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Strategic optimization of artificial intelligence digital learning in informatics engineering

IAES International Journal of Artificial Intelligence · Vol. 15 No. 4 (2026) · hlm. 2999–3008

DOI
10.11591/ijai.v15.i4.pp2999-3008
ISSN
2252-8938, 2089-4872
Terbit
2026-08-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This study examines the strategic optimization of artificial intelligence (AI)-based digital learning in the Department of Informatics Engineering by analyzing the interaction between internal and external factors that influence successful implementation. Employing a qualitative approach based on strengths, weaknesses, opportunities, and threats (SWOT) analysis, this research utilizes the internal factor analysis summary (IFAS) and external factor analysis summary (EFAS) to assess institutional readiness, constraints, and strategic opportunities for AI integration in higher education. The results indicate that external factors exert a stronger strategic influence than internal factors, as reflected by a higher EFAS score (1.50) compared to the IFAS score (1.25). Key external opportunities include the increasing demand for AI competencies, supportive national policies, and opportunities for industry collaboration, while the main internal limitations are limited faculty expertise in AI and insufficient AI-specific learning resources. Based on these findings, this study formulates a SWOT-based strategic framework that converts analytical outcomes into practical recommendations for curriculum development, faculty capacity building, and the adoption of adaptive AI learning technologies. This research contributes a context-specific and empirically grounded strategic model that advances AI integration beyond descriptive analysis, supporting more effective, personalized, and sustainable digital learning, particularly within Informatics Engineering programs in Indonesian Islamic higher education institutions.

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  • Universitas Islam Negeri Sultan Maulana Hasanuddin Banten
  • Universitas Islam Negeri Maulana Malik Ibrahim
  • Udayana University
  • Politeknik STIA LAN Bandung
  • 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
Enung NugrahaAfiliasi tidak diketahui3
Encep SyarifudinAfiliasi tidak diketahui3
Juhji JuhjiUniversitas Islam Negeri Sultan Maulana Hasanuddin Banten3
Supardi SupardiAfiliasi tidak diketahui3
Wazin WazinAfiliasi tidak diketahui2
SITI PATIMAHAfiliasi tidak diketahui2
Iwan KurniawanPoliteknik STIA LAN Bandung2
Intan AzkiyahAfiliasi tidak diketahui2
Lisda Cahya VeliaAfiliasi tidak diketahui1
Vania NurachmaAfiliasi tidak diketahui1
Badrudin BadrudinAfiliasi tidak diketahui1
E. SyarifuddinAfiliasi tidak diketahui1
Syaza Hazwani ZainiAfiliasi tidak diketahui1
Abdul MuinAfiliasi tidak diketahui1
Muhamad AfifAfiliasi tidak diketahui1
Fitri HilmiyatiAfiliasi tidak diketahui1
Siti MasitohUniversitas Islam Negeri Maulana Malik Ibrahim1
Imas MastoahAfiliasi tidak diketahui1
Yulian SyahputriAfiliasi tidak diketahui1
Linda Jati KusumawardaniAfiliasi tidak diketahui1
Muhammad Rizki SaputraAfiliasi tidak diketahui1
Wasehudin WasehudinAfiliasi tidak diketahui1
Asep FurqonuddinAfiliasi tidak diketahui1
Kholid KholidAfiliasi tidak diketahui1
Iwan KosasihAfiliasi tidak diketahui1
Faonaso HarefaAfiliasi tidak diketahui1
Lila Irawati Tjahjo WiduriAfiliasi tidak diketahui1
Yahdiana HarahapAfiliasi tidak diketahui1
Dian Andriani Ratna DewiAfiliasi tidak diketahui1
Muhammad FauziUdayana University1
Amin HidayatAfiliasi tidak diketahui1
WasehudinAfiliasi tidak diketahui1
Zuharnen ZuharnenAfiliasi tidak diketahui1

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) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Strategic optimization of artificial intelligence digital learning in informatics engineering</dc:title>
	<dc:creator>Ansori, Aan</dc:creator>
	<dc:creator>Muqdamien, Birru</dc:creator>
	<dc:creator>Tabrani, Ahmad</dc:creator>
	<dc:creator>Syafrizal, Reza</dc:creator>
	<dc:creator>Sutanto, Sutanto</dc:creator>
	<dc:creator>Wibowo, Eko Wahyu</dc:creator>
	<dc:creator>Dhestiyani, Syifa Amara</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Artificial intelligence; Digital learning; External factor analysis summary; Informatics engineering;  Internal factor analysis summary; Optimization</dc:subject>
	<dc:description xml:lang="en-US">This study examines the strategic optimization of artificial intelligence (AI)-based digital learning in the Department of Informatics Engineering by analyzing the interaction between internal and external factors that influence successful implementation. Employing a qualitative approach based on strengths, weaknesses, opportunities, and threats (SWOT) analysis, this research utilizes the internal factor analysis summary (IFAS) and external factor analysis summary (EFAS) to assess institutional readiness, constraints, and strategic opportunities for AI integration in higher education. The results indicate that external factors exert a stronger strategic influence than internal factors, as reflected by a higher EFAS score (1.50) compared to the IFAS score (1.25). Key external opportunities include the increasing demand for AI competencies, supportive national policies, and opportunities for industry collaboration, while the main internal limitations are limited faculty expertise in AI and insufficient AI-specific learning resources. Based on these findings, this study formulates a SWOT-based strategic framework that converts analytical outcomes into practical recommendations for curriculum development, faculty capacity building, and the adoption of adaptive AI learning technologies. This research contributes a context-specific and empirically grounded strategic model that advances AI integration beyond descriptive analysis, supporting more effective, personalized, and sustainable digital learning, particularly within Informatics Engineering programs in Indonesian Islamic higher education institutions.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-08-01</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-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/28111</dc:identifier>
	<dc:identifier>10.11591/ijai.v15.i4.pp2999-3008</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 4: August 2026; 2999-3008</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v15.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/28111/15269</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Aan Ansori, Birru Muqdamien, Ahmad Tabrani, Reza Syafrijal, Sutanto,  Eko Wahyu Wibowo, Syifa Amara Dhestiyani</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>