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Muhammad Zainudin Al Amin
Contact Email
zainudin@unimus.ac.id
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+6285117483483
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jcase@unimus.ac.id
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GKB2 Unimus Building, Kedungmundu Raya Street No. 18, Tembalang, Semarang City, Central Java 50273, Indonesia
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Kota semarang,
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INDONESIA
Journal of Computing and Smart Ecosystems
Published by Universitas Muhammadiyah Semarang
ISSN : -     EISSN : 31105777     DOI : https://doi.org/10.26714/j-case
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
The scope of J-CaSE covers various topics, including artificial intelligence, the Internet of Things (IoT), big data analytics, cybersecurity, software engineering, and cloud and edge computing. It also explores the application of technology in smart city development and sustainable systems that support modern life. In addition, the journal welcomes research on the governance, policy, and ethical dimensions of emerging technologies, such as AI policy and regulation, data privacy frameworks, and algorithmic accountability. Studies related to smart city policy development, digital governance, and inclusive urban technology strategies are also within the journal’s scope.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 12 Documents
 1   2 
AI-Enhanced Coastal Ecosystem Monitoring for Abrasion and Mangrove Decline Detection Using NDVI and CNN Models Muhammad Ivan Ardiansyah; Saeful Amri; Basirudin Ansor; Wendy Sarasjati; Anggry Windasari; Gansar Timur Pamungkas
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

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.
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.

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