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A Modular Business Intelligence Architecture for Predictive Student Admission Decision Support

International Journal of Cyber and IT Service Management · Vol. 6 No. 2 (2026) · hlm. 172–184

DOI
10.34306/ijcitsm.v6i2.284
ISSN
2808-554X, 2797-1325
Terbit
2026-09-30
Penerbit
Pandawan Sejahtera Indonesia
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0/

Abstrak

Student admission management in higher education increasingly depends on data-driven. Systems that can support predictive decision making beyond descriptive dashboard reporting. Existing Business Intelligence dashboards commonly display admission indicators such as leads, applicants, registrations, targets, program distribution, and historical trends, but they often do not include predictive processing, risk classification, warning generation, or recommendation outputs. This study aims to design an Artificial Intelligence (AI) Business Intelligence architecture for predictive student admission decision support. Using a design oriented model development approach, the proposed architecture integrates admission data collection, preprocessing, Prophet based forecasting, risk scoring, Representational State Transfer (REST) API communication, and dashboard wireframe design. The system architecture consists of Vue.js as the presentation layer, Laravel as the application and API layer, MySQL as the data layer, and Python as the predictive analytics layer. Admission records are transformed into forecasting-ready datasets., processed through Prophet time series prediction, classified into program level risk categories, and converted into warning and recommendation outputs. The proposed model contributes a technical data pipeline and modular predictive workflow that can extend existing descriptive admission dashboards without replacing the entire information system. The architecture aligns with SDG 4 on Quality Education by supporting informed admission planning and with SDG 9 on Industry, Innovation and Infrastructure by promoting modular digital innovation in higher education. Future research should implement the architecture using longitudinal admission datasets, compare Prophet with Autoregressive Integrated Moving Average (ARIMA), Random Forest, and XGBoost, evaluate forecasting accuracy, test API performance, and assess dashboard usability in operational admission management.

Kata kunci

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40 penulis · 147 tautan kepenulisan bersama · 6 lintas organisasi · menampilkan 40 yang paling banyak terhubung

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  • Universitas Raharja
  • Satya Wacana Christian University
  • 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 Raharja18
Ninda LutfianiUniversitas Raharja8
Irwan SembiringSatya Wacana Christian University6
Danny ManonggaSatya Wacana Christian University6
Po Abas SunaryaAfiliasi tidak diketahui4
Alfiah KhoirunisaUniversitas Raharja3
Dwi ApriliasariAfiliasi tidak diketahui3
Muhtarom MuhtaromAfiliasi tidak diketahui3
Agung RizkyAfiliasi tidak diketahui3
Sutarto WijonoAfiliasi tidak diketahui3
Shofiyul MillahAfiliasi tidak diketahui3
Desy AprianiAfiliasi tidak diketahui2
Ihsan Nuril HikamAfiliasi tidak diketahui2
Adam FaturahmanAfiliasi tidak diketahui2
Nuke Puji Lestari SantosoUniversitas Raharja2
Natasya Aprila YusufAfiliasi tidak diketahui2
Umi NarimawatiAfiliasi tidak diketahui2
Joko SiswantoAfiliasi tidak diketahui2
Hindriyanto Dwi PurnomoSatya Wacana Christian University2
Marviola HardiniAfiliasi tidak diketahui2
Aan KaniviaAfiliasi tidak diketahui2
Sabda MaulanaAfiliasi tidak diketahui2
Dwi JulianingsihAfiliasi tidak diketahui2
Ira GeraldinaAfiliasi tidak diketahui2
Sri WatiniAfiliasi tidak diketahui2
Alexander WilliamsAfiliasi tidak diketahui1
Sella AvionitaAfiliasi tidak diketahui1
Dini HidayatiAfiliasi tidak diketahui1
Andriyansah AndriyansahAfiliasi tidak diketahui1
Galih Putra CesnaAfiliasi tidak diketahui1
Ahmad Yadi FauziAfiliasi tidak diketahui1
Hilda HildaAfiliasi tidak diketahui1
Alfri AdiwijayaAfiliasi tidak diketahui1
Nur AzizahAfiliasi tidak diketahui1
Raihan RaihanAfiliasi tidak diketahui1
Sipho DlaminiAfiliasi 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) https://iiast.iaic-publisher.org/ijcitsm/index.php/IJCITSM/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">A Modular Business Intelligence Architecture for Predictive Student Admission Decision Support</dc:title>
	<dc:creator xml:lang="en">Rakhmansyah, Mohamad</dc:creator>
	<dc:creator xml:lang="en">Rahardja, Untung</dc:creator>
	<dc:creator xml:lang="en">Fazri, Muhammad Faizal</dc:creator>
	<dc:creator xml:lang="en">Ivanov, Nikita</dc:creator>
	<dc:subject xml:lang="en">Artificial Intelligence</dc:subject>
	<dc:subject xml:lang="en">Business Intelligence</dc:subject>
	<dc:subject xml:lang="en">Predictive Analytics</dc:subject>
	<dc:subject xml:lang="en">System Architecture</dc:subject>
	<dc:subject xml:lang="en">Student Admission</dc:subject>
	<dc:description xml:lang="en">Student admission management in higher education increasingly depends on data-driven. Systems that can support predictive decision making beyond descriptive dashboard reporting. Existing Business Intelligence dashboards commonly display admission indicators such as leads, applicants, registrations, targets, program distribution, and historical trends, but they often do not include predictive processing, risk classification, warning generation, or recommendation outputs. This study aims to design an Artificial Intelligence (AI) Business Intelligence architecture for predictive student admission decision support. Using a design oriented model development approach, the proposed architecture integrates admission data collection, preprocessing, Prophet based forecasting, risk scoring, Representational State Transfer (REST) API communication, and dashboard wireframe design. The system architecture consists of Vue.js as the presentation layer, Laravel as the application and API layer, MySQL as the data layer, and Python as the predictive analytics layer. Admission records are transformed into forecasting-ready datasets., processed through Prophet time series prediction, classified into program level risk categories, and converted into warning and recommendation outputs. The proposed model contributes a technical data pipeline and modular predictive workflow that can extend existing descriptive admission dashboards without replacing the entire information system. The architecture aligns with SDG 4 on Quality Education by supporting informed admission planning and with SDG 9 on Industry, Innovation and Infrastructure by promoting modular digital innovation in higher education. Future research should implement the architecture using longitudinal admission datasets, compare Prophet with Autoregressive Integrated Moving Average (ARIMA), Random Forest, and XGBoost, evaluate forecasting accuracy, test API performance, and assess dashboard usability in operational admission management.</dc:description>
	<dc:publisher xml:lang="en">Pandawan Sejahtera Indonesia</dc:publisher>
	<dc:date>2026-09-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://iiast.iaic-publisher.org/ijcitsm/IJCITSM/article/view/284</dc:identifier>
	<dc:identifier>10.34306/ijcitsm.v6i2.284</dc:identifier>
	<dc:source xml:lang="en">International Journal of Cyber and IT Service Management; Vol. 6 No. 2 (2026): October; 172-184</dc:source>
	<dc:source>2808-554X</dc:source>
	<dc:source>2797-1325</dc:source>
	<dc:source>10.34306/ijcitsm.v6i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://iiast.iaic-publisher.org/ijcitsm/IJCITSM/article/view/284/425</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Mohamad Rakhmansyah, Untung Rahardja, Muhammad Faizal Fazri, Nikita Ivanov</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0/</dc:rights>
</oai_dc:dc>