Article Per Year (5 Year)
p-Index From 2021 - 2026
2.426
P-Index
This Author published in this journals
All Journal ANDHARUPA Aptisi Transactions on Management Aptisi Transactions on Technopreneurship (ATT) IAIC Transactions on Sustainable Digital Innovation (ITSDI) International Journal of Engineering, Science and Information Technology Startupreneur Business Digital (SABDA Journal) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Prosiding Dedikasi: Pengabdian Mahasiswa Kepada Masyarakat Bohr: Jurnal Cendekia Kimia Journal of Computer Science and Technology Application
Septiani, Nanda
Unknown Affiliation
Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Journal of Computer Science and Technology Application

 1 
Optimizing Student Engagement and Performance usingAI-Enabled Educational Tools Mirdad, Khaizure; Daeli, Ora Plane Maria; Septiani, Nanda; Ekawati, Anita; Rusilowati, Umi
CORISINTA Vol 1 No 1 (2024): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i1.22

Abstract

Education is the primary pillar in the progress of modern society. With the development of artificial intelligence (AI) technology, its potential to advance the learning process has become a major focus. This research focuses on the integration of AI-based educational tools to enhance student engagement and academic performance. Through experimental design with a control group, students were divided into two groups: one using AI tools while the other followed conventional methods. Students from various educational levels participated in this research. Data were collected through questionnaires and academic evaluations to compare the outcomes between the two groups. Data analysis was conducted using SmartPLS, enabling the evaluation of the impact of AI tools on student learning. The results indicate that AI integration enables a more personalized and responsive approach to the unique needs of students. It is expected that AI technology in education will bring significant changes in how students engage and achieve academic success. This research expands the understanding of the potential of AI in improving the education process. The integration of AI technology in learning is a progressive step toward a more adaptive and effective education system, preparing students for success in an increasingly connected and complex world
Optimizing Digital Marketing Strategies through Big Data and Machine Learning: Insights and Applications Andayani, Dwi; Madani, Muchlishina; Agustian, Harry; Septiani, Nanda; Wei Ming, LI
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.v1i2.29

Abstract

In the dynamic realm of digital marketing, the convergence of Big Data and machine learning has ushered in transformative changes, reshaping strategies through advanced data analytics and predictive modeling. This paper examines the pivotal role of these technologies in enhancing marketing practices, focusing on their impact on consumer targeting, engagement, and overall campaign effectiveness. By harnessing vast datasets and applying sophisticated machine learning algorithms, marketers can now predict consumer behavior with unprecedented accuracy, personalize marketing messages, and optimize operational strategies to maximize engagement and return on investment. Despite the profound advantages, the integration of these technologies raises substantial challenges, including data privacy concerns and the need for specialized skills. Through a mixed-methods approach combining quantitative data analysis and qualitative interviews, this study not only demonstrates the improved predictive accuracy and segmentation capabilities afforded by these technologies but also discusses the barriers to their full potential realization. The findings highlight a clear trajectory towards more data-driven, responsive marketing paradigms, suggesting a future where digital marketing strategies are increasingly informed by insights derived from Big Data and machine learning. This paper aims to provide a comprehensive overview of the current landscape and future potential of these transformative technologies in digital marketing.
Big Data Analytics for Smart Cities: Optimizing Urban Traffic Management Using Real-Time Data Processing Miftah, Mohammad; Immaniar Desrianti, Dewi; Septiani, Nanda; Yadi Fauzi, Ahmad; Williams, Cole
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.56

Abstract

Smart cities require efficient traffic management to address congestion and optimize urban mobility. With increasing urban populations and vehicle vol- umes, traditional traffic control systems struggle to meet growing demands, ne- cessitating advanced technological interventions. This study aims to explore the integration of big data analytics and real-time data processing in optimizing urban traffic management. By leveraging machine learning algorithms, sensor data, and predictive models, this research seeks to enhance traffic flow and improve overall transportation efficiency. The methodology involves col- lecting data from traffic sensors, GPS-equipped vehicles, and surveillance cameras, which are then analyzed using Apache Hadoop and Apache Spark to derive meaningful insights. Real-time data processing techniques ensure im- mediate responses to traffic conditions, dynamically adjusting signal timings and rerouting vehicles to mitigate congestion. The results indicate a 15-25% reduc- tion in travel times in high-traffic areas where real-time adaptive signal control is implemented. Furthermore, the analysis highlights distinct traffic patterns, congestion hotspots, and travel time optimization opportunities that can sig- nificantly enhance urban transportation efficiency. This research confirms that big data-driven traffic management can lead to better decision-making, im- proved commuter experiences, and reduced environmental impact through lower emissions. Future studies should focus on advanced predictive algo- rithms, connected vehicle technology, and AI-driven automation to further refine urban traffic solutions. By implementing real-time analytics, smart cities can develop sustainable, efficient, and adaptive traffic management systems that improve mobility and quality of life for urban residents.
Revolutionizing Renewable Energy Systems throughAdvanced Machine Learning Integration Approaches Sri Rahayu; Septiani, Nanda; Ramzi Zainum Ikhsan; Kareem, Yasir Mustafa; Untung Rahardja
CORISINTA Vol 2 No 2 (2025): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i2.115

Abstract

The increasing global emphasis on sustainability has accelerated investments in renewable energy technologies, positioning sources like solar, wind, and hydroelectric power as vital alternatives to fossil fuels. Despite significant progress, integrating renewable energy into existing grids remains challenging due to variability in energy output, grid instability, and inefficiencies in energy storage systems. This study investigates the potential of machine learning (ML) to revolutionize the renewable energy sector by enhancing energy forecasting, grid management, and energy storage optimization. Using a combination of supervised learning, deep learning, and reinforcement learning techniques, we developed predictive and optimization models based on historical and real-time datasets. Additionally, structural equation modeling (SEM) with SmartPLS was employed to analyze the relationships between key variables, such as machine learning algorithms, renewable energy sources, sustainability performance, and operational efficiency. The results indicate that machine learning significantly improves energy forecasting accuracy, grid reliability, and storage efficiency, with R-squared values of 0.685 for operational efficiency and 0.588 for sustainability performance. These findings highlight the transformative role of ML in optimizing renewable energy systems and achieving sustainable energy goals. While ML offers promising solutions for renewable energy challenges, further research is needed to address real-time data integration, model scalability, and economic feasibility. This study provides a foundation for future innovations, emphasizing the importance of intelligent, data-driven strategies in advancing global energy sustainability.
Co-Authors AA Sudharmawan, AA Agustian, Harry Daeli, Ora Plane Maria Desrianti, Dewi Immaniar Dina Novianti Dolan, Ellen Dwi Andayani, Dwi Ekawati, Anita Erwin Prasetya Toepak Fauziah, Zaleha Kareem, Yasir Mustafa Madani, Muchlishina Miftah, Mohammad Mirdad, Khaizure Mochamad Heru Riza Chakim Muhammad Afif Mulyati Nasution, Erni Juliana Al Hasanah Ninda Lutfiani Pérez, Carlos Qurotul Aini Ramzi Zainum Ikhsan Rusilowati, Umi Safiani A. Faaroek Sanali, Mutantri Saulina Panjaitan, Aropria Singh Bist , Ankur Spits Warnar, Harco Leslie Hendric SRI RAHAYU Sriliasta Bangun, Cicilia Sutedja, Indrajani Tatik Mariyanti, Tatik Tri Andini, Nindi Untung Rahardja Victorianda Wahyu Nugroho Wei Ming, LI Williams, Cole Yadi Fauzi, Ahmad Yusrawati Yusrawati Zainum Ikhsan, Ramzi Zarasky, Dwiki Zulkarnain, Andy