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All Journal Journal of Computer System and Informatics (JoSYC) eCo-Fin
Adelia, Dila
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering

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Election Hoax Detection on X using CNN with TF-RF and TF-IDF Weighting Features Adelia, Dila; Astuti, Widi; Lhaksmana, Kemas Muslim
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5778

Abstract

X social media is a microblogging platform for sharing brief thoughts and trends. It has become a focal point for expressing political views. The increased political engagement on X social media has facilitated the swift and extensive sharing of ideas. Still, it also brings the risk of spreading false information and hoaxes that can manipulate public opinion. Preventing fake news on social media is crucial because it can influence election outcomes and social stability. For example, X social media has been used during elections to spread hoaxes, such as false claims of vote tampering or misleading information about candidate qualifications. This study implements a Convolutional Neural Network (CNN) due to its advantages in recognizing complex patterns and achieving high performance in tasks like classification. The dataset used in this study consists of 2,670 tweets. The dataset is divided into three subsets: 60% for training, 20% for testing, and 20% for validation. It also uses Term Frequency Relevance Frequency (TF-RF) and Term Frequency Inverse Document Frequency (TF-IDF) weighting features to improve accuracy in detecting fake news. This study compares the TF-RF and TF-IDF weighting features using the CNN classification method on the topic of the 2024 election. The testing results indicate that both TF-RF and TF-IDF achieved similar overall performance, with TF-RF slightly excelling in recall and F1-score. At the same time, TF-IDF showed a marginally higher precision.
Pengaruh NPM, ROA, ROE, dan Ukuran Perusahaan Terhadap Harga Saham Perusahaan Yang Tergolong Dalam Indeks LQ45 Adelia, Dila; Elly, Moh. Iskak; Wilamsari, Feni
eCo-Fin Vol. 7 No. 2 (2025): eCo-Fin
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/ef.v7i2.2420

Abstract

This study aims to examine the effect of NPM, ROA, ROE and Company Scale on the share price of companies classified in the LQ45 index during the 2019-2023 period. The method used in this research is panel data regression analysis using quantitative methods. The total research subjects include 45 companies. The sample was selected using purposive sampling and 18 companies were observed for 5 years, so the total information tested was 90 information. The information testing method used in this study includes statistical review, mean difference test, and conjecture difference test where information processing is assisted by using EViews software version 13. The results of this study indicate that ROA has a significant positive effect on stock prices (t = 2.717456 sig = 0.0083) while NPM, ROE, and company size have no effect on stock prices. simultaneously the four variables explain 54.02% of stock prices (Adjusted R square = 0.540264).
Co-Authors Elly, Moh. Iskak Kemas Muslim Lhaksmana Widi Astuti Wilamsari, Feni