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All Journal Abdisembrani: Jurnal Pengabdian Masyarakat JOMAFISH
Jumiati
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Pemeriksaan Pertumbuhan dan Perkembangan Balita di Posyandu Desa Maindu Kecamatan Montong Kabupaten Tuban Lia Listiana Wati; Jumiati; Heva Risnaini Kurniyah; Mutiara Hendrawati
Jurnal ABDILAWE Vol 1 No 2 (2023): Oktober 2023
Publisher : Lembaga Pengabdian Kepada Masyarakat Universitas PGRI Ronggolawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55719/as.v1i2.852

Abstract

Kegiatan pemeriksaan pertumbuhan dan perkembangan balita di posyandu ini bertujuan meningkatkan pengetahuan ibu tentang pertumbuhan tinggi dan berat badan pada balita, meningkatkan motivasi ibu melakukan pemeriksaan pertumbuhan dan perkembangan balita ke tempat Posyandu disekitar. Pencapaian tujuan kegiatan tersebut dilakukan melalui pemberian penyuluhan kesehatan dengan metode kuantitatif, observasi dan deskriptif. Kegiatan ini dilakukan kader-kader, ibu-ibu dan KKN Kelompok 10 UNIROW untuk mengamati dan membantu bidan posyandu dalam mengukur tinggi dan berat badan balita. Hasil penelitian yang telah dilakukan menunjukkan bahwa program ini berjalan dengan baik dan lancar serta dapat memberi motivasi ibu-ibu dalam memantau pertumbuhan serta perkembangan pada balita. Apabila tinggi dan berat badan balita kurang normal, maka anak tersebut bisa dikatakan kekurangan gizi sehingga berdampak pada imunitas balita. Sehingga untuk mengatasi hal tersebut, maka dilakukan dengan metode diskusi yang mendalam. Saran yang dapat diajukan dari hasil program kegiatan pemeriksaan pertumbuhan dan perkembangan balita yaitu bagi ibu yang memiki balita disarankan mempunyai motivasi yang tinggi untuk mendapatkan pengetahuan mengenai pertumbuhan dan perkembangan balita dengan cara lebih sering membawa anaknya ke posyandu. Selain itu, petugas kesehatan atau bidan serta kader-kader posyandu setempat diharapkan untuk selalu memberi informasi jadwal terkait kesehatan balita untuk membantu ibu dalam meningkatkan pengetahuannya
Remote Sensing for Mangrove Mapping and Monitoring: Sensors, Indices, Algorithms, and Applications Luhur Moekti Prayogo; Perdana Ixbal Spanton M; Jumiati
JOMAFISH: Journal of Marine and Fisheries Science Vol. 1 No. 1 (2026): November
Publisher : GRM Academic Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67710/jomafish.v1i1.26

Abstract

Mangrove forests are spatially dynamic intertidal ecosystems whose monitoring is constrained by difficult field access, tidal variation, cloud cover, spectral similarity with terrestrial vegetation, and rapid land-use change. Remote sensing has therefore become a central tool for mapping mangrove extent, condition, species composition, structural attributes, and temporal change. This review synthesizes recent developments in optical, synthetic aperture radar (SAR), hyperspectral, unmanned aerial vehicle (UAV), and LiDAR approaches for mangrove mapping, with emphasis on freely available Landsat and Sentinel data and applications relevant to tropical coasts. Studies published mainly during 2016–2026 were examined across four analytical dimensions: sensor characteristics, spectral or structural features, classification algorithms, and accuracy assessment. The evidence shows that Landsat remains valuable for long-term change analysis, whereas Sentinel-2 improves detection of narrow and fragmented stands through 10-m observations. Combining Sentinel-1 SAR and Sentinel-2 optical time series generally improves robustness in persistently cloudy regions. Mangrove-specific indices such as the Mangrove Vegetation Index can simplify extent mapping, while hyperspectral imagery, UAV data, and LiDAR are increasingly important for species discrimination, canopy height, biomass, and restoration assessment. However, no sensor or index removes the need for representative training data and independent validation. Tidal stage, mixed pixels, seasonal compositing, class imbalance, and spatially non-independent validation remain major sources of uncertainty. For Indonesia, an operational strategy should integrate multi-temporal Sentinel-1/2 data, machine learning, field observations, and standardized accuracy reporting to support repeatable mapping for conservation, restoration, blue-carbon accounting, and coastal management.
Satellite-Derived Bathymetry in Tropical Shallow Waters: Methods, Sensors, Accuracy, and Applications Wartono; Jumiati; Ade Hanie Nurfahanie; Luhur Moekti Prayogo
JOMAFISH: Journal of Marine and Fisheries Science Vol. 1 No. 1 (2026): November
Publisher : GRM Academic Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67710/jomafish.v1i1.28

Abstract

Accurate bathymetry is fundamental to coastal navigation, habitat assessment, shoreline management, hydrodynamic modelling, and fisheries planning, yet conventional hydrographic surveys remain costly and difficult in remote or very shallow tropical waters. Satellite-derived bathymetry (SDB) provides a complementary approach by estimating water depth from optical reflectance, wave properties, or spaceborne laser altimetry. This review synthesizes methodological developments in SDB with emphasis on tropical shallow-water environments and studies published from 2018 to August 2026, while retaining foundational algorithms required to explain current practice. The literature was organized according to sensor, preprocessing, calibration data, retrieval model, validation metric, and environmental limitation. Sentinel-2 has become the principal freely available optical source because its 10-m visible bands, five-day revisit, and global coverage are well matched to reef flats, lagoons, and island coasts. Landsat remains important for historical reconstruction, whereas PlanetScope and very-high-resolution imagery improve representation of narrow channels and small geomorphic features. ICESat-2 has substantially changed SDB by supplying independent along-track depth observations that can calibrate empirical and machine-learning models where field soundings are unavailable. Band-ratio and log-linear approaches remain robust baselines, but Random Forest, Support Vector Regression, neural networks, and multi-temporal fusion increasingly improve performance in complex waters. Accuracy nevertheless depends more strongly on optical depth, turbidity, bottom heterogeneity, sun glint, tide, temporal mismatch, and reference-data quality than on algorithm complexity alone. For tropical archipelagic regions such as Indonesia, an operational workflow should combine aquatic atmospheric correction, glint and cloud screening, tidal normalization, spatially independent validation, and explicit uncertainty reporting. SDB should therefore be treated as a scalable complement to hydrographic surveying rather than an unrestricted replacement for echo sounding.
Co-Authors Ade Hanie Nurfahanie Heva Risnaini Kurniyah Lia Listiana Wati Luhur Moekti Prayogo Mutiara Hendrawati Perdana Ixbal Spanton M Wartono