Article Per Year (5 Year)
p-Index From 2020 - 2025
4.232
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jupiter MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Sinkron : Jurnal dan Penelitian Teknik Informatika Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial BAREKENG: Jurnal Ilmu Matematika dan Terapan ILKOM Jurnal Ilmiah JURNAL PETIK Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURTEKSI Jurnal Manajemen Informatika EDUMATIC: Jurnal Pendidikan Informatika Jurnal Sistem informasi dan informatika (SIMIKA) Diseminasi : Jurnal Pengabdian kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Pekemas IPI Garut International Journal of Global Operations Research DECODE: Jurnal Pendidikan Teknologi Informasi Journal of Informatics and Communication Technology (JICT) Journal of Informatics and Communication Technology (JICT)
Nurdiana, Dian
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 34 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 34 Documents
Search

 1   2   3   4 
Analysis of Field Work Practice Information System Service Quality Using The Webqual 4.0 Method and Importance Performance Analysis Nurdiana, Dian; Maulana, Muhamad Riyan; Aprijani, Dwi Astuti; Amastini, Fitria
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.79182

Abstract

In the current digital era, the quality of website services is a crucial factor in supporting the effectiveness and efficiency of information systems, including the Information Systems Study Program Field Work Information System (SIPKL) at Universitas Terbuka. However, currently there is no in-depth evaluation of the quality of SIPKL services from a user perspective. This research aims to review the service quality of the SIPKL website as a whole and measure the level of user satisfaction with the services provided. To achieve this goal, the WebQual 4.0 method is used which measures three main dimensions of service quality, namely usability, information quality, and interaction quality. In addition, the Importance Performance Analysis (IPA) method is applied to evaluate the importance and performance of each service attribute being measured, so as to identify areas that require improvement. Data was collected through a survey with 100 respondents from Information Systems study program students who had used the SIPKL website. The research results show a value of 101.6% for the level of conformity, which indicates that the SIPKL website service performance has met or even exceeded user expectations and interests. Meanwhile, the gap value is categorized as “Good” with a positive value of 0.08 or >0. Indicators that require improvement are in quadrants II and III. Overall, this research provides strategic recommendations for SIPKL website managers to improve service quality so that it is more optimal in supporting students' needs in undergoing PKL.
Evaluating User Experience (UX) on Universitas Terbuka’s Website: A Combined Survey and GTMetrix Performance Analysis Putri, Mayang Anglingsari; Trihapningsari, Denisha; Nurdiana, Dian
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 1 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i1.32094

Abstract

The Universitas Terbuka (UT) website serves as the primary platform for providing academic services and information to students, lecturers, and the general public. However, as the number of users and the complexity of digital services increase, User Experience (UX) becomes a crucial aspect that influences the effectiveness and user satisfaction in accessing information and utilizing available features. This study aims to evaluate and analyze the user experience of the Universitas Terbuka website using a combined approach, incorporating survey questionnaires and web performance analysis. The urgency of this research lies in the need to ensure that the UT website delivers an optimal experience for its users, particularly in terms of ease of navigation, access speed, information clarity, and responsiveness across different devices. With the growing reliance on digital systems in distance learning, UX evaluation becomes a strategic step in identifying challenges and opportunities for improvement. The novelty of this study lies in its holistic approach, which integrates subjective user feedback from surveys with objective web performance analysis. The findings of this research are expected to provide concrete recommendations for enhancing the UX quality of the Universitas Terbuka website, thereby supporting the effectiveness of distance learning and improving access to academic services.
GOJEK DATA ANALYSIS THROUGH TEXT MINING USING SUPPORT VECTOR MACHINE (SVM) AND K-NEAREST NEIGHBOR (KNN) Hasanah, Siti Hadijah; Maulana, Muhamad Riyan; Nurdiana, Dian
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp889-902

Abstract

The main focus of this research is to apply and test the effectiveness of SVM and KNN methods in Gojek data text analysis. This research will examine how the two methods can classify user comments and feedback and identify data sentiment analysis at the same time practically help Gojek understand user needs and improve service quality. The data obtained through scrapping is categorized into positive and negative sentiment. Data is taken from Gojek application user reviews throughout the year 2022 with a total of 1148 sentiment data with a percentage of 80% training data and 20% testing data. Evaluation of model performance using Confusion Matrix and AUC-ROC Curve shows that SVM is more effective than KNN, with accuracy on training data of 92.55% for SVM and 81.71% for KNN, as well as accuracy on testing data of 82.40% for SVM and 77,09% for KNN.
Generative artificial intelligence as an evaluator and feedback tool in distance learning: a case study on law implementation Nurdiana, Dian; Maulana, Muhamad Riyan; Hasanah, Siti Hadijah; Chairunnisa, Madiha Dzakiyyah; Komuna, Avelyn Pingkan; Rif'an, Muhammad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2490-2505

Abstract

The development of generative artificial intelligence (GAI) has impacted various fields, including higher education. This research examines the use of GAI as an evaluator and feedback provider in distance legal education. This study tested five GAI models: ChatGPT, Perplexity, Gemini, Bing, and You, using a sample of 20 students and evaluations from legal experts. Descriptive statistical analysis and non-parametric tests, including Wilcoxon, intraclass correlation coefficient (ICC), Kappa, and Kendall's W, were used to assess accuracy, feedback quality, and usability. The results showed that ChatGPT was the most effective GAI, with the highest mean scores of 4.22 from experts and 4.12 from students, followed by Gemini with scores of 4.15 and 4.07. In terms of binary judgement accuracy, Gemini scored 80%, ChatGPT 60%, while Perplexity, Bing, and You had lower scores. Statistical analysis showed moderate agreement (ICC=0.439) and low alignment (Kappa=-0.058) between the GAIs and expert evaluations, with a Kendall's W value of 0.576 indicating moderate consistency in judgements. These findings emphasize the importance of selecting effective GAIs such as ChatGPT and Gemini to improve academic evaluation and learning in legal education, and pave the way for further innovations in the use of AI.
Co-Authors Adhi Susilo Adhi Susilo Adi, Puput Dani Prasetyo Adiprabowo, Tjahjo Amastini, Fitria Andri Suryadi Andri Suryadi Andri Suryadi Andri Suryadi Andri Suryadi, Andri Aprijani, Dwi Astuti Arival Dwi Saputro Chairunnisa, Madiha Dzakiyyah Demmy Dharma Bhakti Dian Nursantika Dian Rahadian Dini Nur Hakiki Dwi Astuti Aprijani Dwi Astuti Aprijani Eka Julianti Erman Arif Fajar Agus Saputro Hasanah, Siti Hadijah Imania, Kuntum An Nisa Imbuk Risnawati Ismalia Nur Hasanah Kitagawa, Akio Komuna, Avelyn Pingkan Kuntum An Nisa Imania Marsya Jelita Maulana, Muhamad Riyan Mayang Anglingsari Putri, Mayang Anglingsari Moh. Rizky Putra Aji Utama Muhamad Riyan Maulana Muhammad Yassir Nasrulloh, iman Ridwan, Muhammad Yovie Rif'an, Muhammad Saharuna, Saharuna Saputro, Arival Dwi Satyawan, Arief Suryadi Siti Husnul Bariah Trihapningsari, Denisha Unggul Utan Sufandi Utama, Moh. Rizky Putra Aji Yuniar Purwanti