Dwi Arman Prasetya

124 total papers · 630 total citations
74 papers, 392 citations indexed

About

Dwi Arman Prasetya is a scholar working on Information Systems, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Dwi Arman Prasetya has authored 74 papers receiving a total of 392 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Information Systems, 23 papers in Artificial Intelligence and 21 papers in Electrical and Electronic Engineering. Recurrent topics in Dwi Arman Prasetya's work include Blockchain Technology in Education and Learning (10 papers), Data Mining and Machine Learning Applications (10 papers) and Computer Science and Engineering (10 papers). Dwi Arman Prasetya is often cited by papers focused on Blockchain Technology in Education and Learning (10 papers), Data Mining and Machine Learning Applications (10 papers) and Computer Science and Engineering (10 papers). Dwi Arman Prasetya collaborates with scholars based in Indonesia, Japan and Malaysia. Dwi Arman Prasetya's co-authors include Puput Dani Prasetyo Adi, Irfan Mujahidin, Takashi Yasuno, Hiroshi Suzuki, Iswanto Iswanto, Phong Thanh Nguyen, Akio Kitagawa, Syarif Hidayatullah, Volvo Sihombing and Anwar Sanusi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Future Internet and Information.

In The Last Decade

Dwi Arman Prasetya

53 papers receiving 354 citations

Author Peers

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

Author						Papers	Cites
Dwi Arman Prasetya	157	90	83	54	44	74	392
Carsten Wolff	100 0.6×	65 0.7×	54 0.7×	33 0.6×	25 0.6×	97	397
Sean McGrath	116 0.7×	109 1.2×	62 0.7×	112 2.1×	14 0.3×	53	374
Feng Zhou	211 1.3×	77 0.9×	43 0.5×	92 1.7×	41 0.9×	51	351
Erfan Rohadi	70 0.4×	56 0.6×	38 0.5×	24 0.4×	24 0.5×	88	365
Bin Fu	103 0.7×	65 0.7×	72 0.9×	186 3.4×	46 1.0×	72	421
Poonam Yadav	137 0.9×	67 0.7×	114 1.4×	181 3.4×	34 0.8×	47	378
Pradorn Sureephong	85 0.5×	43 0.5×	33 0.4×	23 0.4×	42 1.0×	58	346
Adian Fatchur Rochim	76 0.5×	131 1.5×	62 0.7×	56 1.0×	8 0.2×	96	353
Shang Gao	71 0.5×	38 0.4×	102 1.2×	69 1.3×	46 1.0×	58	424
Deng Li	97 0.6×	61 0.7×	66 0.8×	164 3.0×	18 0.4×	61	416

Countries citing papers authored by Dwi Arman Prasetya

Since Specialization

Citations

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

Fields of papers citing papers by Dwi Arman Prasetya

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dwi Arman Prasetya. 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 Dwi Arman Prasetya. The network helps show where Dwi Arman Prasetya may publish in the future.

Co-authorship network of co-authors of Dwi Arman Prasetya

This figure shows the co-authorship network connecting the top 25 collaborators of Dwi Arman Prasetya. A scholar is included among the top collaborators of Dwi Arman Prasetya 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 Dwi Arman Prasetya. Dwi Arman Prasetya 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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