dan dua jenis representasi citra (citra biner dan citra outline). Hasil pengujian terbaik adalah untuk pengujian ukuran grid sebanyak 8x8 dan menggunakan citra outline, yaitu dengan tingkat akurasi sebesar 97,78%, nilai korelasi 0,981, dan nilai kappa 0,977. Kata kunci -cross validation, entropy, grid entropy, identifikasi, multi layer perceptron, tanda tanganAbstract-The signature is one frequently proof validation used on documents. Recognition of signature is required to verify document whether the signature is given by concerned person or others. In this study, the authors design a signature identification system based on the value of entropy that taken from the grid image of an image of a signature. Training and testing model using a multi layer perceptron and cross validation by three grid sizes (4x4, 8x8, 16x16) and two types of image representation (binary image and the image of the outline). The best test results obtained on the grid size 8x8 using outline image that is the accuracy rate of 97.78%, the value of correlation 0.981, and a kappa value of 0.977.
Sentiment analysis is a field of study that analyzes one's opinions, sentiments, evaluations, attitudes and emotions that are conveyed in written text. There are several factors that cause low accuracy results from sentiment analysis. These factors such as less optimal stemming process, word negation process that does not produce maximum results, writing errors in the dataset, and others. These problems can be overcome by optimizing the process of normalizing words, negation, stemming, and adding methods of semantic expansion. The purpose of adding the Semantic Expansion method and improvement in the process is to increase the accuracy value of the Sentiment Analysis process. This study aims to create a sentiment analysis model from public comments on a public figure (Ridwan Kamil) using the Naïve Bayes Classifier algorithm. Based on the test results in the sentiment analysis model using the Naïve Bayes Classifier method with the addition of the semantic expansion method it is proven that it can improve accuracy. The accuracy obtained using the semantic expansion method is 72%. While the value of accuracy without semantic expansion is 70%.
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