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All Journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Journal of Marine and Coastal Science Jurnal Teknik Informatika (JUTIF)
Setyanto, Arif
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Immunology & microbiology

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Community Structure of Fishes the Association with Seagrasses at Bama Beach, Baluran National Park, Situbondo, East Java Setyanto, Arif; Andriani, Dina; Sulkhani Yulianto, Eko; Tumulyadi, Agus; Bintoro, Gatut; Djoko Lelono, Tri; Adhihapsari, Wirastika; Nur Hidayah, Lisa; Isdianto, Andik; Zakiyah, Umi; Lanudia Fathah, Aulia; Kurnia Wardana, Novar; Mahardika Putri, Berlania
Journal of Marine and Coastal Science Vol. 13 No. 3 (2024): SEPTEMBER
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jmcs.v13i3.61211

Abstract

Seagrass bed has an important role for biota in waters. The existence of fish in seagrass is influenced by other ecosystems close to seagrass, such as mangroves and coral reefs. This study focuses on fish associated with seagrass in Bama Beach. The data include seagrass coverage and the number of fish for the Diversity, Uniformity, and Dominance. The study was carried out on March 15 to 20, 2018 using the Underwater Visual Census. Results show that seagrass cover was highest in seagrass stations near mangroves (62,66%), while at stations near coral reefs was 37,66. The composition of fish associated with seagrass near mangroves was 361 individuals, while near reefs was 1.454 individuals. The values of diversity, uniformity, and dominance of fish associated with seagrass near mangroves are 2,62; 0,88; and 0,09 respectively while those associated with coral reefs have values of 2,93; 0,85; and 0,06. Family Aulostomidae was dominant in the morning at the station near mangroves, and in the afternoon was dominated by Apogonidae. At the station near coral reefs, the Family Pomacentridae was dominant both in the morning and afternoon. The seagrass conditions in this study are in the healthy category with quite diverse fishes. The level of fish diversity in seagrass was influenced by habitats associated with seagrasses than seagrass cover levels. The condition of Bama Beach as a conservation area is quite good, but the activity of tourists around the coastline must be monitored to maintain the environment.
Environmental Acoustic Features Robustness Analysis: A Multi-Aspecs Study Semma, Andi Bahtiar; Kusrini, Kusrini; Setyanto, Arif; da Silva, Bruno; Braeken, An
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23723

Abstract

Abstract—Background: Acoustic signals are complex, with temporal, spectral, and amplitude variations. Their non-stationarity complicates analysis, as traditional methods often fail to capture their richness. Environmental factors like reflections, refractions, and noise further distort signals. While advanced techniques such as adaptive filtering and deep learning exist, comprehensive acoustic feature analysis remains limited.  Objective: This study investigates which acoustic features maintain the highest robustness across diverse environments while preserving discriminative power.  Methods: Audio samples were recorded in controlled environments (jungles, cafés, factories, streets) with varying noise levels. Standardized equipment captured 22050 Hz, 16-bit audio at multiple positions and distances. After amplitude standardization, various acoustic features were extracted and analyzed.  Results: MFCCs demonstrated exceptional reliability, with correlation coefficients of 0.98819 and 0.98889 for closely positioned devices and a robustness score of 0.99. Across different acoustic scenes and sample lengths (1, 3, 5s), MFCCs maintained high correlation (≈0.978) and robustness (0.98), confirming their versatility.  Conclusion: MFCCs proved highly effective for acoustic fingerprinting across settings. Despite limitations in tested environments (≤5m distance, ≤5s samples), their consistent performance validates the methodology. Future research should explore combining MFCCs with spectral features and expanding studies to broader environments and device types.
Comprehensive Systematic Review of TinyML Edge Deployment: Optimization Techniques, Application Domains, and Hardware Ecosystems Bakti, Very Kurnia; Setyanto, Arif; Muhammad, Alva Hendi; Wibowo, Ferry Wahyu
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5541

Abstract

The Internet of Things (IoT) is growing rapidly, making it even more crucial to deploy Machine Learning (ML) models directly on edge devices with limited resources. TinyML fixes this matter by giving microcontroller-class hardware the ability to think for itself. This makes it less reliant on the cloud and better for latency, energy efficiency, and data privacy. This study offers a comprehensive Systematic Literature Review (SLR) of TinyML research published between 2021 and 2025, in accordance with PRISMA principles. We identified 429 records, removed 326 duplicates, and added 83 studies to the final synthesis. The evaluation examines five research inquiries concerning optimization techniques, streamlined architectures, sophisticated learning frameworks, application sectors, and hardware ecosystems. The findings underscore four key themes: enhancing models, utilizing specialized tools and technology, and adapting strategies. Some of the challenges that keep recurring are broken ecosystems, different benchmarking approaches, and on-device learning that isn't compelling when ideas shift. This research presents an open-access taxonomy that categorizes optimization techniques, application trends, and hardware constraints, thereby laying the foundation for a TinyML research agenda within the informatics community. Future directions highlight the importance of adaptive TinyMLOps pipelines, federated learning, LLM-assisted model design, and NVM-based computing to support scalable and sustainable edge intelligence. The results underscore the relevance of TinyML for advancing informatics and computer science, particularly in enabling secure, efficient, and environmentally aligned IoT systems that support SDG 9 and SDG 12.
Co-Authors Alva Hendi Muhammad Andik Isdianto Braeken, An da Silva, Bruno Dina Andriani Djoko Lelono, Tri Ferry Wahyu Wibowo Gatut Bintoro Kusrini Lanudia Fathah, Aulia Mahardika Putri, Berlania Nur Hidayah, Lisa Semma, Andi Bahtiar Sulkhani Yulianto, Eko Tumulyadi, Agus Umi Zakiyah, Umi Very Kurnia Bakti, Very Kurnia Wirastika Adhihapsari