Sa’dianoor Sa’dianoor
Universitas Muhammadiyah Banjarmasin

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Kebijakan pengelolaan permukiman rumah lanting berbasis geospasial dan kearifan lokal di Kampung Awang Landas, Desa Sungai Buluh Muhammad Pajaruddin; Nurul Azkar; Sa’dianoor Sa’dianoor
Jurnal Praksis dan Dedikasi Sosial Vol. 9 No. 2 (2026)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um022v9i22026p110-122

Abstract

The management of floating-house settlements in Kampung Awang Landas, Sungai Buluh Village, is a strategic policy issue as it relates to housing safety, livelihood sustainability, and the preservation of river-based cultural identity. This study aims to analyse the social, economic, physical, and governance dimensions of floating-house settlements and to formulate a more contextual and sustainable management policy model grounded in geospatial analysis and local wisdom. A mixed-methods approach with a sequential exploratory design was employed, beginning with a qualitative phase using in-depth interviews, field observations, and document analysis, followed by quantitative and spatial phases comprising household surveys, drone mapping, satellite imagery analysis, and expert judgement through the Analytic Hierarchy Process (AHP). The findings indicate that floating houses function as adaptive dwellings that support river-based livelihoods, yet their sustainability is undermined by multiple vulnerabilities, including limited access to basic services, weaknesses in buoyancy and mooring systems, and weak policy recognition that results in fragmented government interventions. The proposed policy model highlights the formal recognition of floating houses within the regional settlement system, incremental in‑situ improvement of housing quality, systematic use of geospatial data in planning, and active involvement of communities and local wisdom in all stages of settlement management. Pengelolaan permukiman rumah lanting di Kampung Awang Landas, Desa Sungai Buluh, merupakan isu strategis karena terkait keselamatan hunian, keberlanjutan penghidupan, dan pelestarian identitas budaya masyarakat sungai. Penelitian ini bertujuan menganalisis kondisi sosial, ekonomi, fisik, dan tata kelola permukiman rumah lanting serta merumuskan model kebijakan pengelolaan yang lebih kontekstual dan berkelanjutan berbasis geospasial dan kearifan lokal. Metode yang digunakan adalah mixed methods dengan desain sequential exploratory, diawali eksplorasi kualitatif melalui wawancara mendalam, observasi, dan analisis dokumen, lalu dilanjutkan tahap kuantitatif dan spasial melalui survei rumah tangga, pemetaan drone, analisis citra satelit, dan penilaian pakar menggunakan Analytic Hierarchy Process (AHP). Hasil penelitian menunjukkan bahwa rumah lanting merupakan hunian adaptif yang menopang livelihood berbasis sungai, tetapi menghadapi kerentanan berlapis berupa keterbatasan layanan dasar, kelemahan pelampung dan sistem tambat, serta lemahnya pengakuan kebijakan sehingga intervensi pemerintah masih parsial. Model kebijakan yang direkomendasikan menekankan pengakuan formal rumah lanting sebagai bagian sistem permukiman daerah, peningkatan kualitas hunian secara in‑situ, pemanfaatan data geospasial dalam perencanaan, dan pelibatan aktif komunitas serta kearifan lokal dalam setiap tahapan pengelolaan.
PHYSICAL TRANSFORMATION OF FLOATING HOUSES AND AHP-BASED POLICY PRIORITIES FOR WATER SETTLEMENT MANAGEMENT IN KAMPUNG AWANG LANDAS Muhammad Pajaruddin; Nurul Azkar; Sa’dianoor Sa’dianoor; Syamsul Maarif; Muhammad Noor
Scientechno: Journal of Science and Technology Vol. 5 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v5i4.4209

Abstract

Floating-house settlements (rumah lanting) in Kampung Awang Landas remain an adaptive form of water-based housing but face physical deterioration, limited basic services, and weak policy recognition. This study examined the physical transformation of rumah lanting and identified management priorities using a sequential exploratory mixed-methods design. Qualitative exploration involved 15 informants, comprising 12 floating-house residents, two government officials, and one community elder. The subsequent assessment covered all 18 floating-house units and used Analytic Hierarchy Process (AHP) judgments from three purposively selected experts. Qualitative and observational findings were used to formulate four AHP criteria and 16 subcriteria. The results showed that 50.0% of units were in moderate condition and 27.8% were in poor condition, with the most extensive material transformation occurring in roofs and walls. AHP synthesis ranked Institutional and Government Policy as the highest-priority criterion (0.378), followed by Technical-Infrastructure (0.293), Economic-Livelihood (0.206), and Socio-Cultural and Local Wisdom (0.123). The highest-ranked subcriteria were Policy Direction (0.146), Floating-House Legality and Land Tenure Status (0.112), and Basic Utilities (0.098). The study proposes the PPAI framework, comprising Policy, Physical, Access, and Institutional dimensions, as an evidence-informed planning framework for context-sensitive floating-house settlement management in Kampung Awang Landas.
DEEP LEARNING ARCHITECTURES FOR AUTOMATED CLASSIFICATION OF SATELLITE IMAGERY TO MONITOR GLOBAL DEFORESTATION TRENDS AND LAND USE CHANGES Sa’dianoor Sa’dianoor; Chen Mei; Yang Xiang
Research of Scientia Naturalis Vol. 3 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i4.4327

Abstract

Global deforestation and land-use change require timely, accurate, and transferable monitoring systems capable of processing extensive satellite archives. This study aimed to evaluate deep learning architectures for automated classification of satellite imagery, detection of forest change, and identification of post-deforestation land uses across diverse ecological regions. A comparative experimental design was applied to 120,000 labelled image patches derived from Landsat, Sentinel-1, and Sentinel-2 data representing South America, Southeast Asia, Central Africa, North America, Europe, and Oceania. Nine architectures, including convolutional, recurrent, Siamese, Transformer-based, and hybrid models, were assessed using spatial, temporal, and cross-regional validation. The hybrid CNN–Transformer achieved the strongest overall accuracy, calibration, and transfer performance, while radar–optical fusion improved classification in persistently cloudy tropical environments. Multitemporal models reduced confusion between permanent deforestation, seasonal disturbance, harvesting, and regeneration. Geographic holdout testing produced substantial performance declines across all architectures, demonstrating that random partitioning overestimated operational reliability. The study concludes that effective global deforestation monitoring requires multimodal data, temporal context, uncertainty estimation, active learning, and geographically independent evaluation. Automated classification can strengthen forest surveillance, yet expert verification and statistically adjusted area estimation remain essential for responsible environmental decision-making and policy implementation under rapidly changing climatic, technological, and land-management conditions worldwide over time.