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Isram Rasal
Teknik Informatika, Fakultas Teknologi Industri, Universitas Gunadarma
Computer Science & IT

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SENTIMENT ANALYSIS OF PRODUCT REVIEWS DATA ON TOKOPEDIA BY COMPARING THE PERFORMANCE OF CLASSIFICATION ALGORITHMS Dwi Widiastuti; Isram Rasal; Dessy Wulandari Asfary Putri
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

Social media is a medium where people can express their opinion on something. Opinion mining or sentiment analysis, which is studying people's sentiments towards certain entities. This can be used by companies to find out people's responses to a sales product. Sentiment analysis has received a lot of attention in recent years. Sentiment analysis is one of the main tasks of NLP (Natural Language Processing). In this paper, sentiment polarity categorization becomes the basis for sentiment analysis problems in product reviews. A general process for sentiment polarity categorization is proposed with a detailed description of the process. The data used in this study is an online product review collected from the Tokopedia application. Classification is carried out on sentence level categorization and star rating level categorization. There are three models used to compare the classification process, namely SVM, Random Forest, and Naïve Bayes models. The results of this research paper are in the form of a comparison of the performance of the three models against the polarity categorization of product review sentiment on Tokopedia
COMPARISON OF ACCURACY PERFORMANCE K-NEAREST NEIGHBOR ALGORITHM AND SUPPORT VECTOR MACHINE FOR PREDICTING DEATH IN CONGESTIVE HEART FAILURE Isram Rasal; Dwi Widiastuti; Desy Wulandari Asfary Putri
INFOKUM Vol. 10 No. 5 (2022): December, Computer and Communication
Publisher : Sean Institute

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Abstract

Congestive heart failure or Congestive Heart Failure (CHF) is the number one cause of death in the world. There are approximately 5.7 million adults with heart failure in the United States and 550,000 new cases are diagnosed each year. This has encouraged a lot of research on heart failure, one of which is using the Machine Learning method to predict death from heart failure early. From these problems, the authors will conduct Machine Learning research using two different algorithm models, namely K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). These two models will predict death due to heart failure. The dataset regarding the factors for diagnosing heart failure can be accessed widely and freely on the Kaggle website which is divided into two, namely data training and data testing then analysis and prediction are carried out, so that information is obtained in the form of an accuracy rate in predicting death in heart failure. Using this function also produces the accuracy of each model on the data that has been trained. Data taken were 299 patient data with 13 features or attributes, then divided into 239 training data and 60 test data. The value obtained is an accuracy of 85%. The accuracy obtained is more than 80% of the total data tested so that it can be used or implemented to classify heart failure.
Co-Authors Dessy Wulandari A.P Desy Wulandari Asfary Putri Dwi Widiastuti, Dwi