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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) Focus: Jurnal of Law International Journal of Science Education and Cultural Studies Jurnal Pengabdian Masyarakat Sultan Indonesia Journal of Applied Artificial Intelligence in Education
Adelakun, Najeem Olawale
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Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Other

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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.
Strengthening Civic Awareness and Religious Moderation among Millennials Through a Participatory Approach Based on Local Wisdom Rosyad, Ali Miftakhu; Hidayat, Taufik; Zaenudin, Zaenudin; Khoiriyah, Affy; Adelakun, Najeem Olawale
Jurnal Pengabdian Masyarakat Sultan Indonesia Vol. 3 No. 1 (2026): Abdisultan
Publisher : Sultan Publsiher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/abdisultan.v3i1.480

Abstract

The increasing cases of intolerance and radicalism among millennials pose a serious threat to Indonesia's diversity and national unity. This community service program aims to strengthen civic awareness and religious moderation through a participatory approach based on local wisdom among millennials in Indramayu Regency. This study employed a one-group pretest-posttest quasi-experimental design with participatory community engagement approach, involving 135 participants consisting of college students, youth members of religious organizations, and community activists aged 18-35 years. The program was implemented through four stages: initial assessment of civic awareness and religious tolerance attitudes, interactive training based on interfaith dialogue, mentoring for implementing religious moderation actions, and evaluation of attitudinal changes. Paired sample t-test analysis (t = 21.35; p < 0.001) revealed statistically significant increases in the civic awareness index from 52.8 to 91.7 (73.8% improvement) and the religious moderation index from 57.4 to 91.5 (68.4% improvement). The program successfully generated 12 religious moderation movements initiated by participants, including interfaith dialogue forums, anti-hoax campaigns on religious content, and collaborative cross-faith social projects. The distinctive contribution of this study lies in the strategic integration of Indramayu's local wisdom ("ngaji bareng" and "silih asih antar umat") as a culturally rooted medium for simultaneously fostering civic awareness and religious moderation, demonstrating that participatory approaches anchored in indigenous values yield more sustainable outcomes than conventional top-down interventions.
Integrating Artificial Intelligence into Entrepreneurship Education for Engineering Graduates in Nigeria Adelakun, Najeem Olawale; Mande, Samaila
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 1 (2026): March 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i1.297

Abstract

Artificial Intelligence (AI) is rapidly transforming the world of education, innovation and entrepreneurship; in the field of engineering education, AI has become an accelerator and a mediator - helping eliminate the divide between theory and practice, as well as between academic knowledge and that which is applicable in industry. The study examines the possibility of artificial intelligence integration in order to expand entrepreneurial skill, human capital, and innovation systems within Nigerian tertiary institutions. The study explores the effect of cluster formation, enabling environments, workforce retention, and AI adoption on entrepreneurial performance through a mixed-method approach, where quantitative data reveal that AI-enriched learning environments and the use of AI-enhanced learning environments have a strong positive relationship with entrepreneurial performance, and qualitative information indicates that intelligent systems are enabling factors influencing entrepreneurial performance in terms of creativity, employability, and collaboration. The results of the study show that AI-based entrepreneurship education can increase the application of practical problem-solving, intelligent mentoring, and data-based policy design of sustainable development. The research concludes that implementing AI in engineering programs has the potential to revolutionize Nigeria human capital base by eliminating skills shortages and enhancing the growth of an innovation-driven economy.
IMPLEMENTATION CHALLENGES OF DATA PROTECTION LAW IN NIGERIAN HIGHER EDUCATION Rosyad, Ali Miftakhu; Adelakun, Najeem Olawale; Amiarsa, Panji; Kamal, Maulana; Hamamah, Fatin
FOCUS: Jurnal of Law Vol 6 No 2 (2025): Focus: October Edition
Publisher : Faculty of Law Universitas 17 Augustus 1945 Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47685/focus.v6i2.785

Abstract

This study examines the gap between the existence of data protection regulations and their ineffective implementation in Nigerian higher education institutions. Despite the enactment of the Nigeria Data Protection Regulation (NDPR) 2019 and the Nigeria Data Protection Act (NDPA) 2023, universities continue to face challenges in ensuring data security and compliance. This research explores why implementation remains ineffective and how legal, technological, and organizational factors contribute to this condition. Using a socio-legal approach, the study combines normative legal analysis with empirical data from semi-structured interviews. The findings show that weak enforcement, limited institutional capacity, inadequate ICT infrastructure, low legal awareness, and unsupportive organizational culture hinder effective implementation. These factors interact to create systemic barriers to compliance. The study emphasizes the need for stronger enforcement, institutional reform, capacity-building, and technological investment to achieve effective data protection.
Implementation of Deep Learning Approach of Pancasila Education to Increase Critical Thinking Students Rosyad, Ali Miftakhu; Adelakun, Najeem Olawale
International Journal of Science Education and Cultural Studies Vol. 4 No. 2 (2025): ijsecs
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijsecs.v4i2.463

Abstract

Critical thinking is essential in 21st-century higher education, yet traditional Pancasila education often fails to develop this competency. This mixed-method study investigated how deep learning approaches pedagogical strategies emphasizing mastery, problem-solving, and disposition development enhance critical thinking among 120 undergraduate students at Universitas Wiralodra. Students were divided into experimental groups receiving deep learning instruction and control groups receiving traditional methods. Results showed experimental group students achieved substantially higher critical thinking scores with large effect sizes (Cohen's d=1.66, p<0.001). Qualitative findings revealed that problem-based learning, Socratic dialogue, and reflective practices enhanced students' analytical abilities and engagement. The main contribution of this study is demonstrating that contextualized deep learning pedagogies can effectively transform civic education from knowledge transmission to critical competency development in Indonesian higher education settings. Practically, this research provides evidence-based implementation frameworks for educators seeking to enhance critical thinking through authentic problem-solving and collaborative inquiry in Pancasila education.
System Based Artificial Intelligence Adoption in Nigerian Tertiary Education: A Conceptual Governance Model for Learning, Academic Integrity, and Equity Adelakun, Najeem Olawale; Lasisi, Mariam Adenike; Kolawole, Tolani Damilola
Journal of Applied Artificial Intelligence in Education Vol 2, No 1 (2026): July 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i1.713

Abstract

This study explores the adoption of Artificial Intelligence in Nigerian tertiary education institutions from a conceptual governance perspective, specifically on learning outcomes, academic integrity and equity in HEIs. AI is no longer a tool for learning support but is a governance issue that requires policies and guidelines, ethical supervision, and pedagogical incorporation as it becomes more central to academic life. The study employs the narrative review method to integrate the empirical and conceptual evidence and to gain insight into the role of different types of Artificial Intelligence systems and the shifts in student learning practices and responses to the changes in institutional policy (2019-2025). The analysis presents Artificial Intelligence as an ecosystem of technologies and not as a single technology, and outlines the different governance considerations for each system type. The analysis reveals that Artificial Intelligence plays a role in the areas of personalised learning, academic productivity and access to educational resources in tertiary institutions in Nigeria. The benefits have many drawbacks, including academic dishonesty in generative systems, privacy and transparency issues in adaptive systems, and equity and dependency issues in intelligent tutoring systems. The institutional governance has been a gradual process and the students have been slow to take up Artificial Intelligence tools. The paper concludes that the integration of Artificial Intelligence in tertiary education in Nigeria should be structured, supported by a framework for policy development and Artificial Intelligence literacy, curriculum reform, and investment in digital infrastructure should have implications for other similar higher education systems in Sub Saharan Africa.
Co-Authors Amiarsa, Panji Fatin Hamamah, Fatin Khoiriyah, Affy Kolawole, Tolani Damilola Lasisi, Mariam Adenike Mande, Samaila Maulana Kamal Omolola, Samuel Adeniyi Rosyad, Ali Miftakhu Taufik Hidayat Zaenudin Zaenudin