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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Journal of Computer Networks, Architecture and High Performance Computing
Akhmad Mustolih
Universitas Amikom Purwokerto
Computer Science & IT Education

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Sentiment Analysis Motorku X Using Applications Naive Bayes Classifier Method Akhmad Mustolih; Primandani Arsi; Pungkas Subarkah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i2.24864

Abstract

The rapid development of technology has brought convenience to humans in their daily lives. The continuously evolving technology generates large amounts of data. Data can provide valuable information if processed effectively. The Motorku X application is one of the innovations created by Astra Motor to facilitate consumers or potential customers in servicing and purchasing motorcycles. The Motorku X application generates review data every day. These review data can be utilized for future application development. To make the most of the reviews, sentiment analysis is one of the techniques used to process the review data. Sentiment analysis is a method to measure consumer sentiments in terms of positive or negative reviews. The algorithm used in this research is the Naïve Bayes classifier. One of the advantages of Naïve Bayes is its ability to work quickly and efficiently in terms of computational time. The research consists of several stages: data collection, data labeling, pre-processing, data splitting, tf-idf weighting, implementation of Naïve Bayes classifier, and evaluation of the results. The data comprises 1000 reviews divided into two classes: positive class (number) and negative class (number). The research was conducted with three scenarios of training and testing data sharing: 90%:10%, 80%:20%, and 70%:30%. The best results were achieved with the 90%:10% ratio, with an accuracy of 76%, precision of 76%, and recall of 97%.
Sentiment Analysis of Twitter Cases of Riots at Kanjuruhan Stadium Using the Naive Bayes Method Bryan Jerremia Katiandhago; Akhmad Mustolih; Wachyu Dwi Susanto; Pungkas Subarkah; Chendri Irawan Satrio Nugroho
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i1.2196

Abstract

Sentiment analysis is a process carried out to analyze opinions, sentiments, judgments, and emotions from the riot case at the kanjuruhan stadium. The purpose of this research is to find out public opinion about the tragedy that is currently happening at the Kanjuruhan Stadium. the data was obtained from social media Twitter using the Twitter API, then after that, an analysis was carried out. data from the results of the analysis will be classified using the Naive Bayes method. The classification process is divided into 7 (seven) stages, namely Crawling, Cleansing, pre-processing, labeling, classification, data training, and data testing. In the labeling process, data is classified into 2 (two) classes, namely the positive class and the negative class. The data obtained before the preprocessing process was 1963 tweets, after the preprocessing the data obtained was 1001 tweets. The data will be trained and tested using the naive Bayes classification method. classification results obtained precision values of 82% for negative data and 65% for positive data, recall values obtained 74% for negative data and 75% for positive data, F1-score values obtained 78% for negative data and 70% for positive data, while accuracy value obtained 74%.
Co-Authors Bryan Jerremia Katiandhago Chendri Irawan Satrio Nugroho Primandani Arsi Pungkas Subarkah Wachyu Dwi Susanto