Article Per Year (5 Year)
p-Index From 2021 - 2026
1.857
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Information Systems Engineering and Business Intelligence Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JTP - Jurnal Teknologi Pendidikan Aptisi Transactions on Technopreneurship (ATT) Jurnal Mantik Journal of Applied Engineering and Technological Science (JAETS) International Journal of Engineering, Science and Information Technology Abditeknika - Jurnal Pengabdian Kepada Masyarakat Innovative: Journal Of Social Science Research ITEJ (Information Technology Engineering Journals)
Retnowardhani, Astari
Unknown Affiliation
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 Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Desain Learning Management System dan Konten Berbasis Kecerdasan Buatan Generatif untuk Edukasi Perubahan Iklim bagi Perempuan Pegiat Lingkungan Puspita, Virienia; Retnowardhani, Astari; Andayani, Fitria
Abditeknika Jurnal Pengabdian Masyarakat Vol. 5 No. 1 (2025): April
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/abditeknika.v5i1.7630

Abstract

Dalam menghadapi krisis iklim yang semakin intens di Indonesia, edukasi perubahan iklim menjadi penting. Komunitas pegiat lingkungan, Peri Bumi, telah aktif dalam upaya edukasi dan inisiasi lingkungan. Namun, komunitas ini menghadapi tantangan dalam pengelolaan pembelajaran yang efektif dan produksi konten edukatif. Berdasarkan kebutuhan tersebut maka tim dosen universitas Bina Nusantara menginisiasi program pengabdian kepada masyarakat dengan mengembangkan Learning Management System (LMS) berbasis kecerdasan buatan generatif (Generative AI) serta memberikan pelatihan pembuatan konten digital. Hasil evaluasi menunjukkan bahwa LMS Peri Bumi telah meningkatkan aksesibilitas pembelajaran bagi 121 pengguna aktif dalam enam bulan pertama. Selain itu, pelatihan yang dilakukan menghasilkan peningkatan keterampilan anggota komunitas secara signifikan dengan total N-Gain sebesar 85,2%. Secara keseluruhan, program ini berkontribusi dalam menciptakan model edukasi berbasis komunitas yang berkelanjutan dan dapat direplikasi ke komunitas pegiat lingkungan lainnya.   In response to the escalating climate crisis in Indonesia, climate change education has become increasingly crucial. The environmental advocacy community Peri Bumi has been actively engaged in environmental education and initiatives. However, these communities face challenges in managing effective learning and producing educational content. Based on these needs, the Bina Nusantara University lecturer team initiated a community service program by developing a Learning Management System (LMS) based on generative artificial intelligence (generative AI) and providing digital content creation training. The results of the evaluation show that LMS Peri Bumi has improved learning accessibility for 121 active users in the first six months. In addition, the training conducted resulted in a significant improvement in the skills of community members with an N-Gain of 85.2%. Overall, the program contributes to creating a community-based education model that is sustainable and can be replicated to other communities of environmentalists.
Optimizing Learning Experiences: A Study of Student Satisfaction with LMS in Higher Education Ayubi, M. Nizar; Retnowardhani, Astari
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 2 (2025): July
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i2.501

Abstract

This research delves into the examination of student satisfaction regarding the use of Learning Management Systems (LMS) through a comprehensive questionnaire distributed among 326 participants. The study adopts a modified DeLone and McLean is Model as its methodology to assess various dimensions of LMS satisfaction. Utilizing SmartPLS for hypothesis testing, the study rigorously analyzes the data collected. The findings indicate the acceptance of all proposed hypotheses, revealing significant correlations among the variables under scrutiny. A notable outcome is the identification of service quality as the most prominent influencer of student satisfaction within LMS environments. This underscores the critical imperative for higher education institutions to prioritize and address service quality concerns proactively. Practical solutions may encompass optimizing technical support structures, refining user interfaces for enhanced accessibility, ensuring system stability, and facilitating ongoing training and support initiatives. By addressing these pivotal areas, institutions can elevate the overall student learning experience and enhance the efficacy of LMS platforms in facilitating robust educational outcomes.
Tingkat Kepuasan Pengguna E-Learning Mahasiswa Pascasarjana Universitas Swasta Terbaik di Jakarta: Pembelajaran Online Learning Pascasarjana Selama Pandemi Covid-19 pada Tahun 2020-2021 Ayub Ezra R; Astari Retnowardhani
ITEJ (Information Technology Engineering Journals) Vol. 7 No. 1 (2022): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v7i1.97

Abstract

Innovation learning that is relatively cheaper, easily accessible, or an integrated system as an e-learning platform in blended or hybrid. The purpose of this study was to analyze the effect of using e-learning on the satisfaction level of postgraduate students at a private university in Jakarta during the COVID-19 pandemic. Sample data obtained from postgraduate students; the number is 324 postgraduate students through online questionnaires on a google form. To conduct research testing through PLS-SEM analysis with the help of the SmartPls application version 3.0. The results of this study indicate that for H1 the original sample value is 0.158, the p-value is below 0.05, and the t-statistic is 2.791, which is greater than the t-table value of 1.962. For the results of H2, the original sample value is 0.132 and the p-value is above 0.05, and the t-statistic is 1.583 which is smaller than the t-table value of 1.962. For the H3 results, the original sample value is 0.201, the p-value is below 0.05, and the t-statistic is 2.221 which is greater than the t-table value of 1.962. So that, the overall result of this research is that the quality of the system, and the quality of information that affect the level of user satisfaction.
Comparison of LSTM and TCN Models for Customer Churn Prediction Based on Sentiment and Transaction Data Made Bayu Brahmanda Dharmasaguna; Astari Retnowardhani
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.979

Abstract

This study investigates the combined use of customer review sentiment analysis and transaction history to predict customer churn on the Balimall Market e-commerce platform. The dataset includes 41,519 reviews labeled with positive and negative sentiments and 48 transaction samples labeled as churn or non-churn based on RFM method. Two deep learning models, Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN), are applied in parallel for each analysis path. Data pre-processing includes filtering, cleaning, tokenizing, normalization, sentiment labeling, as well as feature engineering and churn labeling. Evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics shows that TCN excels with 91.55% accuracy on sentiment analysis and 91.67% on churn prediction, while LSTM achieves 86.35% and 86.67% respectively. Segment analysis shows that 47.30 % of users express negative sentiment yet remain active, 51.69 % express positive sentiment and remain active , 0.54 % express negative sentiment and churn, and 0.48 % express positive sentiment and churn. This finding demonstrates that negative sentiment alone does not necessarily lead to churn; instead, the greatest churn risk arises in negative sentiment churners and positive sentiment churners. Expert validation confirmed the reliability of both models, with the recommendation of using a hybrid to combine the advantages of each architecture. The results of this study are expected to help Baliyoni Group design a more targeted customer retention strategy and improve customer satisfaction by examining these segment conditions.
Co-Authors Andayani, Fitria Ayub Ezra R Ayubi, M. Nizar Desita Mustikaningrum Diyurman Gea Jarot S. Suroso Made Bayu Brahmanda Dharmasaguna Niky Purnama Putra Pramadya, Rifky Pratama, Rikky Ihza Puspita, Virienia Putratama, Donny R, Ayub Ezra Raziv Herman Diputra Sandy Anugerah Saragih, Margreth Handayani Yaya Sudarya Triana