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Yusra
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT

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Klasifikasi Sentimen Masyarakat di Media Sosial Twitter terhadap Calon Presiden 2024 Prabowo Subianto dengan Metode K-NN Avaldy Rahmat Rivita; Yusra; Muhammad Fikry
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.890

Abstract

The 2024 Republic of Indonesia Presidential Election is a democratic stage to determine the President of the Republic of Indonesia and Vice President of the State of Indonesia for the 2024-2029 period which is scheduled to take place on Wednesday, 14 February 2024. This election is the fifth direct presidential and vice presidential election in Indonesia. Several parties have currently nominated or selected their presidential candidates for the 2024 presidential election. Three presidential candidates have emerged, namely Prabowo Subianto, Ganjar Pranowo, and Anies Baswedan. Based on a survey, Prabowo Subianto is the presidential candidate (capres) with the highest electability compared to other competitors. The society's view of the 2024 presidential candidate, especially Prabowo Subianto, has raised many pros and cons. Society's view can be seen on social media, like one of  this is the Twitter. This study aims to classify public sentiment towards the Presidential Candidate (capres) Prabowo Subianto on Twitter. The amount of data used is 2100 tweets which are collected based on the keywords "Presidential Candidate" and "Prabowo Subianto". The application of the K-Nearest Neighbor (K-NN) method with weighting in the form of TF-IDF and Feature Selection in the form of Threshold will be implemented using Google Colab. Based on the results of testing the K-NN method using the confusion matrix at seven K values, namely (3,5,7,9,11,13,15) with the comparisons used 70:30, 80:20, 90:10 the highest accuracy was obtained at K = 5 at the ratio of training data and test data 80:20.
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Ancaman Resesi Ekonomi 2023 dengan Metode K-Nearest Neighbor Dimas Ferarizki; Yusra; Muhammad Fikry; Febi Yanto; Fitri Insani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 2 (2023): Oktober 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i2.1306

Abstract

A recession is a decline in overall economic activity, this is considered a phase of significant and sustainable economic decline in various sectors and economic indicators. The threat of a recession in 2023 has become a topic of discussion in many countries, including Indonesia. This happens because Indonesia is threatened as a country affected by a recession due to weakening economic activity in the real sector. This sentiment classification research aims to analyze public opinion and opinion regarding the issue of recession news in 2023 which is conveyed via the social media platform Twitter. This research aims to understand whether these opinions fall into the category of positive sentiment or negative sentiment. Apart from that, this research also aims to measure the level of accuracy in classifying these sentiments into appropriate classes. This research has several main processes starting from data collection then manual data labeling, text processing, feature weighting (TF-IDF), Thresholding feature selection and K-Nearest Neighbor method classification. Based on the classification results using a testing model from a total of 1000 comment data divided between 596 positive class data and 404 negative class Twitter data regarding the threat of recession in 2023, the highest accuracy results were obtained at 85% at a value of k = 3 using the 90:10 comparison model training and testing data
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Ganjar Pranowo dengan Metode Support Vector Machine Syaiful Azhar; Yusra; Muhammad Fikry; Surya Agustian; Iis Afrianty
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1537

Abstract

The classification of public sentiment towards Ganjar Pranowo on Twitter can provide insights into his popularity, support, or criticism. This research aims to classify public sentiment towards Ganjar Pranowo on Twitter using the Support Vector Machine (SVM) method. The research data consists of 4000 tweets collected from Twitter. After undergoing preprocessing, these tweets are classified using SVM into positive or negative classes. The classification method is optimized to produce the most optimal model by testing the influence of feature selection stages and SVM parameter tuning. The data is divided into 80% training (TRAIN_SET) and 20% testing (TEST_SET). The optimal model is validated using 10% of the randomly selected TRAIN_SET for validation data. Sixteen experiments are conducted to explore the optimal model, with the highest validation results (top rank 4 models) tested on the TEST_SET, yielding F1-scores of 84.13%, 84.13%, 84.13%, and 84.13% for experiment IDs 1, 7, 14, and 16, respectively. In this research, SVM proves to be sufficiently effective in classifying sentiment-related tweets about Ganjar Pranowo on Twitter
Co-Authors Avaldy Rahmat Rivita Dimas Ferarizki Febi Yanto Fitri Insani Iis Afrianty Muhammad Fikry Surya Agustian Syaiful Azhar