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Hindayati Mustafidah
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Gedung Fakultas Teknik dan Sains Universitas Muhammadiyah Purwokerto Jl. K.H. Ahmad Dahlan, Dukuh Waluh, Kembaran, Banyumas, Central Java, Indonesia
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JUITA : Jurnal Informatika
Published by Universitas Muhammadiyah Purwokerto
ISSN : 20869398     EISSN : 25798901     DOI : 10.30595/JUITA
Core Subject : Science,
Computer Science & IT
UITA: Jurnal Informatika is a science journal and informatics field application that presents articles on thoughts and research of the latest developments. JUITA is a journal peer reviewed and open access. JUITA is published by the Informatics Engineering Study Program, Universitas Muhammadiyah Purwokerto. JUITA invites researchers, lecturers, and practitioners worldwide to exchange and advance knowledge in the field of Informatics. Documents submitted must be in Ms format. Word and written according to author guideline. JUITA is published twice a year in May and November. Currently, JUITA has been indexed by Google Scholar, IPI, DOAJ, and has been accredited by SINTA rank 2 through the Decree of the Director-General of Research and Development Strengthening of the Ministry of Research, Technology and Higher Education No. 36/E/KPT/2019. JUITA is intended as a media for informatics research among academics, practitioners, and society in general. JUITA covers the following topics of informatics research: Software engineering Artificial Intelligence Data Mining Computer network Multimedia Management Information System Digital forensics Game
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 23 Documents
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Search results for , issue "JUITA Vol. 14 Issue 1, March 2026" : 23 Documents clear
Design and Development of an Augmented Reality Based Storytelling Platform for Interactive Solar System Learning in Primary Education I Putu Andika Subagya Putra; Ni Wayan Sri Darmayanti; Putu Beny Pradnyana; Luh Made Dwi Wedayanthi
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.29157

Abstract

Concepts in primary science education, such as the Solar System, are often difficult for students to understand when presented through conventional two-dimensional learning media. This study aims to design and develop a storytelling-driven Augmented Reality (AR) learning platform to support interactive Solar System learning in primary education. A Research and Development (R&D) approach was employed, encompassing needs analysis, conceptual design, prototype development, limited trials, and formative evaluation. The developed platform enables learners to visualize three-dimensional planetary models using marker-based AR accompanied by age-appropriate narrative explanations. Limited trials involving ten fourth-grade students and two science teachers were conducted to examine usability and user perceptions. The findings indicate that students and teachers perceived the platform as engaging and supportive for visualizing abstract astronomical concepts. The novelty of this study lies in the integration of structured storytelling with AR visualization tailored for primary learners within an R&D framework. However, the results are based on formative evaluation and user perceptions; therefore, further studies with larger samples and objective learning outcome measures are recommended.
Prediction of Potential Fishing Zones Using K-Means Clustering and Random Forest in Batam Waters Sarah Astiti; Alvendo Wahyu Aranski; Darmansah
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.29679

Abstract

Identification of potential fishing zones remains a significant challenge in fisheries management, particularly in coastal and island waters characterized by high spatial and temporal environmental variability. In Batam waters, fishing activities are still dominated by fishermen's experience and heuristic judgment, while existing studies often focus on a single prediction model or limited environmental parameters. This indicates a research gap, namely the lack of an integrated framework that simultaneously captures environmental heterogeneity and improves prediction accuracy using a data-driven approach. To address this gap, this study proposes a hybrid data mining framework that explicitly integrates unsupervised environmental zoning and supervised classification for predicting fishing potential. Weather and oceanographic variables—including sea surface temperature, chlorophyll-a concentration, wind speed, ocean current speed, and salinity—are used in conjunction with historical fish catch data. K-Means clustering is first used to identify homogeneous marine environmental zones, which are then incorporated as contextual features into a Random Forest classification model. Model performance is then evaluated using accuracy, precision, recall, F1 score, and confusion matrix analysis. The results show that the proposed hybrid approach achieves an accuracy of 89.2% and an F1 score of 89.1%, representing a quantitative improvement of approximately 5.6% in accuracy and 5.0% in F1 score compared to the baseline Random Forest model without clustering. This comparison clearly demonstrates that the integration of clustering information significantly improves classification performance. Furthermore, feature importance analysis confirms that sea surface temperature and chlorophyll-a concentration are the most influential predictors, while cluster labels contribute indirectly by improving the model's contextual understanding of complex environmental conditions. The novelty of this research is articulated through the integration of unsupervised marine environmental zoning with supervised machine learning in a local fisheries context, which allows for improved predictive performance and enhanced model interpretability. Unlike conventional approaches that treat environmental variables independently, the proposed framework captures multidimensional environmental interactions in a structured manner. The implications of these findings are profound. The proposed model can support data-driven decision-making for fishermen by reducing search time and operational costs, while providing a scientific basis for fisheries managers for spatial planning and sustainable resource management. Therefore, this research contributes both methodologically and practically to the advancement of intelligent fisheries prediction systems in dynamic coastal environments such as Batam waters.
Editor Preface and Table of Content JUITA Jurnal Informatika
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Editor Preface and Table of Content Vol. 14 Issue 1, March 2026

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