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Eva Khudzaeva
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Department of Information System, Faculty of Science and Technology, Universitas Islam Negeri Syarif Hidayatullah Jakarta Jl. Ir. H. Juanda No.95, Cempaka Putih, Ciputat Timur. Kota Tangerang Selatan, Banten 15412
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Applied Information System and Management
Published by Universitas Islam Negeri Syarif Hidayatullah Jakarta
ISSN : 26212536     EISSN : 26212544     DOI : 10.15408/aism
Core Subject : Education,
Languange, Linguistic, Communication & Media
Arjuna Subject : -
Articles 306 Documents
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Genetic Algorithm–Optimized Clustering for University Promotion Target Recommendation Ulla Delfana Rosiani; Clauria Dwi Putri Nabillah; M. Hasan Basri; Ahmadi Yuli Ananta; Yushintia Pramitarini
Applied Information System and Management (AISM) Vol. 9 No. 1 (2026): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v9i1.50061

Abstract

Competition among higher education institutions demands promotional strategies that are more targeted and data-driven. This study proposes a clustering-based recommendation model for determining university promotion targets by integrating Genetic Algorithm (GA) optimization into three clustering methods: K-means, Fuzzy C-means (FCM), and K-medoids. The dataset consists of 925 student records (cohorts 2021–2023) from the Information Technology Department, with the selected attributes including school origin, NPSN, school location (city and province), and GPA. Clustering performance was evaluated using the Davis-Bouldin Index (DBI) and the Silhouette Coefficient as primary metrics, with intra- and inter-cluster distances as supporting indicators. The results show that GA-K-means achieves the best performance at K = 3, with a DBI of 1.2792 and a Silhouette Coefficient of 0.2876, and the improvement is statistically significant (p < 0.05). GA optimization also improves FCM performance but does not significantly improve K-medoids performance. Although the GA increases computational time by approximately two to three times, the improvement in clustering quality justifies its use in non-real-time decision-support scenarios. The proposed model enables universities to determine promotion targets in a more objective, adaptive, and data-driven manner, supporting strategic decision-making in higher education promotion.
Application of EfficientNet Transfer Learning with Incremental Fine-Tuning for Road Damage Detection Riki Winanjaya; Abdi Rahim Damanik; Anton Abdulbasah Kamil
Applied Information System and Management (AISM) Vol. 9 No. 1 (2026): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v9i1.50124

Abstract

Image-based road damage detection is an essential component of intelligent infrastructure monitoring systems. However, conventional transfer learning often fails to adapt pre-trained models to domain-specific characteristics such as fine-crack textures, illumination variations, and perspective distortions. This study proposes an EfficientNet-based road damage classification model that leverages incremental fine-tuning and multi-stage data augmentation to enhance feature adaptation and model robustness. The experiments were conducted using the Road Damage Detection dataset from Kaggle, comprising 1,400 labeled images across several road damage classes. The dataset was partitioned into 80:10:10 splits for training, validation, and testing, with stratification. The proposed approach gradually unfreezes EfficientNet layers through a structured incremental fine-tuning schedule while applying staged augmentation to expand data diversity. Experimental results show that the baseline EfficientNet transfer learning model achieved 78.26% accuracy, whereas the proposed model improved performance to 97.10% accuracy, with 97.60% macro precision, 97.20% macro recall, and 97.30% macro F1-score. The results demonstrate that incremental fine-tuning effectively enhances feature adaptation to road damage textures, while multi-stage augmentation improves model robustness. These findings indicate that the proposed approach provides an effective strategy for improving deep-learning-based road damage detection systems in real-world infrastructure monitoring applications.
Clustering Analysis of E-Learning Readiness in Java Island Indonesia with GIS Visualization Eva Khudzaeva; Qurrotul Aini; Evy Nurmiati; Ismi Ana Sulasiyah; Ibrahim Shehu Usman
Applied Information System and Management (AISM) Vol. 9 No. 1 (2026): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v9i1.50267

Abstract

E-learning readiness (e-readiness) is used as a tool to measure the success rate of using ICT in the academic process. Until now, a lot of research on e-readiness has been collected in various regions, especially Java Island, but the data has not been grouped and visualized, so it is difficult to know the level of readiness. The purpose of this study is to group e-readiness data using clustering analysis, then create a GIS-based map of the distribution of e-readiness clusters. To obtain optimal clustering results, researchers used the K-means and PCA combination as a cluster optimization method. The total dataset used is 27 locations' data with 2 parameters selected based on the level of e-learning readiness. Based on the results of the performance analysis using the selected internal clustering validation metrics, specifically the Davies Bouldin Index (DBI) and the average within centroid distance, each metric indicates the best cluster with values of 0.057 and 0.001, respectively. The most optimal cluster formed using the K-means and PCA methods, with a total of three clusters spread across various areas on the island of Java. As for the division of each cluster by its location point, namely, cluster 1 (amounting to 20 locations) for the ready level, cluster 2 (amounting to 4 locations) for the less ready level, and cluster 3 (amounting to 3 locations) for the very ready level.  
A Systematic Review of Deep Learning and Computer Vision Methods for Accurate Object Volume Measurement Muhamad Achya Arifudin; Kusrini; Andi Sunyoto; Ferry Wahyu Wibowo
Applied Information System and Management (AISM) Vol. 9 No. 1 (2026): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v9i1.50286

Abstract

Precise and efficient object volume measurement is crucial across diverse industrial domains, including logistics, manufacturing, and agriculture, where traditional methods are often labor-intensive and error-prone. DL and CV technologies offer a compelling alternative, providing greater accuracy, speed, and safety through non-contact, real-time, and adaptive solutions. To address existing knowledge gaps, this SLR is believed to be the first to integrate and analyze the three critical dimensions of DL holistically- and CV-based volume measurement: core methodologies, practical applications across various industries, and outstanding challenges, thereby providing a unified and comprehensive understanding of the state of the art that previous, more fragmented reviews have failed to deliver. Regarding methodology, dominant DL techniques include CNNs, Mask R-CNN, and U-Net for segmentation; GANs for 3D model generation; and PointNet/voxel networks for 3D data processing, with sensor integration impacting model architecture. Applications span agriculture, logistics, manufacturing, and construction, demonstrating high accuracy with error rates as low as 0.75% and MAPE typically ranging from 3.2% to 5%. Challenges involve occlusions, diverse environmental conditions, data scarcity, and computational costs. Prioritized research directions include lightweight models, multi-task learning, improved generalization, greater robustness, and explainable AI. Overall, this SLR comprehensively synthesizes current DL and CV methodologies, their practical applications, and future research directions in object volume measurement.
From Participation to Legislative Knowledge: A Bibliometric Review of E-Participation Research in Legislative Institutions Rahayu Yuni Susanti; Marimin; Dikky Indrawan; Nur Hasanah
Applied Information System and Management (AISM) Vol. 9 No. 1 (2026): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v9i1.50289

Abstract

This study provides a comprehensive science map of e-Participation research in legislative institutions, addressing the fragmentation and lack of connection between citizen input and internal knowledge management. The purpose is to map the field's intellectual structure, evaluate the traceability of citizen inputs into parliamentary outputs, and identify strategic research gaps. Using the SPAR-4-SLR framework and PICOS-based screening, 41 empirical journal articles were selected from the Scopus database for bibliometric analysis using Bibliometrix and VOSviewer. Findings indicate that the field is dominated by European contexts and grounded in participation theory, institutional implementation, and behavioral adoption models. While recent trends show an increasing focus on advanced analytics and artificial intelligence, existing literature fails to explain how e-Participation mechanisms integrate with internal knowledge management. Specifically, there is no standardized metric to trace citizen input from initial submission to final committee recommendations or legislative amendments. This study contributes by offering an integrated scientific map tailored to legislative settings and proposing the Legislative Knowledge Transformation Model (LKTM). This model provides strategic guidance and practical indicators to strengthen evidence-based and transparent legislation. By establishing a reproducible pipeline for managing participatory data, the research paves the way for comparative validation in developing countries to enhance parliamentary oversight and institutional legitimacy. 
SWOT-Based Strategic Development for Enhancing MSMEs Digital Marketing Performance in Banda Aceh Meutia Fadilla; Hazful Maizi; Friesca Erwan
Applied Information System and Management (AISM) Vol. 9 No. 1 (2026): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v9i1.50364

Abstract

This study identifies the internal and external factors influencing the digital marketing performance of Micro, Small, and Medium Enterprises (MSMEs) in Banda Aceh to formulate a context-based strategic roadmap for improving digital competitiveness. The research addresses a gap left by previous studies, which focused primarily on individual platform use and sales outcomes, overlooking digital capability management as an applied information systems issue. Using a descriptive qualitative approach, the study involved 9 MSME owners from the culinary, fashion, and service sectors, selected through purposive sampling. Data were collected through semi-structured interviews, digital content observations, marketplace platforms, and document reviews. The analysis used thematic coding consisting of open, axial, and selective coding, with results mapped into a SWOT matrix. Findings indicate that the primary strengths lie in cultural storytelling, adaptability to trends, and customer loyalty. At the same time, the weaknesses include unstructured content management, weak visual branding, limited understanding of analytics, and low cybersecurity awareness. Externally, opportunities arise from government training and digital payment adoption, whereas threats include algorithmic changes and account security risks. The study contributes by repositioning digital marketing as a digital capability management problem and providing strategic guidance for MSME owners, local governments, and supporting institutions to foster sustainable digital transformation in regional contexts. 

Page 31 of 31 | Total Record : 306

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