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p-Index From 2021 - 2026
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All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) International Journal of Research and Applied Technology (INJURATECH)
Rafdhi, Agis Abhi
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Pandawa App: Student Guide Application after the Covid-19 Pandemic Rafdhi, Agis Abhi; Bachtiar, Adam Mukharil; Hayati, Euis Neni; Mega, Raiswati Untsa
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 4 No. 2 (2023): INJIISCOM: VOLUME 4, ISSUE 2, DECEMBER 2023
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v4i2.13895

Abstract

The purpose of this research is to design a mobile-based application that functions as a pre-lecture socialization platform so that the post-Covid-19 transition period can be maintained and carried out well. The research method used in this research is descriptive analysis with a qualitative approach. We used an object-oriented approach with the System Development Life Cycle Prototyping in the application development process. The results show that the Pandawa application development can provide lecture guidance properly using a digital platform that can be accessed via smartphone. The main concept of this application is to contain procedures or guidelines for implementing face-to-face lectures during the transition period from the Covid-19 pandemic in the New Normal era. In addition, this application also has a feature integrated with the local government for reporting if there are residents who test positive for Covid-19. Therefore, it can be followed up directly and quickly. In the end, this application is present as an information medium to adapt new habits in the world of education, especially at the tertiary level.
Cloud-Based Big Data Analytics for Academic Decision Support Systems: A Case Study of University Learning Management Systems Rafdhi, Agis Abhi
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Komputer Indonesia

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Abstract

This study aims to address the computational limitations of traditional on-premise servers in higher education by proposing an integrated cloud-based big data analytics framework tailored for Learning Management Systems (LMS). Through a literature-based case study methodology, recent peer-reviewed studies from major academic databases were systematically extracted, compared, and synthesized to identify infrastructural gaps and design a novel conceptual model. The results indicate that while existing analytical models achieve high predictive accuracy, they frequently fail at the institutional scale due to latency bottlenecks and limited pedagogical usability. To resolve these issues, this study formulates a three-layered architecture comprising a Data Ingestion Layer, a Cloud Analytics Engine Layer, and a Decision Support System (DSS) Presentation Layer. This framework efficiently offloads heavy computational workloads to scalable cloud environments and translates complex algorithmic outputs into actionable insights via an intuitive academic dashboard. Implementation scenarios, such as student early warning systems and curriculum difficulty evaluations, demonstrate the framework's practical utility over traditional approaches. In conclusion, the proposed architecture effectively transforms static LMS platforms into proactive DSS. Future research should prioritize empirical prototyping with real-time institutional data and the integration of advanced security encryption protocols to ensure compliance with educational data privacy regulations.
Data-Driven Urban Planning: The Role of Spatial Data Mining in Smart City Decision Support Systems Rafdhi, Agis Abhi
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Komputer Indonesia

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Abstract

This study aims to address the analytical limitations of traditional Geographic Information Systems (GIS) in urban governance by proposing an integrated, cloud-based Spatial Data Mining Decision Support System (SDM-DSS) framework. Employing a systematic literature review methodology, recent peer-reviewed studies (2021–2026) from major scientific databases were extracted, compared, and thematically synthesized to identify architectural vulnerabilities in current smart city models. The results indicate that while advanced SDM algorithms exhibit high theoretical accuracy for modeling urban phenomena, their practical deployment is frequently hindered by fragmented architectures, localized computational bottlenecks, and a lack of real-time Internet of Things (IoT) integration. To resolve these operational deficiencies, this study formulates a three-layered conceptual architecture comprising a Data Management Layer, a Spatial Analytics Engine, and a Presentation Dashboard. By decoupling heavy computational workloads into a scalable cloud environment, the proposed framework seamlessly translates complex algorithmic outputs into intuitive, actionable policy directives, as demonstrated through dynamic public facility allocation and predictive disaster mitigation scenarios. In conclusion, the integrated SDM-DSS architecture fundamentally transforms reactive urban planning into a proactive, predictive paradigm. Future research should prioritize the empirical prototyping of this framework using real-time municipal data streams and the incorporation of privacy-preserving machine learning techniques to ensure data sovereignty.
Co-Authors Bachtiar, Adam Mukharil Hayati, Euis Neni Mega, Raiswati Untsa