Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization Jurnal Sylva Lestari
Boby Bagja Pratama
Alumni Fakultas Perikanan dan Ilmu Kelautan Unpad
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Energy Environmental Science Materials Science & Nanotechnology

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimizing Mangrove Classification with Data Fusion: Machine Learning Approaches for Enggano Island, Indonesia Pratama, Boby Bagja; Pratama, Wahyunda; Rudiastuti, Aninda Wisaksanti; Sugara, Ayub; Nugroho, Feri
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3360

Abstract

Mangrove ecosystems are crucial in coastal protection, carbon sequestration, and biodiversity. Accurate mapping is vital for the conservation and sustainable management of these species, especially in vulnerable areas like Enggano Island, Indonesia. This study evaluates the performance of machine learning algorithms in GEE to model mangrove distribution on Enggano Island, Indonesia, using multi-source data, including optical data (Sentinel-2), radar data (Sentinel-1), and elevation data filtering (FABDEM). Three input configurations were developed to explore the best combination of data: (1) visual and infrared bands from Sentinel-2, (2) Sentinel-2 band ratios and spectral indices, and (3) a fusion of Sentinel-2 optical data with Sentinel-1 SAR data. Several machine-learning algorithms, including Random Forest (RF), Classification and Regression Trees (CART), Minimum Distance (MD), Gradient Tree Boost (GTB), K-Nearest Neighbor (KNN), and Support Vector Machines (SVM), were assessed using accuracy, precision, recall, and F1 score. Results showed that the third configuration, which combined Sentinel-2 optical bands, band ratios, and Sentinel-1 radar polarimetric, provided the best performance with the highest overall accuracy (OA 95.19%) using the Random Forest algorithm. This approach demonstrated superiority in overcoming mangrove classification challenges, such as cloud cover, seasonal variability, and spectral similarity with non-mangrove vegetation. These results support the importance of mangrove monitoring in small islands and tropical regions, contributing to ecosystem conservation and coastal disaster mitigation.
Disempowered on Household Plots: A Study on Gendered Division of Labor in Small-Scale Agroforestry Practices in Lamala Sub-District, Banggai Regency, Indonesia Lawasi, Moh Andika; Septina, Ane Dwi; Yusnikusumah, Tri Rizkiana; Pratama, Boby Bagja; Pratiwi, Dian; Humaida, Nida; Suli, Andreas Aprilano Thomas
Jurnal Sylva Lestari Vol. 14 No. 2 (2026): May
Publisher : Department of Forestry, Faculty of Agriculture, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jsl.v14i2.1411

Abstract

Understanding gender aspects in the management of small-scale agroforestry is important to creating an inclusive and sustainable model in subsistence agriculture. This research aimed to analyze the actualization of the gender dimension in productive, reproductive, and social-political activities in small-scale agroforestry management among subsistence farmer groups in Lamala Sub-District, Banggai Regency, Indonesia. The study employed a mixed-methods case study approach using the Harvard Analytical Framework (HAF) to analyze the gender-based division of labor, integrating observations, interviews with 5 key informants, and a questionnaire administered to 50 respondents. The data collected were then analyzed thematically and descriptively. The results showed a gender imbalance in the management of small-scale agroforestry. HAF mapping indicated that women were involved in only 52.63% of the identified productive activities, whereas men were involved in all productive activities. In contrast, women carried out all identified reproductive activities, while men were involved in only 33.33% of them and only occasionally. In socio-political activities, women were involved in 66.67% of the identified activities, again only occasionally, whereas men were involved in all activities and dominated 66.67% of them. These findings suggest that small-scale agroforestry is not gender-neutral, as its productive, reproductive, and socio-political activities are structured through unequal gender relations. This research recommends education for small-scale farmers on gender and productivity, helping the community understand gender equality in efforts to improve access, productivity, and outcomes through equitable, egalitarian role distribution. Keywords: agroforestry, community empowerment, gender equality, Harvard Analytical Framework, subsistent farmer
Co-Authors Ane Dwi Septina Ayub Sugara Dian Pratiwi Feri Nugroho Humaida, Nida Lawasi, Moh Andika Pratama, Wahyunda Rudiastuti, Aninda Wisaksanti Suli, Andreas Aprilano Thomas Yusnikusumah, Tri Rizkiana