Dina Utami

593 citations
20 papers · 326 · h-index 9

Impact in

Papers in

    • Computer Science and Engineering 5
    • AI in Service Interactions 5
    • Edcuational Technology Systems 4
    • Data Mining and Machine Learning Applications 3
    • Multimedia Learning Systems 3

Dina Utami

20 papers receiving 313 citations

Peers

Dina Utami
Comparison fields: 5 of 80
  • Applied Psychology 62
  • Human-Computer Interaction 39
  • Health Informatics 7
  • Social Psychology 77
  • Family Practice 8
Replace Reza Amini with:
Reza Amini United States
M. Sazzad Hussain Australia
Everlyne Kimani United States
Stefán Ólafsson United States
Wei Qi Koh Ireland
Aqueasha Martin-Hammond United States
Eric Forbell United States
Julio Vega United States
Silke ter Stal Netherlands
Teresa K. O’Leary United States
Dina Utami relative to Reza Amini United States Reza Amini's profile →
Citations per field
00.5×2.7×
Reza Amini · 1×
Citations per year

Countries citing papers authored by Dina Utami

Since Specialization
Citations

This map shows the geographic impact of Dina Utami's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dina Utami with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dina Utami more than expected).

Fields of papers citing papers by Dina Utami

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dina Utami. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dina Utami. The network helps show where Dina Utami may publish in the future.

Co-authors

The 9 scholars most cited alongside Dina Utami, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dina Utami Line = papers co-authored together Dina Utami links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 2016108
2
Animasi Dalam Pembelajaran
201144
3 201838
4 202227
5 201927
6 201715
7 201613
8 201412
9 20179
10 20198
11 20175
12 20174
13 20184
14 20203
15 20182
16 20172
17
A Conversational Agent-based Clinical Trial Search Engine
20132
18 20191
19 20191
20 20201

About Dina Utami

Dina Utami is a scholar working on Artificial Intelligence, Information Systems, Social Psychology, General Health Professions and Health, having authored 20 papers that have together received 326 indexed citations. Recurring topics across this work include Computer Science and Engineering (5 papers), AI in Service Interactions (5 papers), Edcuational Technology Systems (4 papers), Social Robot Interaction and HRI (4 papers), Data Mining and Machine Learning Applications (3 papers), Multimedia Learning Systems (3 papers), Health Literacy and Information Accessibility (2 papers) and Mobile Health and mHealth Applications (2 papers). The work is most often cited by research in Applied Psychology (62 citations), Human-Computer Interaction (39 citations), Health Informatics (7 citations), Social Psychology (77 citations) and Family Practice (8 citations). Dina Utami has collaborated with scholars based in Indonesia, United States and Mexico. Frequent co-authors include Timothy Bickmore, Michael K. Paasche‐Orlow, Robin K. Matsuyama, Dhaval Parmar, Stefán Ólafsson, Kusprasapta Mutijarsa, Louis J. Kruger, Barbara Barry and Bridgette A. Barry. Their work appears in journals such as Journal of Health Communication, Autonomous Agents and Multi-Agent Systems, Journal of Medical Internet Research, SHILAP Revista de lepidopterología and Zenodo (CERN European Organization for Nuclear Research).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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