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All Journal JUITA : Jurnal Informatika JSAI (Journal Scientific and Applied Informatics) Fokus: Publikasi Ilmiah untuk Mahasiswa, Staf Pengajar dan Alumni Universitas Kapuas Sintang Jurnal Puan Indonesia Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) Computer & Science Industrial Engineering Journal
Darmansah
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Prediction of Potential Fishing Zones Using K-Means Clustering and Random Forest in Batam Waters Astiti, Sarah; Aranski, Alvendo Wahyu; Darmansah
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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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.
PENINGKATAN KOMPETENSI SISWA SEKOLAH MENENGAH KEJURUAN MELALUI PELATIHAN PEMROGRAMAN WEB BERBASIS HTML, CSS, DAN JAVASCRIPT Darmansah; Handoko, Koko; Adhiatma, Novri; Elisa, Erlin
JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) Vol. 6 No. 1 (2026): April
Publisher : Lembaga Penelitian Pengabdian Masyarakat Penerbitan dan Percetakan Indonesian Scholar Khiar Wafi (LPPMPP IKHAFI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54650/jpmtt.v6i1.655

Abstract

The development of digital technology demands increased student programming competency as part of strengthening digital literacy. However, information technology learning in high schools (SMA/SMK) is usually limited to the use of basic software and web programming. The purpose of this community service activity is to improve the skills of SMK Kolese Tiara Bangsa Batam students by teaching them the basics of web programming using HTML, CSS, and JavaScript. The activity was carried out in four stages: preparation, intensive hands-on training, and project-based learning. Evaluation was carried out through pre-tests and post-tests, and program follow-up. The evaluation results showed that the average score increased by 61.6% from 52.4 in the pre-test to 84.7 in the post-test. In addition, 86% of participants were able to complete a simple website project independently. The level of participant satisfaction with the training was very good. The results indicate that the practice-based training approach is effective in improving students' conceptual understanding and technical skills in website development. As a model for continuously strengthening students' digital competencies, this program can be implemented in other schools.
Co-Authors Adi Prastya Afrianto, Nurdi Alvendo Wahyu Aranski Amrizal Arnomo , Sasa Ani Budi Permana Putra Budy Satria Dori Gusti Alex Candra Elisa, Erlin Fitra Kasma Putra Handoko, Koko Harman, Rika Imam Asrori Meiditra, Irzon Novri Adhiatma Novri Adhiatma, Novri Ratnasari, Evy ROPIKA AYU YOLANDA Sarah Astiti S.Kom., M.MT Sirait, Ganda Sumantika, Arsyad Tomy Nanda Putra Victor Yunisa Oktavia zheng, Agustiawan