Esa Prakasa
- Computer Vision and Pattern Recognition top 10%
- Industrial and Manufacturing Engineering top 10%
- Organic Chemistry
- Environmental Engineering
- Artificial Intelligence
- Co-authors
- Bambang SugiartoHermawan NugrohoAzura Mohd AffandiHilman F. PardedeM. H. Ahmad FadzilRatih DamayantiYan RiantoVijanth Sagayan Asirvadam
- Topics
- Wood and Agarwood Research (11 papers)Industrial Vision Systems and Defect Detection (9 papers)Smart Agriculture and AI (7 papers)
- Cited by
- Industrial and Manufacturing EngineeringComputer Vision and Pattern RecognitionGeneral Dentistry
- Journals
- SHILAP Revista de lepidopterologíaComputers in Biology and MedicineSN Applied Sciences
- Partner nations
- IndonesiaMalaysiaSwitzerland
In The Last Decade
Esa Prakasa
38 papers receiving 227 citations
Peers
Comparison fields: 5 of 90
- Computer Vision and Pattern Recognition 76
- Industrial and Manufacturing Engineering 53
- Organic Chemistry 49
- Environmental Engineering 35
- Artificial Intelligence 25
Countries citing papers authored by Esa Prakasa
This map shows the geographic impact of Esa Prakasa'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 Esa Prakasa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Esa Prakasa more than expected).
Fields of papers citing papers by Esa Prakasa
This network shows the impact of papers produced by Esa Prakasa. 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 Esa Prakasa. The network helps show where Esa Prakasa may publish in the future.
Co-authorship network of co-authors of Esa Prakasa
This figure shows the co-authorship network connecting the top 25 collaborators of Esa Prakasa. A scholar is included among the top collaborators of Esa Prakasa 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 Esa Prakasa. Esa Prakasa 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 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | 2 | |
| 10 | 11 | |
| 11 | 9 | |
| 12 | 4 | |
| 13 | 36 | |
| 14 | 5 | |
| 15 | 5 | |
| 16 | 45 | |
| 17 | 14 | |
| 18 | 5 | |
| 19 | 5 | |
| 20 | PENGUJIAN KLASIFIKASI POLA TELAPAK TANGAN DENGAN MENGGUNAKAN RANGKAIAN MATRIKS SENSOR CAHAYA | 0 |
About Esa Prakasa
Esa Prakasa is a scholar working on Industrial and Manufacturing Engineering, Computer Vision and Pattern Recognition and Media Technology, having authored 47 papers that have together received 242 indexed citations. Recurring topics across this work include Wood and Agarwood Research (11 papers), Industrial Vision Systems and Defect Detection (9 papers) and Smart Agriculture and AI (7 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (53 citations), Computer Vision and Pattern Recognition (76 citations) and General Dentistry (6 citations). Esa Prakasa has collaborated with scholars based in Indonesia, Malaysia and Switzerland. Frequent co-authors include Bambang Sugiarto, Hermawan Nugroho, Azura Mohd Affandi, Hilman F. Pardede, M. H. Ahmad Fadzil, Ratih Damayanti, Yan Rianto, Vijanth Sagayan Asirvadam, Ahmad Fadzil Mohamad Hani and Putu Harry Gunawan. Their work appears in journals such as SHILAP Revista de lepidopterología, Computers in Biology and Medicine and SN Applied Sciences.
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.