SENTIMENT ANALYSIS OF ONLINE LOANS ON TWITTER USING LEXICON BASED METHODS AND SUPPORT VECTOR MACHINE (SVM)

Many online platforms for movie streaming have emerged nowadays in Indonesia with the development of technology. Streaming platforms that are widely used by the public are Netflix, Disney+, HBO Go, WeTV, Vidio. A lot of comparisons between streaming platforms have become a topic of discussion on social media. The opinions expressed by users of the streaming platform greatly affect other users who want to watch the movie. The transmission of opinions from users becomes positive and negative comments that are received by the public and spread widely on social media. Opinions that contain both positive and negative comments become a problem and can affect other users who want to watch movies through streaming platforms. The opinions of users about the streaming platform can be analyzed through sentiment analysis. At the stage of data collection using scrapy framework tools with Python, as much as 1,500 lines of text are preprocessed. LDA method to present topics that contain representative words. Classification using the Support Vector Machine (SVM) gets more positive comments than negative comments. The test results obtained an accuracy of 0.82, recall of 0.8, F1 score of 0.79, and precision of 0.8.

In the year 2018, 18.9% of the population in Indonesia mentioned that the main reason for their use of the Internet is social media. One of the social media with an active user of 6.43 million users is Twitter. Based on the surge of information published via Twitter, it is possible that such information may contain the user's opinions on an object, such objects may be events around the community such as a product or service. This makes the company use Twitter as a medium to disseminate information. An example is an Internet Service Provider (ISP) such as Indihome. Through Twitter, users can discuss each other's complaints or satisfaction with Indihome's services. It takes a method of sentiment analysis to understand whether the textual data includes negative opinions or positive opinions. Thus, the authors use the Support Vector Machine (SVM) method in sentiment analysis on the opinions of the Indihome service user on Twitter, with the aim of obtaining a sentiment classification model using SVM, and to know how much accuracy the SVM method generates, which is applied to sentiment analysis, and to see how satisfied the Indihome service users are based on Twitter. After testing with SVM method The result is accuracy 87%, precision 86%, recall 95%, error rate 13%, and F1-score 90%

One of the social media with 24.85 million active users is Twitter. Information published on Twitter can be in the form of user opinions on an object, such as a product or service. Therefore, the company utilizes Twitter as a medium to disseminate information. This makes the company use Twitter as a medium to disseminate information. An example is an Internet Service Provider (ISP) company such as Indihome. Through Twitter, users can discuss their complaints and satisfaction with Indihome services. A method is needed, namely sentiment analysis to understand whether the textual data includes neutral opinion, negative opinion or positive opinion. So, the authors use the Support Vector Machine (SVM) method in sentiment analysis of Indihome service user opinions on Twitter, with the aim of getting a sentiment classification model using SVM and to find out how much accuracy is produced by the SVM method applied to sentiment analysis and to find out how satisfied Indihome service users are based on Twitter. After testing with the SVM method the results are 91% accuracy. Precision 51% Recall 75% and F1-Score 59%.

Infrastructure is one of the important elements which supports the economic development of a country. Increasing the quality of infrastructure will also increase the level of the economy of a country. Infrastructure development also provides easy access for goods distribution or services throughout Indonesia and facilitates connectivity between communities. Besides, parallel infrastructure development is also needed to avoid economic inequality between regions in Indonesia. The development of social media in Indonesia has made people easier to convey their responses regarding the performance of government which one of them is related to infrastructure development. One of the types of social media that is currently popular among Indonesian people is Twitter. Considering that the popularity of Twitter in Indonesia, sentiment analysis as an application from text mining was conducted. We use support vector machine (SVM) as an analytical method to make a prediction model for classification of data opinion on Twitter. Some opinions which derived from it were classified into two groups, negative and positive opinions. The data was unbalanced data between negative class and positive class. Therefore, to handle this, a boosting algorithm on the SVM method was added. As a result, sentiment analysis of infrastructure development in Indonesia using the boosting support vector machine (BSVM) method gives an accuracy of 95.43%, a specificity of 81.67% and sensitivity of 97.54%. The classification results were made in the form of web framework application with visualization in the form of wordcloud and streamgraph graphics that were displayed interactively with a web-based application R Shiny.

Madura is one of the most popular tourist destinations in Indonesia. It offers a variety of interesting attractions such as beaches, islands and a unique traditional culture. However, like other tourist destinations, Madura tourism also receives different opinions from the community. Some public opinions can be in the form of positive, negative and neutral feelings towards Madura tourism. To find out the public opinion, it is necessary to conduct sentiment analysis using the Support Vector Machine (SVM) method. In this analysis, data was collected from various social media platforms discussing Madura tourism. Reviews from the community are divided into three categories into positive, negative, neutral, which are processed by the SVM method. The purpose of using the Support Vector Machine method for sentiment analysis of public opinion about Madura tourism where the results can be analysed to provide recommendations for improvements to Madura tourism that are useful for improving the quality of Madura tourism based on public sentiment. From the test results carried out using K = 5 fold cross validation, positive sentiment is obtained from as many as 192 tweets and an accuracy of 92.592% using Confusion Matrix.

Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. The results showed that in the both of considered methods, the models including discharges of one, two and three days ago had higher accuracy in verification step and the accuracy of models decreased with increasing discharge values. Comparing the performance of GP and SVM methods indicated that, however the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than SVM method with R=0.976 and RMSE=1.80 (m3/s), but the SVM is easier than GP method. Thus, the SVM method can be used as an alternative method in forecasting daily river discharge.

Accuracy of data and information on various types of land use and land cover (LULC) is crucial in watershed management planning. The development of remote sensing technology in predicting LULC has been widely used, such as the Support Vector Machine (SVM) and Random Trees (RT) methods. Both methods are part of the model of machine learning and belong to the category of supervised learning. This method can predict, study, and recognize data patterns from the results of training data. The aim of this research is the use of SVM and RT methods for the identification of LULC. Based on the SVM method analysis results, the forest area is 211.61 km2, coffee plantations are 32.92 km2, shrubs are 16.85 km2, built-up/bare land is 3.40 km2, the water body is 3.17 km2, and cloud/shadow is 6.79 km2. While the RT method obtained a forest area of 215.51 km2, coffee plantations covering an area of 29.32 km2, shrubs are covering an area of 16.32 km2, built-up/bare land surrounding an area of 7.22 km2, water body covering an area of 2.62 km2, and cloud/shadow covering an area of 3.75 km2. Overall, the percentage accuracy of the SVM method of 87%, and the RT of 86%. The success rate of modeling SVM (AUC = 0.73) and RT (AUC = 0.75) based on the results of data validation using the area under the receiver operating characteristic curve (AUC) was considered fair good.

Sirekap is a mobile application that was built to help the public monitor and oversee the development of the 2024 elections held in Indonesia. The research aims to apply the Support Vector Machine algorithm in analyzing sentiment about the use of the Sirekap application in 2024. The Support Vector Machine method is used to classify user sentiment into classes, namely positive, negative, and very negative. The amount of data used is 15,000 data sourced from Sirekap application reviews on Google PlayStore, with more detailed research stages including data collection, data preprocessing, data labeling, visualization, word weighting, and testing and analysis. The results show that the Support Vector Machine algorithm provides an accuracy of 88% for the Sirekap 2024 application. These results are expected to help developers to develop further the Sirekap 2024 application in improving the quality of the application and providing better user comfort Based on the results of the sentiment analysis of Sirekap 2024 application users on the Google Play store using the Support Vector Machine (SVM) method, an accuracy rate of 88% was obtained in classifying the sentiment of reviews into positive, negative, and very negative. This shows that the Support Vector Machine method is quite accurate for sentiment analysis of Indonesian text data. Overall, most reviews are very negative with a percentage reaching 76.9%, followed by negative reviews at 12.6%, and the least are positive reviews at 11%.

Everyone's freedom to express their opinions is now poured into a platform known as social media. This platform allows people in the digital world to communicate with each other using the internet. YouTube is one of the most popular social media platforms worldwide. In 2023, the Government, in this case the Ministry of Religious Affairs of the Republic of Indonesia and Commission VIII of the House of Representatives have approved the Hajj Travel Cost 1444 H/2023 AD with a range of Rp90,050,637.26 per regular pilgrim. In contrast to the government of the Kingdom of Saudi Arabia, which implemented a policy of reducing the cost of the Hajj package by 30% from 2022. This has caused pros and cons to the hajj cost increase. Public opinion on social media is the focus of this research to conduct sentiment analysis. Sentiment analysis has been developed through various methods, but there are still many challenges to produce accurate sentiment analysis. The challenges include accuracy, binary classification, data sparsity, and polarity shift. One of the challenges in improving accuracy is the focus of this research. In this study, the Support Vector Machine method is applied and Information Gain feature selection is added. The accuracy results obtained in this study are the Support Vector Machine method (87%) and Support Vector Machine combine with information gain feature selection (89%). It can be concluded, the support vector machine method combined with information gain feature selection proves an increase in accuracy by 2%.

The specifications of the forests in Kalimantan are part of the vegetation type which is characteristic of dry lowland forests that are rich in biodiversity. The Kalimantan forest area is known as an area that has the highest vascular biodiversity in the world. Until 2014, the rate of forest loss as a source of biodiversity was always high every year. The National Polar-Orbiting Suomi Partnership (Suomi NPP) is a new generation of weather satellite imagery from NASA which currently continues to develop application algorithms for environmental monitoring. The support vector machine (SVM) method is a one-class learning machine method with artificial neural network methods that can recognize patterns from input or examples given and also include supervised learning. This study aims to analyze the effect of each parameter on the SVM method and the combination that can produce the highest accuracy, and analyze the ability of the SVM method for making hotspot maps based on the number of forest fire areas in East Kalimantan. The results of the study showed that the Support Vector Machine (SVM) method on the spectral data produced a total of 85%.

In this paper, we develop a low-cost portable spectrometer based on Support Vector Machines (SVM) method. The purpose of the spectrometer development is for halal food research project. The portable spectrometer uses various light-emitting diodes (LEDs), light sensor and Arduino microcontroller. The initial research of the spectrometer is testing various oil, such as palm oil, olive oil, sesame oil, soybean oil, and lard. The waveform analysis of each oil can be distinguished by spectroscopy method. But, by using Support Vector Machines (SVM) method the various oil can be distinguished and classified better. The data measurement, classification, and graphical interface result show on the interface of Raspberry Pi based on Python 2.7. Thus, the spectrometer can be used for lard detection.

In recent times, Coronavirus Disease 2019 (COVID-19) has become a growing concern which has taken the world by surprise. Early detection of this virus can be used to save millions of lives. In this study, a Support Vector Machine (SVM) method is proposed for the identification and classification of COVID-19 as an early diagnostic method to help clinicians and doctors to accurate distinguish COVID-19 from SARS-CoV-2. A discrete wavelet transform (DWT) algorithm was used to extract features, while SVM was used to classify the extracted features. For the performance evaluation, metrics such as sensitivity (Sens), specificity (Spec), accuracy (Acc), and F -score metrics were used. A detection rate of 98.2 % was achieved using the proposed SVM method. Finally, the performance of the SVM method is compared to that of current methods, and it is discovered that the SVM method outperforms them.

Insurance Administration Organization, which can be used by all people. However, this organization has received various criticisms from the public through social media, namely Twitter. This study aims to analyze public sentiment about the Indonesian Social Insurance Administration Organization on Twitter. The method used in this research is the Naive Bayes Classifier (NBC) method and uses the Support Vector Machine (SVM) method as a comparison. The amount of data used was 12,990 tweets with a data collection period from September 14, 2019 - February 18, 2020. The study compared the two classifier models built, namely the classifier model with two sentiment classes and four sentiment classes. The accuracy results show that the SVM method has a better accuracy value than the NBC method. SVM has an accuracy value of 63.60% and 82.77% for the two sentiment classes in the four sentiment classifier model. The tweet classification results show that the public's conversation about the Indonesian Social Insurance Administration Organization on Twitter has a negative polarity value tendency.

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

A new kind of surface material is found and defined in the Balmer–Kapteyn (B-K) cryptomare region, Mare-like cryptomare deposits (MCD), representing highland debris mixed by mare deposits with a certain fraction. This postulates the presence of surface materials in the cryptomare regions. In this study, to objectively verify the existence of the MCD in the cryptomare regions, based on the Chang’E-2 microwave radiometer (MRM) data, the support vector machine (SVM) method was adopted, where the K-means algorithm was used to optimize the training samples and the random forest algorithm was used to select the proper band features. Finally, the extracted MCD is identified with the datasets including Lunar Reconnaissance Orbiter Wide Angle Camera, Diviner, and Clementine UV–VIS. The main findings are as follows: (1) Compared to the range outlined via the TB counter, the range of the MCD is objectively extracted using the SVM method in the B-K cryptomare region, which is reasonably indicated by the FeO abundance, TiO2 abundance, and rock abundance distributions. (2) The MCDs were extracted in the Dewar, Lomonosov–Fleming (L-F), and Schiller–Schickard (S-S) regions, indicating that the MCDs are widely distributed in the cryptomaria. (3) The presence of MCDs is concentrated in a limited region, accounting for 64.9%, 52.3%, 76.4%, and 64%, respectively, in the range of Dewar, L-F, S-S, and B-K regions identified using the optical data. The occurrence of the MCD gives a new understanding of the surface evolution in the cryptomare regions.