Journal of Computing and Smart Ecosystems

Applying the UX Honeycomb Model to Evaluate User Satisfaction in the Maxim Application

Wed, 31 Dec 2025 00:00:00 +0000

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.

The Use of Artificial Intelligence for Cyber Threat Detection: A Systematic Literature Review of Research Methods, Accuracy, and Gaps

Muhammad Distian Andi Hermawan — Wed, 31 Dec 2025 00:00:00 +0000

This study presents a comprehensive Systematic Literature Review (SLR) on the use of Artificial Intelligence (AI) for cyber threat detection, focusing on methods, accuracy levels, and research gaps from the last five years. A total of 47 eligible studies were analyzed using the PRISMA framework. The findings show that deep learning has become the dominant approach, outperforming traditional machine learning in identifying complex threats such as DDoS, zero-day attacks, and advanced malware. Hybrid models also demonstrate high accuracy, exceeding 95% in several datasets. However, significant gaps remain, including limited real-time evaluations, outdated public datasets, insufficient research on explainable AI, and the lack of adversarial defense mechanisms. This review emphasizes the need for more robust, interpretable, and adaptive AI-based security systems to address evolving cyber threats effectively. The results provide essential insights and guidance for future research in AI-driven cybersecurity.

Virtual Cosmetic Chemistry Lab Design for Bilingual STEAM-Based, Disability-Friendly Learning to Enhance Adaptive Skills

Wed, 31 Dec 2025 00:00:00 +0000

Digitalization is growing rapidly along with the development of industrial revolution 4.0 and society 5.0. One of these digitalization products is a digital platform. Current advances in educational technology to improve the quality of learning in educational institutions include using PhET (Physics Environment Technologies) which is a type of virtual laboratory. This research aims to produce a development product in the form of a FORCHEM (Formulation Chemistry Learn and Challenge) application in Virtual Laboratory with a bilingual class system on cosmetic chemistry material at high school level. The research method used is RnD to produce certain products and test the effectiveness of these products in relation to scientific literacy, and FORCHEM innovative learning platform is designed in the form of a virtual laboratory-based application with an online bilingual class system which is equipped with several superior features, one of which is the virtual lab feature and SiBi Microteaching in it, in addition to transferring knowledge and implementing it, it also introduces and brings generation Z into entrepreneurship through the creation of cosmetic products. which is carried out and provides character values in the midst of area megatrends, in order to form a golden generation in 2045 through expanding opportunities for access to higher and better quality education. FORCHEM is able to form 4C (Critical Thinking and Problem Solving, Communication, Creative and Innovation, and Collaboration) through scientific literacy, as well as innovative learning platforms that are disability-friendly and sustainable.

Clustering Regional Educational Performance in Indonesia Using K-Means

Herwindo Bagus Saputro, M. Mujiya Ulkhaq — Wed, 31 Dec 2025 00:00:00 +0000

This study examines regional disparities in educational development across Indonesia by clustering 38 provinces based on indicators of SDG 4 (Quality Education). Using the k-means algorithm with k = 4, the analysis identifies groups of provinces with similar educational profiles to support evidence-based policymaking. The resulting clusters reveal substantial heterogeneity. Cluster 1 (highly disadvantaged) consists of Papua Pegunungan and Papua Tengah, which exhibit the lowest national performance across schooling and attendance indicators. Cluster 2 (disadvantaged) includes 20 provinces with low to moderate achievement levels, including short average schooling duration and low upper-secondary completion. Cluster 3 (advanced) comprises 15 provinces with relatively strong educational outcomes. Cluster 4 (highly advanced) is represented solely by the Special Region of Yogyakarta, demonstrating markedly superior performance. These findings highlight persistent educational inequality and suggest differentiated policy priorities. Interventions for lagging clusters should focus on improving access, teacher quality, and infrastructure, particularly in remote and disadvantaged regions. By providing an empirically derived typology of provincial education performance, this study contributes to better-targeted strategies for achieving SDG 4 and reducing regional disparities in Indonesia.

Designing a Looker Studio-Based Analytics Dashboard for Flight Delay Analysis

Wina Elsa Wardana Wardana — Wed, 31 Dec 2025 00:00:00 +0000

The development of digital technology in the Industrial Revolution 4.0 era has encouraged intensive data utilization in various sectors, including the aviation industry. One of the main problems faced by airlines is flight delays, which have a significant impact on operations, costs, and customer satisfaction. This study aims to analyze flight delay patterns using the Flight Delays dataset from Kaggle and to design an interactive dashboard based on Looker Studio to support decision-making. The methods used include data collection, data cleaning, statistical analysis (One-Way ANOVA, linear regression, and Chi-Square test), and data visualization. The results show that there are significant differences in the average delay between airlines, flight distance has a very weak effect on delays, and the airport of origin has a significant relationship with delay occurrences. The resulting dashboard is able to provide comprehensive insights regarding delay factors so that it can be used in optimizing flight operations.

WebGIS-Based Diagnosis of Economic Vulnerability: Implementing the Inflation Risk-Burden Matrix via a Spiral Development Framework

Wed, 31 Dec 2025 00:00:00 +0000

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.

AI-Enhanced Coastal Ecosystem Monitoring for Abrasion and Mangrove Decline Detection Using NDVI and CNN Models

Wed, 31 Dec 2025 00:00:00 +0000

Coastal ecosystems in Indonesia are increasingly threatened by accelerating abrasion and severe mangrove degradation, especially in Mangunharjo, Semarang, where shoreline retreat continues to endanger local communities and ecological stability. This study aims to develop an AI-driven monitoring framework for detecting coastal abrasion and mangrove loss using Normalized Difference Vegetation Index (NDVI) combined with a Convolutional Neural Network (CNN) classifier. Multispectral data from Sentinel-2 imagery were processed to extract NDVI time-series from 2015 to 2025, followed by image preprocessing, normalization, and CNN-based classification. The model identifies abrasion-affected zones and declining mangrove cover, while the geospatial dashboard visualizes risk levels and restoration priority areas. Experimental results show that the CNN–NDVI model achieves high accuracy in distinguishing stable and abrasion-prone regions, with clear detection of vegetation loss patterns along the western coastline of Mangunharjo. The developed dashboard successfully integrates prediction output, interactive mapping, and AI-assisted recommendations for mangrove restoration. In conclusion, this system demonstrates the potential of combining satellite data, CNN-based analysis, and geospatial visualization to support data-driven decision-making for coastal ecosystem management and sustainable environmental planning.