Nanik Suciati
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 5%
- Information Systems top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Neurology top 10%
- Co-authors
- Chastine FatichahEko PrasetyoAgus Eko MinarnoAnny YuniartiHandayani TjandrasaAgus Zainal ArifinDaniel SiahaanFitri Bimantoro
- Topics
- Computer Science and Engineering (45 papers)Data Mining and Machine Learning Applications (30 papers)Image Retrieval and Classification Techniques (26 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE AccessApplied Soft Computing
In The Last Decade
Nanik Suciati
180 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 140
- Computer Vision and Pattern Recognition 669
- Artificial Intelligence 427
- Information Systems 186
- Radiology, Nuclear Medicine and Imaging 157
- Neurology 111
Countries citing papers authored by Nanik Suciati
This map shows the geographic impact of Nanik Suciati'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 Nanik Suciati with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nanik Suciati more than expected).
Fields of papers citing papers by Nanik Suciati
This network shows the impact of papers produced by Nanik Suciati. 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 Nanik Suciati. The network helps show where Nanik Suciati may publish in the future.
Co-authorship network of co-authors of Nanik Suciati
This figure shows the co-authorship network connecting the top 25 collaborators of Nanik Suciati. A scholar is included among the top collaborators of Nanik Suciati based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nanik Suciati. Nanik Suciati is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 4 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 2 | |
| 8 | 3 | |
| 9 | 1 | |
| 10 | 16 | |
| 11 | 29 | |
| 12 | 1 | |
| 13 | 7 | |
| 14 | 1 | |
| 15 | 3 | |
| 16 | 2 | |
| 17 | 8 | |
| 18 | Implementasi Metode Kombinasi Histogram of Oriented Gradients dan Hierarchical Centroid untuk Sketch Based Image Retrieval | 2 |
| 19 | DETEKSI WILAYAH CAHAYA INTENSITAS TINGGI CITRA DAUN MANGGA UNTUK EKSTRAKSI FITUR WARNA DAN TEKSTUR PADA KLASIFIKASI JENIS POHON MANGGA | 2 |
| 20 | Image Inpainting using Erosion and Dilation Operation | 13 |
About Nanik Suciati
Nanik Suciati is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Computer Graphics and Computer-Aided Design, having authored 196 papers that have together received 1.4k indexed citations. Recurring topics across this work include Computer Science and Engineering (45 papers), Data Mining and Machine Learning Applications (30 papers) and Image Retrieval and Classification Techniques (26 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (669 citations), Human-Computer Interaction (82 citations) and Neurology (111 citations). Nanik Suciati has collaborated with scholars based in Indonesia, Japan and Australia. Frequent co-authors include Chastine Fatichah, Eko Prasetyo, Agus Eko Minarno, Anny Yuniarti, Handayani Tjandrasa, Agus Zainal Arifin, Daniel Siahaan, Fitri Bimantoro, Diana Purwitasari and Eha Renwi Astuti. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Applied Soft Computing.
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.