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All Journal Journal of Information Systems and Informatics
Echobu, Faith O
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Computer Science & IT

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Detection of Hate Speech Code Mix Involving English and Other Nigerian Languages Ndabula, Joseph Nda; Olanrewaju, Oyenike Mary; Echobu, Faith O
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.595

Abstract

Hate speech is a recurrent event and has become a cause for global concern. The proliferation of hate speech has recently become prevalent, breeding room for violence and discrimination against specific individuals or groups. In Nigeria, message masking (use of language-mix) has become the new normal, especially in disseminating hateful and inciting comments. Hence, there is a need to curb the spread over social media. Therefore, this research focuses on detecting hate speech on social media with a code-mix of English, Pidgin and any of the three major Nigerian languages (Hausa, Igbo and Yoruba). The research used two machine learning algorithms: Support Vector Machine (SVM) and Random Forest (RF). Data were collected from tweets on the EndSARS protest and the 2023 Nigerian elections. The major features were extracted, and the text was converted into vectors using TF-IDF and Bag-of-words (BoW), which were used to train and test the model. The result showed that SVM performed better in classifying hate speech than RF on both TF-IDF and BoW features, averaging 93.43% for accuracy, 93.70% for precision, 93.43% for recall, and 93.57% for F1-score.
Prediction of Forex Prices on USD/NGN Using Deep Learning (LSTM and GRU) Techniques Olanrewaju, Mary O; Luka, Stephen; Echobu, Faith O
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.606

Abstract

The goal of the project is to develop a model to forecast the Foreign Exchange (FOREX) prices of United State Dollar to Nigerian Naira (USD/NGN), utilizing two machine learning algorithms, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). These were chosen for this study because they have been found to be effective in previous studies that have been examined. The principles of machine learning and its applications, as well as the many machine learning techniques and algorithms will be covered in this study. Additionally, various extraction methods that will be used in the study will be presented. Data from the Investing.com dataset would be retrieved for this study's purpose and divided into training and test sets. Using the two machine learning techniques previously mentioned, the model would be trained and tested. Then, to measure the model's performance in terms of accuracy and precision, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error would be utilized. The results obtained showed that, GRU performed better than LSTM with a 0.950 Test R2 score and an adjusted R2 score of 0.122. The RMSE is way lower than LSTMs at 0.105 and MAE is even lower at 0.950.
Co-Authors Luka, Stephen Ndabula, Joseph Nda Olanrewaju, Mary O Olanrewaju, Oyenike Mary