Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal Journal of Computing and Smart Ecosystems
Kilala Mahadewi
Department of Information Technology, Faculty of Engineering and Computer Science, Universitas Muhammadiyah Semarang, Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Applying the UX Honeycomb Model to Evaluate User Satisfaction in the Maxim Application Auliya Rohman Riquelme Al Ubaidah; Eva Febyliana; Maulana Sihdi Habibie; Mustika Restu Nur Asri; Kilala Mahadewi; Nova Christina Sari; Muhammad Zainudin Al Amin
Journal of Computing and Smart Ecosystems Vol. 1 No. 2 (2025): J-CaSE
Publisher : S1 Teknologi Informasi, Universitas Muhammadiyah Semarang

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Abstract

This study evaluates the user experience of the Maxim application using Peter Morville’s UX Honeycomb approach, encompassing seven dimensions: usability, desirability, findability, accessibility, credibility, value, and usefulness. A descriptive quantitative method was employed, with data collected through questionnaires from active users of the Maxim application. Data analysis was conducted using descriptive statistics. The results indicate a positive evaluation, particularly in usability (access speed, average score of 3.87). However, the payment process and overall comfort received lower scores, suggesting the need for improvement. These findings indicate that the Maxim application is generally effective, but improvements to specific features could enhance user satisfaction.
WebGIS-Based Diagnosis of Economic Vulnerability: Implementing the Inflation Risk-Burden Matrix via a Spiral Development Framework Eva Febyliana; Teuku Zaine Abror Attolok; Auliya Rohman Riquelme Al Ubaidah; Kilala Mahadewi
Journal of Computing and Smart Ecosystems Vol. 1 No. 2 (2025): J-CaSE
Publisher : S1 Teknologi Informasi, Universitas Muhammadiyah Semarang

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Abstract

This study develops a WebGIS application to diagnose regional economic vulnerability using the Inflation Risk–Burden Matrix supported by a Spiral Development Framework. Monthly inflation data from 150 Indonesian cities for 2021–2024 are transformed into two indicators: long-term inflation burden and annual volatility risk. These indicators classify each city into four vulnerability quadrants. Findings show that more than half of the cities fall into the High-Burden & High-Risk category, indicating strong structural pressures and unstable price dynamics. The WebGIS system visualizes these classifications through thematic layers, spatial interaction tools, and automatic diagnostic pop-ups, allowing users to interpret inflation conditions more easily. The study concludes that integrating analytical metrics with spatial visualization enhances diagnostic accuracy and supports more effective, evidence-based decision-making for regional inflation control.
Co-Authors Al Amin, Muhammad Zainudin Auliya Rohman Riquelme Al Ubaidah Eva Febyliana Maulana Sihdi Habibie Mustika Restu Nur Asri Nova Christina Sari Teuku Zaine Abror Attolok