Penerjemahan Bahasa Isyarat Indonesia Menggunakan Kamera pada Telepon Genggam Android

Penginderaan visual atau machine vision merupakan suatu proses manipulasi data citra. Data tersebut dapat digunakan untuk melakukan intepretasi banyak hal, salah satunya yaitu pengenalan gesture. Pengenalan gesture adalah antarmuka yang dapat mengenali gerak-isyarat seorang manusia dan mentranslasikan gerakan tersebut sebagai instruksi yang dapat dipahami oleh komputer. Pengenalan gesture dapat digunakan untuk penerjemahkan bahasa isyarat pada orang tunawicara. Hal ini karena banyaknya orang yang tidak mengerti bahasa tangan orang tunawicara. Sehingga, orang tunawicara kesulitan dalam berinteraksi di masyarakat.Pada tugas akhir ini pengenalan gesture untuk penerjemahan bahasa isyarat lebih mengarah pada hand recognition, yaitu pendeteksian Perubahan gerak tangan, dengan menggunakan android mobile phone sebagai divaisnya. Android mobile phone memiliki kamera untuk menangkap citra orang tuna wicara saat berkomunikasi menggunakan bahasa isyarat berupa gerakan tangan. Selanjutnya, citra diproses oleh processing unit android untuk melakukan proses hand recognition. Setelah proses tersebut selesai, maka layar display akan memunculkan huruf atau kata dari Perubahan posisi gerak tangan yang dilakukan orang tunawicara yang berada di depan kamera

Multi Faceted Text Classification using Supervised Machine Learning Models

In recent year’s document management tasks (known as information retrieval) increased a lot due to availability of digital documents everywhere. The need of automatic methods for extracting document information became a prominent method for organizing information and knowledge discovery. Text Classification is one such solution, where in the natural language text is assigned to one or more predefined categories based on the content. In my research classification of text is mainly focused on sentiment label classification. The idea proposed for sentiment analysis is multi-class classification of online movie reviews. Many research papers discussed the classification of sentiment either positive or negative, but in this approach the user reviews are classified based on their sentiment to multi classes like positive, negative, neutral, very positive and very negative. This classification task would help the business to classify the user reviews same as star ratings, which are manually given by users. This paper also proposes a better classification approach with multi-tier prediction model. The goal of this research is to provide a better understanding classification for sentiment analysis by applying different preprocessing techniques and selecting suitable features like bag of words, stemming and removing stop words, POS Tagging etc. These features are adjusted to fit with some of the machine learning text classification algorithms such as Naïve Bayes, SVM, sand SGD on frameworks like WEKA, SVMLight & Scikit Learn

FEATURES EXTRACTION OF HEP-2 IMMUNOFLUORESCENCE PATTERNS BASED ON TEXTURE AND REGION OF INTEREST TECHNIQUES

Autoimmune disease is a disease that happens when improper immune response in the body fighting against substance, cells and tissues that naturally exists and needed in human’s body. This will later on cause autoantibody disease such as SLE where internal organ failed to perform their basic functions. Antinuclear antibody (ANA) test is a way to test the presence of autoantibodies in individual blood serum. This study focuses on ANA test that is done using indirect immunofluorescence HEp-2 cell coating slides that are used to place the blood serum. However, there are several problems encountered with current technique, such as inaccuracy of the result as the result is viewed by naked eyes of operator. There is no objective definition for positive, negative or border line of infection. This project involves developing features extraction technique of HEp-2 cell of 2 main patterns namely Nucleolar and Centromere using texture and region of interest technique. Next, to design an algorithm that can automatically identify the 2 patterns of the HEp-2 cell tested using ANA. To execute features extraction, image pre-processing is performed to enhance image in terms of its intensity, brightness and contrast. Only clear and good input image will produce good results. Image segmentation will be done after pre-processing completed to further enhance the image according to its edge or region to be used for the input image. Different methods of features extraction will be used and compared for better outcome. To differentiate between one pattern from another, image classification is done by evaluating the properties of internal image from features extraction and a boundary is drawn between Centromere and Nucleolar pattern. The result shows four different types of properties of internal cells which are homogeneity, contrast, energy and correlation. After analysis has been done, energy between Centromere and Nucleolar are different from each other and used to classify the pattern in SVM classifier. Tools used in this study are MATLAB software and image processing tools in MATLAB