DOI: https://doi.org/10.14710/j.gauss.15.1.46-56

KLASIFIKASI SENTIMEN KASUS ONLINE TRADING BINOMO PADA TWITTER MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)

*Elva Nadia Nugroho  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Tatik Widiharih  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Arief Rachman Hakim  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2026 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

Investment in futures trading and technology is growing in Indonesia, making many domain sites of online trading companies appear that are easy for everyone to access. The Binomo app is so viral among the public because of an ad that displays professional traders who can earn 1000 USD a day without leaving their homes. Binary options trading ultimately causes losses for some people, allegedly caused by affiliates. The Binomo case is widely discussed on social media, especially Twitter. Tweets displaying public opinion on Twitter can be used for sentiment analysis and categorizing public opinion on the Binomo case. 2.686 tweets were collected by Twitter scraping between January 1 and April 1, 2022. 1.519 tweets were left after pre-processing. The data were processed using the Convolutional Neural Network algorithm with the Word2Vec method to determine their accuracy and identify topics often discussed by the public on Twitter. A CNN model with 70% training data, 3, 4, 5, kernel sizes, 4 batch sizes, 30 epochs, and Adam optimizers was used to build the classification in this research. The accuracy value obtained from the performance evaluation of the Convolutional Neural Networ model research was 89%.

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Keywords: Binomo case; Twitter; Sentiment Analysis; Convolutional Neural Network; Word2Vec

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