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All Journal Bulletin of Electrical Engineering and Informatics JUTI: Jurnal Ilmiah Teknologi Informasi
Ulima Inas Shabrina
Institut Teknologi Sepuluh Nopember
Computer Science & IT Dentistry Education Electrical & Electronics Engineering Library & Information Science

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OPTIMIZING SENTIMENT ANALYSIS IN EDUCATIONAL YOUTUBE VIDEOS: A COMPARATIVE STUDY OF ROBERTA AND MULTINOMIAL NAIVE BAYES Ulima Inas Shabrina; Muhammad Iskandar Java; Siti Rochimah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 2, July 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i2.a1204

Abstract

YouTube has evolved into a globally influential platform, engaging over 2.1 billion users worldwide and serving as a prominent medium for sharing, consuming, and creating diverse video content. Particularly popular among younger demographics, YouTube stands as a multifaceted hub spanning various genres and has significantly impacted education by providing extensive educational materials, fostering independent learning, and supporting a wealth of educational resources. This research conducts an in-depth investigation into sentiment analysis specifically within the context of educational YouTube videos. Leveraging advanced machine learning techniques, notably RoBERTa, this research conducts a comparative analysis with Multinomial Naive Bayes (MNB). The primary focus is on exploring RoBERTa's adaptability and performance across a spectrum of educational video content, revealing its commendable accuracy of 91.21%, surpassing MNB's accuracy of 79.59%. However, it is observed that RoBERTa's performance is notably affected by smaller datasets, highlighting the critical importance of ample and diverse training data for achieving optimal results. These findings highlight the pivotal role of dataset characteristics and size in developing robust sentiment analysis models, especially with advanced natural language processing methods like RoBERTa.
Attention-enhanced wasserstein GAN for agricultural market data imputation Ulima Inas Shabrina; Riyanarto Sarno; Ratih Nur Esti Anggraini; Agus Tri Haryono
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10549

Abstract

Crop price prediction in ASEAN markets is hindered by incomplete and inconsistent data, making data imputation essential. This study introduces the Wasserstein generative adversarial imputation network with attention (WGAIN+Att) to improve data quality for forecasting. Four configurations—GAIN, GAIN+Att, WGAIN, and WGAIN+Att—were evaluated on rice, corn, and soybean datasets (1961–2023). Results show that WGAIN+Att, particularly when attention is applied across all matrices (x, m, and z), achieved the best imputation performance, minimizing mean absolute error (MAE) and preserving statistical distributions, with optimal results at a 0.1 missing rate and 0.9 hint rate. In predictive tasks, GAIN-based imputations combined with convolutional neural networks (CNN)-long short-term memory networks (LSTM)-gated recurrent units (GRU) models consistently outperformed others in forecasting accuracy, achieving lower MAE and root mean squared error (RMSE). The findings highlight the role of attention in stabilizing imputation and ensuring realistic reconstructions, while also showing that aligning imputation with forecasting objectives improves agricultural price predictions.
Co-Authors Agus Tri Haryono Anggraini, Ratih Nur Esti Muhammad Iskandar Java Riyanarto Sarno Siti Rochimah