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All Journal INTECOMS: Journal of Information Technology and Computer Science JSAI (Journal Scientific and Applied Informatics) bit-Tech International Journal of Informatics and Computation Jurnal Tekinkom (Teknik Informasi dan Komputer) International Journal of Engineering, Technology and Natural Sciences (IJETS) Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Insand Comtech : Information Science and Computer Technology Journal Eduvest - Journal of Universal Studies
Joko Aryanto
Universitas Teknologi Yogyakarta
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Neuroscience Social Sciences

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Analyzing Cognitive Determinants of Internet Outcome Diversity using SEM and K-Means Clustering Nathanael Denandro; Joko Aryanto
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3815

Abstract

While the digital divide has traditionally been examined through socioeconomic and infrastructural lenses, this study explicitly prioritizes the causal role of cognitive intelligence (IQ) as a primary determinant of third-level digital inequality, focusing on how individuals convert access into diverse internet outcomes. Using a quantitative cross-sectional design with 132 respondents in Indonesia, the analysis applies Covariance-Based Structural Equation Modeling (CB-SEM) as the principal analytical approach to estimate direct and mediated relationships among cognitive intelligence, material access, digital skills, and outcome diversity, complemented by K-Means clustering to reveal heterogeneity in user profiles rather than to construct a predictive model. The SEM results indicate that IQ significantly influences digital skills (β = 0.47, p < 0.01) and indirectly affects outcome diversity (β = 0.38, p < 0.01), while digital skills emerge as the strongest predictor of outcome diversity (β = 0.63, p < 0.01), confirming their central mediating role. These findings operationalize the integration of cognitive capacity into third-level digital divide models by demonstrating that internal cognitive resources systematically condition the conversion of access into outcomes, extending beyond conventional resource-based explanations. The clustering analysis identifies four distinct user segments, including a Resource-Limited Active group that achieves high proficiency despite constrained socioeconomic resources, indicating alternative learning pathways. The combined analytical strategy provides complementary insights by linking structural causality with user heterogeneity, which cannot be captured by single-method approaches. These results suggest that effective digital inclusion policies must incorporate cognitively adaptive strategies alongside infrastructure development
Design and Evaluation of a Flutter Node.js Mongo DB System for Hospital Nutritional Inventory Auditing Raihan Muflih; Joko Aryanto
Eduvest - Journal of Universal Studies Vol. 6 No. 9 (2026): Eduvest - Journal of Universal Studies (Issue in Progres)
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i9.53443

Abstract

Manual auditing of hospital nutritional supplies often results in delays, transcription errors, and discrepancies between physical stock and system records. This study designed and evaluated a mobile-cloud inventory auditing system for the nutritional installation at RSUD Kraton Pekalongan. The system was developed using the Waterfall Software Development Life Cycle (SDLC) model and implemented with a Flutter mobile application, a Node.js backend, a MongoDB database, and Firebase-based data synchronization. Actual operational data from a one-month deployment in April 2025 were used to evaluate the system against the previous manual workflow. The results showed an input accuracy of 98.47% (1,226 valid entries out of 1,245), a 90% reduction in inventory discrepancies, and an 80% reduction in auditing time, from 4.5 hours to 0.9 hours per section. Furthermore, network latency evaluation showed an average optimized payload ingestion time of 0.8 seconds under stable connectivity, supported by a low database input/output (I/O) footprint of 310.91 B/s for inbound traffic. These findings indicate that the proposed system substantially improved data consistency, reduced the burden of manual transcription, and accelerated hospital logistics auditing without requiring excessive network bandwidth. The study contributes a validated implementation model for mobile-cloud inventory management in healthcare settings and provides a practical foundation for future predictive inventory planning.
Co-Authors Agil Ghani Istikmal Ahmad Sulaeman Ahmad Tri Hidayat Allieffa Salsabilla Pradisthi Anang Faktchur Rachman Dzaky Abdur Razaq kasmawaru kasmawaru Layati Layati Nathanael Denandro Raihan Muflih Roselilie Simbulan Wijaya, Nurhadi Yuli Asriningtias