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Muhammad Zainudin Al Amin
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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 18 Documents
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Integrating Shortest Job First (SJF) Scheduling with Neural Networks for Enhanced Predictive Process Scheduling Aditya Putra Ramdani; Midda Restia Primadani; Fari Katul Fikriah; Atika Mutiarachim
Journal of Computing and Smart Ecosystems Vol. 1 No. 1 (2025): J-CaSE
Publisher : S1 Teknologi Informasi, Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jcase.v1i1.746

Abstract

Process scheduling is a critical component of operating systems, directly influencing CPU utilization and overall system efficiency. The Shortest Job First (SJF) algorithm is theoretically optimal in minimizing average waiting time but is limited by its dependence on accurate burst time estimation. This study proposes a hybrid scheduling approach that integrates neural networks (NN) with SJF to dynamically predict process execution times. The neural model was trained on process-level features, including CPU usage, memory usage, priority, and arrival time, and its predictions were embedded into the SJF mechanism. Simulation results demonstrate that the NN-enhanced SJF achieves notable reductions in average waiting time and turnaround time while improving CPU utilization compared to traditional SJF and Round Robin algorithms. These findings highlight the practical viability of lightweight predictive models for enhancing classical scheduling techniques and extend their applicability to dynamic and heterogeneous computing environments.
From Text to Action: AI-Driven Classification of Public Service Complaints in Karanganyar, Indonesia Muhammad Zainudin Al Amin; Farel Imam Maulana; Riefandi Dwiki Surya Putra; Mohammad Nurul Huda
Journal of Computing and Smart Ecosystems Vol. 1 No. 1 (2025): J-CaSE
Publisher : S1 Teknologi Informasi, Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jcase.v1i1.747

Abstract

Efficiently classifying public complaints is crucial for fostering transparent and responsive governance in the digital age. However, the sheer volume and textual nature of complaint data pose significant challenges for manual categorization, particularly within local government systems. This study seeks to develop an automatic classification model for public complaints by employing Logistic Regression and TF-IDF vectorization. The dataset, comprising complaints submitted to the Karanganyar Regency Government from January to June 2025, underwent preprocessing through standard natural language techniques and was converted into numerical features using TF-IDF. Logistic Regression was chosen for its simplicity, interpretability, and effectiveness with sparse text data. To address class imbalance, class weighting and stratified sampling were utilized. The model achieved an overall accuracy of 61%, surpassing the Naive Bayes baseline. Confusion matrix analysis demonstrated strong performance in dominant categories, although minority classes continued to present challenges. The results suggest that Logistic Regression offers a practical and explainable solution for early-stage complaint classification systems, especially in public sector contexts. This study lays the foundation for the future development of intelligent e-government platforms capable of real-time complaint handling.
Evaluating Usability And Customer Satisfaction in E-Marketplaces Using System Usability Scale and Customer Satisfaction Score Nova Christina Sari; Yusa Putra; Alfa Hikmatun Nabilah; Revania Jeni Puspitasari
Journal of Computing and Smart Ecosystems Vol. 1 No. 1 (2025): J-CaSE
Publisher : S1 Teknologi Informasi, Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jcase.v1i1.753

Abstract

In the digital era, user experience (UX) plays a critical role in determining the success of online platforms, particularly in the highly competitive e-commerce industry. This study presents a comparative evaluation of three major e-commerce applications in Indonesia namely Shopee, Tokopedia, and Lazada using two standardized instruments: the System Usability Scale (SUS) and the Customer Satisfaction Score (CSAT). A total of 100 respondents participated in the survey, which included 10 questions mapped to SUS and 10 questions mapped to CSAT. The results revealed that all three platforms scored poorly in usability and customer satisfaction metrics. Shopee achieved the highest SUS score 51, categorized as marginal, while Tokopedia 50 and Lazada 49.55. CSAT scores followed a similar pattern, with Shopee 52%, Tokopedia 55%, and Lazada 50% falling into low or very low satisfaction categories. These findings highlight the need for substantial improvements in both usability and service quality to enhance overall user experience and engagement. This study emphasizes the importance of integrating both SUS and CSAT methods to obtain a holistic understanding of user perceptions
Enhancing Conceptual Understanding of the Solar System Through 3D Augmented Reality in Primary Education Eva Febyliana; Teuku Zaine Abror Attolok; Diaz Aditya; Raina Artika Ramadlonia; Taufik Ismail; Muhammad Zainudin Al Amin
Journal of Computing and Smart Ecosystems Vol. 1 No. 1 (2025): J-CaSE
Publisher : S1 Teknologi Informasi, Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jcase.v1i1.782

Abstract

The advancement of digital technologies has introduced new methods in science education, including the use of Augmented Reality (AR). Traditional astronomy instruction often relies on two-dimensional media, which limits students’ ability to visualize and interact with celestial phenomena. This paper presents EduPlanet, a 3D AR-based educational application designed to enhance students’ understanding of the solar system. The application allows users to explore planets interactively, visualize orbital movements, and access informative content in real time. Developed using Unity and Vuforia SDK, EduPlanet consists of three main modules: learning content, marker-based AR visualization, and a quiz system with instant feedback. Functional testing using the Black Box method confirmed that all features performed as intended. Informal usability testing with elementary school students showed high levels of engagement, particularly in the AR and quiz components. The findings suggest that EduPlanet offers an effective and accessible tool to support astronomy learning in primary education, with potential for broader application in digital science pedagogy.
Development Potential of AR Anatomy as an Interactive Learning Medium for Elementary Science Education Muhammad Fiqri Zulfikar; Nabila Ismawarni.Ka; Dyah Ayu Kusumaningtyas; Syahrul Ramadhon; Irba Ilzami Al Haq; Muhammad Zainudin Al Amin
Journal of Computing and Smart Ecosystems Vol. 1 No. 1 (2025): J-CaSE
Publisher : S1 Teknologi Informasi, Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jcase.v1i1.784

Abstract

In the era of the Industrial Revolution 4.0 and Society 5.0, education is expected to deliver adaptive, interactive, and contextual learning experiences. One of the main challenges in elementary science education lies in teaching abstract concepts such as human anatomy, where traditional textbooks and two-dimensional images often fail to support deep understanding. Augmented Reality (AR) offers a promising alternative by enabling the real-time visualization of three-dimensional (3D) models through mobile devices. This study explores the development and potential of “AR Anatomy,” a camera-based AR application designed to provide interactive visualizations of human organs for elementary students. Using a qualitative literature review of recent studies published after 2022, the analysis indicates that AR Anatomy can enhance student motivation, active engagement, and spatial understanding, while offering a cost-effective alternative to physical anatomical models. Nevertheless, limitations remain, including restricted organ coverage, lack of integrated evaluation features, and limited alignment with the national curriculum. In conclusion, AR Anatomy represents a promising step toward technology-enhanced science education at the elementary level, with further refinement needed to improve content coverage and classroom implementation.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jcase.v1i2.786

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.
The Use of Artificial Intelligence for Cyber Threat Detection: A Systematic Literature Review of Research Methods, Accuracy, and Gaps Muhammad Distian Andi Hermawan
Journal of Computing and Smart Ecosystems Vol. 1 No. 2 (2025): J-CaSE
Publisher : S1 Teknologi Informasi, Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jcase.v1i2.890

Abstract

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 Nilna Inayatan Nafiah; Dika Putra Wijaya; Cahya Adidharma; Shefira Salvabila Safitri; Revianti Aisyah Safitri; Fina Kharisma Musallamah; Aura Gitta Zhafirah; Marshya Qurrotul Aini Wibowo; Ulfa Rahmawati; Nani Farida; Sumari Sumari; Danar Danar
Journal of Computing and Smart Ecosystems Vol. 1 No. 2 (2025): J-CaSE
Publisher : S1 Teknologi Informasi, Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jcase.v1i2.903

Abstract

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
Journal of Computing and Smart Ecosystems Vol. 1 No. 2 (2025): J-CaSE
Publisher : S1 Teknologi Informasi, Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jcase.v1i2.904

Abstract

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.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jcase.v1i2.906

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.

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