Iping Supriana

169 total papers · 575 total citations
97 papers, 370 citations indexed

About

Iping Supriana is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Iping Supriana has authored 97 papers receiving a total of 370 indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Artificial Intelligence, 48 papers in Information Systems and 18 papers in Computer Vision and Pattern Recognition. Recurrent topics in Iping Supriana's work include Edcuational Technology Systems (31 papers), Data Mining and Machine Learning Applications (15 papers) and Multimedia Learning Systems (14 papers). Iping Supriana is often cited by papers focused on Edcuational Technology Systems (31 papers), Data Mining and Machine Learning Applications (15 papers) and Multimedia Learning Systems (14 papers). Iping Supriana collaborates with scholars based in Indonesia, Sri Lanka and United States. Iping Supriana's co-authors include Masayu Leylia Khodra, Kridanto Surendro, Ayu Purwarianti, Muhammad Naufal Rachmatullah, Nur Ulfa Maulidevi, Arry Akhmad Arman, Tien Fabrianti Kusumasari, Nor Azan Mat Zin, Adang Suwandi Ahmad and Darlis Herumurti and has published in prestigious journals such as SHILAP Revista de lepidopterología, Information Processing in Agriculture and Automated Software Engineering.

In The Last Decade

Iping Supriana

82 papers receiving 344 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Iping Supriana	127	122	81	41	36	97	370
Anik Nur Handayani	120 0.9×	102 0.8×	61 0.8×	14 0.3×	23 0.6×	126	406
Ahmad B. Alkhodre	72 0.6×	102 0.8×	78 1.0×	41 1.0×	16 0.4×	40	416
Masna Wati	228 1.8×	147 1.2×	24 0.3×	30 0.7×	14 0.4×	95	421
Adhi Susanto	76 0.6×	81 0.7×	74 0.9×	87 2.1×	14 0.4×	91	402
Sofianita Mutalib	74 0.6×	101 0.8×	71 0.9×	33 0.8×	12 0.3×	74	406
Ajib Susanto	148 1.2×	148 1.2×	145 1.8×	24 0.6×	15 0.4×	74	406
M F Syahputra	69 0.5×	78 0.6×	111 1.4×	13 0.3×	8 0.2×	65	388
Bhanu Sharma	74 0.6×	57 0.5×	91 1.1×	66 1.6×	10 0.3×	64	365
Huzain Azis	183 1.4×	149 1.2×	37 0.5×	26 0.6×	5 0.1×	51	354
Raden Venantius Hari Ginardi	128 1.0×	98 0.8×	24 0.3×	76 1.9×	17 0.5×	99	386

Countries citing papers authored by Iping Supriana

Since Specialization

Citations

This map shows the geographic impact of Iping Supriana'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 Iping Supriana with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Iping Supriana more than expected).

Fields of papers citing papers by Iping Supriana

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Iping Supriana. 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 Iping Supriana. The network helps show where Iping Supriana may publish in the future.

Co-authorship network of co-authors of Iping Supriana

This figure shows the co-authorship network connecting the top 25 collaborators of Iping Supriana. A scholar is included among the top collaborators of Iping Supriana 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 Iping Supriana. Iping Supriana is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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