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All Journal Aptisi Transactions on Management Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi International Transactions on Education Technology (ITEE) Journal of Computer Science and Technology Application
Williams, Tane
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Social Sciences Other

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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.
Penggunaan Data Analistik dalam Strategi Pemasaran untuk Mempertahankan Loyalitas Pelanggan: Use of Analytical Data in Marketing Strategy to Maintain Customer Loyalty Sibarani, Blasius Erik; Setiawan, Sandy; Hadi, Tan; Williams, Tane; Mkhize, Thabo
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 3 No 1 (2024): September
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v3i1.592

Abstract

Penelitian ini mengkaji penggunaan data analistik dalam strategi pemasaran untuk mempertahankan loyalitas pelanggan. Di era digital yang terus berkembang, perusahaan semakin bergantung pada data analistik untuk memahami perilaku pelanggan dan menyusun strategi pemasaran yang efektif. Melalui analisis data yang mendalam, perusahaan dapat mengidentifikasi tren, preferensi, dan kebutuhan pelanggan secara lebih akurat. Penelitian ini mengeksplorasi berbagai teknik dan alat data analistik yang digunakan dalam pemasaran, seperti segmentasi pelanggan, analisis sentimen, dan personalisasi kampanye. Hasil dari penelitian ini menunjukkan bahwa penerapan data analistik dalam strategi pemasaran tidak hanya meningkatkan kepuasan pelanggan tetapi juga memperkuat hubungan jangka panjang antara perusahaan dan pelanggan. Studi kasus dari beberapa perusahaan terkemuka di industri menunjukkan bahwa integrasi data analistik dalam strategi pemasaran berkontribusi signifikan terhadap peningkatan loyalitas pelanggan. Kesimpulannya, data analistik merupakan elemen krusial dalam pengembangan strategi pemasaran yang efektif dan berkelanjutan, yang pada akhirnya berdampak positif pada retensi pelanggan.
International Business Expansion Strategies: A Data-Driven Approach with IBM SPSS Williams, Tane; Kallas, Evelin; Garcia, Emily; Fitzroy, Arabella; Sithole, Precious
APTISI Transactions on Management (ATM) Vol 8 No 2 (2024): ATM (APTISI Transactions on Management: May)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v8i2.2275

Abstract

This paper presents a structural framework to enhance time management proficiency within dynamic work environments. The framework integrates prioritization techniques, task scheduling methods, delegation strategies, and technology utilization to optimize time allocation and productivity. The methodology involves the application of the Eisenhower Matrix, Pareto Principle, and time-blocking techniques, supported by case studies in diverse professional settings. Results indicate a 20% improvement in project completion times, a 25% reduction in project turnaround time, and a 30% increase in project visibility. These findings underscore the framework’s effectiveness in enhancing time management and achieving long-term success. Implications include recommendations for continuous refinement and integration of emerging technologies.
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
Revolutionizing Logistics Business Models through Big Data and Blockchain: A Business Model Canvas Analysis Ainun Mutiara, Indah; Febriansyah, Yusuf; Kamal, Mustofa; Zainum Ikhsan, Ramzi; Williams, Tane
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i2.28

Abstract

The world is moving quickly towards automation and digitalization in the modern era. This change is becoming crucial to corporate competitive strategies, especially in the logistics industry. The use of data in organizational decision-making is an essential aspect of this digital and automated environment. Several business sectors are implementing Big Data and Blockchain technologies to improve organizational capabilities by developing effective business processes. This inexorably affects the development of new business models that fit the changing global business landscape. The Business Model Canvas (BMC) is an effective tool for analyzing internal and external business model changes. A SWOT analysis of these business model transformations is necessary to explain the new business process changes further. First, the analysis shows that for businesses to function at their best, current technological advancements—particularly in Big Data and Blockchain—will continue to disrupt them. Second, there have been significant internal and external changes to intra- and inter-organizational relationships due to the implementation of Big Data and Blockchain. Thirdly, the benefits of Blockchain and Big Data technologies for business, especially logistics, can be further explained by SWOT analysis.
Co-Authors Ainun Mutiara, Indah Anderson, James Davies, J George Davis, Christopher febriansyah, yusuf Fitzroy, Arabella Garcia, Emily Hadi, Tan Juanda Kallas, Evelin Melinda, Vivi Mkhize, Thabo Mustofa Kamal Nizirwan Anwar Setiawan, Sandy Sibarani, Blasius Erik Sithole, Precious Zainum Ikhsan, Ramzi