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All Journal Andalasian International Journal of Applied Science, Engineering, and Technology International Journal of Informatics, Information System and Computer Engineering (INJIISCOM)
Adelakun, Najeem Olawale
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Deep Learning-Based Sentiment Analysis Using Gated Recurrent Unit Adelakun, Najeem Olawale; Lasisi, Mariam Adenike
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 5 No. 1 (2025): March 2025
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v5i1.217

Abstract

In today’s volatile financial markets, investor sentiment plays a crucial role in shaping market dynamics and influencing investment decisions. Traditional analytical methods often fail to capture the subtle emotional cues embedded in vast amounts of unstructured textual data derived from news articles, social media, and financial reports. This study addresses this challenge by employing a deep learning-based approach using Gated Recurrent Units (GRU) for sentiment analysis, thereby enhancing the accuracy of financial market predictions. The research employs a systematic methodology that begins with data collection from various financial sources. This is followed by rigorous preprocessing, including data cleaning, tokenization, and downsampling to balance sentiment classes. Sentiment labeling and feature engineering, utilizing word embeddings, convert textual data into a format suitable for deep learning. The Gated Recurrent Unit (GRU) model is then trained on these features, and its performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. Results indicate that while the Gated Recurrent Unit (GRU) model effectively captures neutral sentiments, it struggles to accurately classify negative and positive sentiments, highlighting areas for improvement. These findings underscore the potential of GRU-based models in financial sentiment analysis while emphasizing the need for refined techniques to enhance classification accuracy. Future research should investigate hybrid architectures, integrate attention mechanisms, and leverage real-time data to enhance the robustness and comprehensiveness of market forecasting. These insights strongly advocate for ongoing advancements in deep learning strategies to refine sentiment classification and financial prediction models.
Design and Implementation of a Remote Control Based Home Automation System Adelakun, Najeem Olawale; Omolola, Samuel Adeniyi
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 4 No. 1 (2024): March 2024
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v4i1.115

Abstract

This study describes an innovative remote control-based home automation system that uses electronic components such as IC TSOP1738, BC 548, IC CD4017, capacitors, diodes IN4007, resistors, and LEDs to boost household convenience. The essential component, TSOP1738, acts as an infrared receiver, decoding signals from a typical remote control and enabling continuous communication inside the system. The BC 548 transistor acts as a critical switch, regulating current flow to connected appliances. The IC CD4017 enables sequential operation for a more systematic approach to device control. Meticulously constructed circuitry, including capacitors, diodes IN4007, and resistors, ensures optimal performance and stability. LEDs serve as intuitive indicators, offering visual feedback on device status. Extensive testing validates the system's robustness, precision, and dependability. The study investigates real applications that demonstrate the system's adaptability in various household situations, thereby contributing to the progress of home automation technology. This study provides an accessible and efficient solution for modern families, with potential developments in the future to increase automation capabilities and usage on a larger scale.
Repositioning Artificial Intelligence as a Core Enabler of Educational and Entrepreneurial Ecosystems in Nigeria Adelakun, Najeem Olawale; Mande, Samaila
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 8 No. 1 (2027): INJIISCOM: VOLUME 8, ISSUE 1, JUNE 2027 (Online First)
Publisher : Universitas Komputer Indonesia

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Abstract

This study investigates Artificial Intelligence (AI) as a catalyst for integrating Nigeria’s educational and entrepreneurial ecosystems to mitigate engineering graduate unemployment. Employing a structured questionnaire, primary and secondary data were collected to evaluate AI's impact on entrepreneurial clusters, enabling environments, and workforce retention. Findings reveal that AI significantly enhances employability by identifying skill gaps and fostering mentorship. Furthermore, within entrepreneurial ecosystems, AI improves opportunity recognition, resource optimization, and cluster-based innovation. The discussion highlights that AI-driven retention strategies effectively minimize skills attrition by embedding graduates within adaptive, locally relevant enterprises. In conclusion, integrating AI into academic curricula and ecosystem policy frameworks is an urgent strategic imperative to unlock human capital, reduce unemployment, and foster sustainable economic growth in Nigeria.
Co-Authors Lasisi, Mariam Adenike Mande, Samaila Omolola, Samuel Adeniyi