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All Journal Computatio : Journal of Computer Science and Information Systems Aptisi Transactions on Management International Transactions on Education Technology (ITEE) Senarai: Journal of Islamic Heritage and Civilization
Anderson, James
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Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Social Sciences

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Enhancing Waste-to-Energy Conversion Efficiency and Sustainability Through Advanced Artificial Intelligence Integration Melinda, Vivi; Williams, Tane; Anderson, James; Davies, J George; Davis, Christopher
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i2.597

Abstract

Artificial intelligence (AI) has emerged as a pivotal tool in optimizing waste-to-energy conversion technology, addressing critical environmental issues while promoting sustainable energy sources. This study delves into the multifaceted role of AI in enhancing the efficiency and effectiveness of waste-to-energy processes. By leveraging AI, significant improvements can be achieved in automated waste sorting, process monitoring, and energy production forecasting. The integration of AI into these domains not only streamlines operations but also enhances the accuracy of data management, analysis, and processing. This results in a more efficient conversion of waste into energy, mitigating adverse environmental impacts and fostering sustainable energy practices. The research highlights the practical applications of AI in optimizing the entire waste-to-energy workflow, underscoring its potential to revolutionize this sector. Moreover, the study addresses the inherent challenges and discusses future prospects for AI implementation in waste-to-energy technologies. Through comprehensive analysis and case studies, the findings reveal that AI can significantly contribute to reducing environmental footprints and promoting a circular economy. This exploration provides valuable insights into how AI-driven innovations can lead to more sustainable and efficient waste management and energy production systems, paving the way for future advancements in this critical field.
Advanced Predictive Models for the Startup Ecosystem Using Machine Learning Algorithms Febiansyah, Hidayat; Rahardja, Untung; Adiyarta, Krisna; Anderson, James; Kanivia, Aan
APTISI Transactions on Management (ATM) Vol 8 No 3 (2024): ATM (APTISI Transactions on Management: September)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v8i3.2345

Abstract

The startup ecosystem, characterized by its dynamism, presents significant challenges in predicting its future trajectory. Traditional analytical methods often fall short in comprehensively addressing the myriad factors that shape this ecosystem. This research aims to enhance the predictability of trends within the startup landscape by integrating the Technology Acceptance Model (TAM) with the advanced Random Forest algorithm. While existing literature has extensively explored the challenges startups face and the nuances of stakeholder interactions, the integration of TAM's constructs with key empirical attributes, specifically Investment Dynamics, Startup Metrics, Stakeholder Interactions, Entrepreneurial Challenges, and Technological Infrastructure, is a pioneering approach. Drawing from a comprehensive dataset that spans a diverse array of startups, this study operationalizes TAM's constructs in conjunction with the specified attributes. The subsequent application of the Random Forest algorithm offers a novel predictive methodology. Initial results highlight the superior predictive capabilities of this integrated model compared to traditional approaches. The findings provide insights into the intricate relationship between technological perceptions, as framed by TAM, and the tangible realities of the startup domain. The fusion of TAM with state-of-the-art machine learning signifies a groundbreaking direction in startup ecosystem research. This innovative approach offers stakeholders an enhanced analytical tool, ensuring more informed decision-making and a deeper grasp of the multifaceted nature of startup ecosystems.
Applying Data Science to Analyze and Improve Student Learning Outcomes in Educational Environments Anwar, Nizirwan; Juanda; Anderson, James; Williams, Tane
International Transactions on Education Technology (ITEE) Vol. 3 No. 1 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v3i1.679

Abstract

This study explores the application of data science to analyze and improve student learning outcomes within educational environments, responding to the increasing demand for data-driven approaches in education. The objective is to identify key performance indicators that influence learning success and to develop predictive models that support personalized academic interventions. The research applies a mixed-method approach, combining quantitative data analysis from student records and qualitative insights gathered from educational stakeholders. Machine learning algorithms and statistical models are employed to identify patterns and relationships within large datasets, helping to pinpoint factors such as attendance, engagement levels, and assessment performance that most strongly correlate with learning outcomes. Results indicate that predictive models can effectively forecast student performance, allowing educators to proactively support at risk students and tailor learning experiences to individual needs. Furthermore, the findings demonstrate that integrating data science tools into educational decision-making can improve not only academic outcomes but also institutional strategies for student success. This study concludes that data science offers substantial potential for enhancing learning environments, enabling a more responsive and personalized education system that supports each student’s unique journey towards academic achievement
Post-Arab Spring Tunisia: Socio-Political Dynamics and Institutional Reform in a Post-Revolution Era Fawwaz, Zayyan Malik; Awang Junaidi, Awangku Hafiz; Clarke, Jessica; Anderson, James; Amir, Raihan Zulkarnain
Journal of Islamic Heritage and Civilization Vol. 1 No. 4 (2025): Islamic Philosophy, Theology, and Civilization
Publisher : Tunas Harapan Ummat Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.0501/senarai.2025.1.4.185-203

Abstract

This article explores Tunisia’s transformation a decade after the Arab Spring in the social, political, legal, and educational fields. The methods used include qualitative descriptive analysis and social history. Data was collected through heuristic methods, utilizing online sources about Tunisia, such as scientific articles, journalistic reports, dissertations, and reports from international institutions. This article demonstrates that the transformation in Tunisia’s socio-political, legal, and educational sectors is not only dynamic, reflecting the principles of a modern and democratic state, but also indicates a tendency to strengthen the stability of both the government and the state. Tunisia implemented new constitutional reforms in 2022 and transformed the education sector by improving both the quality and infrastructure of education. Based on these find- ings, the article concludes that a country’s transformation, including that of Tunisia, is determined by the dynamics of its socio-cultural, political, legal, and educational structures.
Sistem Pendeteksi Pola Pembelian Konsumen Menggunakan Algoritma Apriori Anderson, James
Computatio : Journal of Computer Science and Information Systems Vol. 7 No. 1 (2023): Computatio: Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v7i1.16196

Abstract

Sistem pendeteksi pola pembelian konsumen menggunakan algoritma apriori adalah sebuah aplikasi berbasis web yang dibuat untuk mencatat transaksi antara toko dengan konsumen, kemudian memberikan pola pembelian konsumen berdasarkan data transaksi yang tersimpan pada sistem. Dari pola pembelian konsumen yang dihasilkan, aplikasi akan memberikan rekomendasi pengadaan barang untuk kelompok item tersebut. Aplikasi ini dibuat dengan menggunakan bahasa pemrograman Python, dengan metode hitung Apriori, Double Moving Average, dan Economic Order Quantity (EOQ). Pengujian aplikasi dilakukan melalui 3 tahapan yaitu Unit Testing, Internal Testing, dan User Acceptance Testing. Dengan dibuatnya aplikasi ini, diharapkan proses penentuan item-item yang harus diperhatikan dan cara pengadaan barang untuk mendapatkan biaya yang optimal dapat lebih mudah, sehingga toko Raja Jaya dapat terbantu.
Co-Authors Adiyarta, Krisna Amir, Raihan Zulkarnain Awang Junaidi, Awangku Hafiz Clarke, Jessica Davies, J George Davis, Christopher Fawwaz, Zayyan Malik Febiansyah, Hidayat Juanda Kanivia, Aan Melinda, Vivi Nizirwan Anwar Untung Rahardja Williams, Tane