Analysis Sentiment Based on IMDB Aspects from Movie Reviews using SVM

A movie is a spectacle that can be done at a relaxed time. Currently, there are many movies that can be watched via the internet or cinema. Movies that are watched on the internet are sometimes charged to watch so that potential viewers before watching a movie will read comments from users who have watched the movie. The website that is often used to view movie comments today is IMDB. Movie comments are many and varied on the IMDB website, we can see comments based on the star rating aspect. This causes users to have difficulty analyzing other users' comments. So, this study aims to analyze the sentiment of opinions from several comments from IMDB website users using the star rating aspect and will be classified using the support vector machine method (SVM). Sentiment analysis is a classification process to understand the opinions, interactions, and emotions of a document or text. SVM is very efficient for many applications in science and engineering, especially for classification (pattern recognition) problems. In addition to the SVM method, the TF-IDF technique is also used to change the shape of the document into several words. The results obtained by applying the SVM model are 79% accuracy, 75% precision, and 87% recall. The SVM classification is also superior to other methods, namely logistic regression.

SENTIMENT ANALYSIS OF THE LARGE PRIEST OF FPI’S RETURN USING SUPPORT VECTOR MACHINE WITH OVERSAMPLING METHOD

At the end of 2020, Habib Rizieq's return to Indonesia drew criticism from the public for causing crowds during the Covid-19 pandemic. News and opinions about Habib Rizieq fill internet platforms, including Twitter. The researcher wants to classify the opinion text data of Habib Rizieq's return from Twitter into positive and negative sentiments using the Support Vector Machine method. Opinion data comes from Twitter, so the data is analyzed by text mining through the preprocessing stage. The SVM classification of unbalanced data between positive and negative classes resulted in 95.06% accuracy with a negative class precision value of 84% and better than 72% recall, in the positive class the precision value was 96% less than 2% of recall 98%. While the SVM classification with the oversampling method gets 100% accuracy, precision, and recall. The results of positive sentiments are known that the public will always support and want freedom for Rizieq, for negative sentiments it is known that many people are disappointed with Rizieq regarding the lies of his swab test results.

Disc damage likelihood scale recognition for Glaucoma detection

Glaucoma is a visual disorder, which is one of the significant driving reason for visual impairment. Glaucoma leads to frustrate the visual information transmission to the brain. Dissimilar to other eye illness such as myopia and cataracts. The impact of glaucoma can’t be cured; The Disc Damage Likelihood Scale (DDLS) can be used to assess the Glaucoma. The proposed methodology suggested simple method to extract Neuroretinal rim (NRM) region then dividing the region into four sectors after that calculate the width for each sector and select the minimum value to use it in DDLS factor. The feature was fed to the SVM classification algorithm, the DDLS successfully classified Glaucoma disease with 70% percentage; moreover, when the dimensions of both Optic Disc(OD) and Optic Cup(OC) were used as additional features the accuracy rate raised to 91%.

ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE

Remote sensing can make seagrass aboveground carbon stock (AGCseagrass) information spatially extensive and widely available. Therefore, it is necessary to develop a rapid approach to estimate AGCseagrass in the field to train and assess its remote sensing-based mapping. The aim of this research is to (1) analyze the Percent Cover (PCv)-AGCseagrass relationship in seagrass at the species and community levels to estimate AGCseagrass from PCv and (2) perform AGCseagrass mapping at both levels using WorldView-2 image and assess the accuracy of the resulting map. This research was conducted in Karimunjawa and Kemujan Islands, Indonesia. Support Vector Machine (SVM) classification was used to map seagrass species composition, and stepwise regression was used to model AGCseagrass using deglint, water column corrected, and principle component bands. The results were a rapid AGCseagrass estimation using an easily measured parameter, the seagrass PCv. At the community level, the AGCseagrass map had 58.79% accuracy (SEE = 5.41 g C m−2), whereas at the species level, the accuracy increased for the class Ea (64.73%, SEE = 6.86 g C m−2) and EaThCr (70.02%, SEE = 4.32 g C m−2) but decreased for ThCr (55.08%, SEE = 2.55 g C m−2). The results indicate that WorldView-2 image reflectance can accurately map AGCseagrass in the study area in the range of 15–20 g C m−2 for Ea, 10–15 g C m−2 for EaThCr, and 4–8 g C m−2 for ThCr. Based on our model, the AGCseagrass in the study area was estimated at 13.39 t C.

Analysis of rainfall classification over Tanah Laut disrict based on global climate indicators using support vector machine method

The Support Vector Machine (SVM) classification method can be applied in various fields, one of which is meteorology and climatology in rainfall forecasting. Thus, a study was conducted by classifying rainfall to recognize the relationship between global phenomena and rainfall and the results of applying the classification using the SVM method to rainfall in the Tanah Laut Regency. The analysis is carried out using the SVM Multiclass concept with 4 categories of rainfall classification: low, medium, high, and Extreme. The kernel used in SVM is the RBF kernel with optimization parameters used, namely Cost (C) 1,5,10,15 and Gamma (γ) 1,5,10,15. The dataset formed is based on the annual period, climatic conditions, and seasonality. The Spearman Rank correlation test describes the relationship between global phenomena and rainfall with a correlation range of (−0.1456 ) − (0.43144) for the entire dataset. The implementation of the SVM classification method shows that the Cost (C) 10 and Gamma (γ) ≥ 5 parameters obtained the highest accuracy of 100% on the training data. In contrast, in testing the data testing, the accuracy was good, namely the accuracy of 78.00% in La Nina and 81.38% in seasonal periods.

Umpire Gesture Detection and Recognition using HOG and Non-Linear Support Vector Machine (NL-SVM) Classification of Deep Features in Cricket Videos

Gesture Recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human–computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation, monitoring patients or elder people, surveillance systems, sports gesture analysis, human behaviour analysis etc., to virtual reality. In recent years, there has been increased interest in video summarization and automatic sports highlights generation in the game of Cricket. In Cricket, the Umpire has the authority to make important decisions about events on the field. The Umpire signals important events using unique hand on signals and gestures. The primary intention of our work is to design and develop a new robust method for Umpire Action and Non-Action Gesture Identification and Recognition based on the Umpire Segmentation and the proposed Histogram Oriented Gradient (HOG) feature Extraction oriented Non-Linear Support Vector Machine (NL-SVM) classification of Deep Features. Primarily the 80% of Umpire action and non-action images in a cricket match, about 1, 93, 000 frames, the Histogram of Oriented Gradient Deep Features are calculated and trained the system having six gestures of Umpire pose. The proposed HOG Feature Extraction oriented Non-Linear Support Vector Machine classification method achieves the maximal accuracy of 97.95%, the maximal sensitivity of 98.87%, the maximal specificity of 98.89% and maximal Precision of 97.02% which indicates its superiority.