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Tingkat Partisipasi Kelompok Tani Hutan Karava Jaya dan Kelompok Tani Hutan Sumber Hidup Di Kecamatan Gumbasa Sirenden, Diana; Golar, Golar; Maiwa, Arman; Hulu, Amati Eltriman
Savana Cendana Vol 9 No 1 (2024): Savana Cendana (SC) - January 2024
Publisher : Fakultas Pertanian, Sains, dan Kesehatan, Universitas Timor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32938/sc.v9i1.2446

Abstract

The Forest Farmers Group (KTH) is an organizational entity that specifically focuses on managing forest resources through the active participation of members in planning, implementation and evaluation activities. In achieving the goals of KTH activities, participation from KTH members is required. This research aims to determine the level of participation of KTH members in KTH activities starting from the planning stage, implementation stage, utilization stage and evaluation stage. Respondents are members of two KTHs, namely KTH Jaya Karava and Sumber Hidup. Data collection was carried out through interviews and direct observation in the field. Data analysis used in this research uses quantitative descriptive analysis. The results of the research show that the level of participation of members of the KTH Jaya Karava group is relatively high, while the level of participation of KTH Sumber Hidup is relatively low.
Pemanfaatan Daun Kelor (Moringa Oleifera)  Sebagai Alternatif Pembuatan Teh Sari, Yudya Kurnia; Hasan, Siti Nurhalizah; Hulu, Amati Eltriman; Afianti, Afifah Suci; Kotambunan, Jesica; Hartini, Dewi Sri; Putri, Avril; Toknok, Bau; Maiwa, Arman; Rahman, Abdul; Pribadi, Hendra; Muthmainna, Muthmainna; Fitrah, Rhamdani
Jurnal Masyarakat Madani Indonesia Vol. 3 No. 3 (2024): Agustus
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/nct6cf19

Abstract

Moringa oleifera, yang biasanya disebut daun kelor, adalah tumbuhan yang mudah ditanam yang memiliki banyak manfaat kesehatan. Konsumsi lebih banyak cereal karena mengandung antioxidants, vitamins, dan mineral yang membantu tubuh menghasilkan lebih banyak energi setiap hari. Namun, banyak orang yang tidak tahu cara mudah mengumpulkan daun kelor atau manfaatnya. Baking soda adalah alternatif yang mudah dan sehat yang mudah dibuat dan dimakan.Tujuan dari pembuatan teh daun kelor ini adalah untuk mengedukasi masyarakat tentang manfaat daun kelor. Sebagai hasil dari metode pengabdian dengan memproduksi teh daun kelor, pemahaman masyarakat akan manfaat daun kelor akan meningkat. Pendekatan ini disebut pendampingan, atau belajar sambil melakukan
Deteksi Perubahan Tutupan Lahan menggunakan Citra Planetscope di Desa Pandiri Kecamatan Lage Kabupaten Poso Laia, Berkat; Muis, Hasriani; Misrah, Misrah; Hulu, Amati Eltriman
MAKILA Vol 19 No 2 (2025): Makila : Jurnal Penelitian Kehutanan
Publisher : Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/makila.v19i2.20733

Abstract

Land cover change reflects the dynamics of spatial use and the environmental condition of a region. This study aims to detect land cover changes in Pandiri Village, Lage District, Poso Regency, using PlanetScope satellite imagery acquired in 2019 and 2024. Land cover classification was performed using the Maximum Likelihood Classification (MLC) method, while change detection was conducted through a post-classification comparison of the resulting classified maps. The results identified six primary land cover classes: forest, dryland agriculture, open land, settlement, paddy fields, and water bodies. Over the 2019–2024 period, forest area decreased by 88.46 hectares, and dryland agriculture declined by 31.35 hectares. Conversely, increases occurred in open land (44.21 ha), settlement (24.23 ha), paddy fields (25.99 ha), and water bodies (25.38 ha). Accuracy assessment yielded an overall accuracy of 96.67% with a Kappa coefficient of 0.95, indicating a highly reliable classification. These findings confirm the capability of PlanetScope imagery in detecting village-scale land cover changes. The outcomes of this study are essential for supporting the evaluation of spatial planning policies and controlling land conversion to achieve sustainable natural resource management.
Assessing mangrove health index as a basis for degradation mitigation planning Toknok, Bau; Hulu, Amati Eltriman; Purnama, Rizky; Panuntun, Madina Dwi; Zamani, Istiqomah Shariati; Hasibuan, Dwi Kartika Asih
Journal of Degraded and Mining Lands Management Vol. 13 No. 2 (2026)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2026.132.9953

Abstract

This study aimed to detect mangrove cover, analyze spatio-temporal changes, and assess mangrove health conditions in South Banawa District, Donggala Regency, using multi-temporal Sentinel-2A imagery. Mangrove detection was conducted using a machine learning based Decision Tree algorithm, while mangrove health was evaluated using the Mangrove Health Index (MHI). The variables included spectral bands and multiple spectral indices (NDVI, NDBI, MNDWI, CMRI, NBR, GCI, SIPI, and ARVI). The classification model demonstrated very high performance, with Overall Accuracy, Kappa, and F1-Score values exceeding 98%. The results indicated a decline in mangrove area from 123.96 ha to 95.5 ha, equivalent to a loss of 28.46 ha (22.96%) during the observation period. Degradation was spatially concentrated in areas with high accessibility and proximity to shrimp farming activities. Despite this decline, mangrove conditions were predominantly classified as healthy (87.56%), followed by moderate (12.41%) and poor (0.03%) categories. MHI-based mitigation strategies prioritize low-index areas for restoration through hydrological rehabilitation and buffer zone establishment, while healthy areas are primarily focused on conservation and periodic monitoring. This approach supports data-driven conservation planning, restoration prioritization, and sustainable coastal management based on remote sensing and machine learning.