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Leveraging Machine Learning Models to Enhance Startup Collaboration and Drive Technopreneurship

Aptisi Transactions on Technopreneurship (ATT) · Vol. 6 No. 3 (2024)

DOI
10.34306/att.v6i3.462
ISSN
2656-8888, 2655-8807
Terbit
2024-09-13
Penerbit
Pandawan
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

In the dynamic and competitive realm of startups, identifying and cultivating effective collaborations is crucial for sustained success. This research evaluates how machine learning (ML) technologies can enhance startup collaborations by advancing decision-making processes through the analysis of historical data. Employing the SmartPLS methodology, this study collected data from 220 stakeholders, including 207 actively engaged in startups that are either utilizing or integrating ML technologies. The investigation focuses on understanding ML models, the importance of historical data, and the dimensions of collaboration critical to the success of startups. Through analysis with PLS-SEM, it was found that ML models significantly boost inter-startup synergy and the effectiveness of collaborative efforts. The results provide vital insights for industry practitioners and strategic decision-makers, offering practical strategies to employ ML in optimizing collaboration and ensuring sustainable growth within the technopreneurship arena. This study not only highlights the benefits of ML in fostering cooperative ventures but also aims to refine the strategic frameworks essential to the startup ecosystem.

Kata kunci

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40 penulis · 165 tautan kepenulisan bersama · 57 lintas organisasi · menampilkan 40 yang paling banyak terhubung

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  • Universitas Raharja
  • Satya Wacana Christian University
  • Universitas Catur Insan Cendekia Cirebon
  • Universitas Banten Jaya
  • Universitas Buddhi Dharma
  • State University of Semarang
  • Universitas Komputer Indonesia
  • Organisasi lain
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Qurotul AiniUniversitas Raharja26
Irwan SembiringSatya Wacana Christian University14
Danny ManonggaSatya Wacana Christian University10
Nuke Puji Lestari SantosoUniversitas Raharja6
Agung RizkyUniversitas Raharja6
Kristoko Dwi HartomoSatya Wacana Christian University5
Alfiah KhoirunisaUniversitas Raharja5
Po Abas SunaryaUniversitas Raharja5
Fitra Putri OgandaUniversitas Raharja5
Hendry HendrySatya Wacana Christian University4
Shofiyul MillahUniversitas Raharja4
Ade IrianiSatya Wacana Christian University4
Dwi ApriliasariUniversitas Raharja3
Indri HandayaniUniversitas Raharja3
Eka Purnama HarahapUniversitas Raharja3
Sudaryono SudaryonoUniversitas Banten Jaya3
Aditya MardiansyahUniversitas Raharja3
Iwan SetyawanSatya Wacana Christian University3
Chandra LukitaUniversitas Catur Insan Cendekia Cirebon2
April Firman DaruState University of Semarang2
Desy AprianiUniversitas Raharja2
Nur AzizahUniversitas Raharja2
Mohamad RakhmansyahUniversitas Raharja2
Ihsan Nuril HikamUniversitas Raharja2
Adam FaturahmanUniversitas Raharja2
Umi NarimawatiUniversitas Komputer Indonesia2
Joko SiswantoPoliteknik Keselamatan Transportasi Jalan2
Sudaryono SudaryonoUniversitas Gadjah Mada2
Firman HanafiUniversitas Raharja2
Eko SediyonoSatya Wacana Christian University2
Krismiyati KrismiyatiSatya Wacana Christian University2
Marviola Grace HardiniUniversitas Raharja2
Dwi JulianingsihUniversitas Raharja2
Ahmad RamadanAfiliasi tidak diketahui2
Benny DaniawanUniversitas Buddhi Dharma1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://att.aptisi.or.id/index.php/att/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Leveraging Machine Learning Models to Enhance Startup Collaboration and Drive Technopreneurship</dc:title>
	<dc:creator xml:lang="en">Wijono, Sutarto</dc:creator>
	<dc:creator xml:lang="en">Rahardja, Untung</dc:creator>
	<dc:creator xml:lang="en">Purnomo, Hindriyanto Dwi</dc:creator>
	<dc:creator xml:lang="en">Lutfiani, Ninda</dc:creator>
	<dc:creator xml:lang="en">Yusuf, Natasya Aprila</dc:creator>
	<dc:creator xml:lang="id">Lutfiani, Ninda</dc:creator>
	<dc:subject xml:lang="en">Machine Learning Models</dc:subject>
	<dc:subject xml:lang="en">Historical Data</dc:subject>
	<dc:subject xml:lang="en">Startup Collaboration</dc:subject>
	<dc:subject xml:lang="en">Long-term Partnership Success</dc:subject>
	<dc:description xml:lang="en">In the dynamic and competitive realm of startups, identifying and cultivating effective collaborations is crucial for sustained success. This research evaluates how machine learning (ML) technologies can enhance startup collaborations by advancing decision-making processes through the analysis of historical data. Employing the SmartPLS methodology, this study collected data from 220 stakeholders, including 207 actively engaged in startups that are either utilizing or integrating ML technologies. The investigation focuses on understanding ML models, the importance of historical data, and the dimensions of collaboration critical to the success of startups. Through analysis with PLS-SEM, it was found that ML models significantly boost inter-startup synergy and the effectiveness of collaborative efforts. The results provide vital insights for industry practitioners and strategic decision-makers, offering practical strategies to employ ML in optimizing collaboration and ensuring sustainable growth within the technopreneurship arena. This study not only highlights the benefits of ML in fostering cooperative ventures but also aims to refine the strategic frameworks essential to the startup ecosystem.</dc:description>
	<dc:publisher xml:lang="en">Pandawan</dc:publisher>
	<dc:date>2024-09-13</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://att.aptisi.or.id/index.php/att/article/view/462</dc:identifier>
	<dc:identifier>10.34306/att.v6i3.462</dc:identifier>
	<dc:source xml:lang="en">Aptisi Transactions on Technopreneurship (ATT); Vol. 6 No. 3 (2024): November; 432−442</dc:source>
	<dc:source xml:lang="id">Aptisi Transactions On Technopreneurship (ATT); Vol 6 No 3 (2024): November; 432−442</dc:source>
	<dc:source>2656-8888</dc:source>
	<dc:source>2655-8807</dc:source>
	<dc:source>10.34306/att.v6i3</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://att.aptisi.or.id/index.php/att/article/view/462/286</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Sutarto Wijono, Untung Rahardja, Hindriyanto Dwi Purnomo, Ninda Lutfiani, Natasya Aprila Yusuf</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>