Research Article
Evaluation of Knowledge, Attitude, Motivation, and Behavior of Low-Income Community Towward Making Environmentally Safe Toilets in Lowland Areas of South Sulawesi Province
Bakhrani Abdul Rauf,
Faizal Amir,
St. Fatmah Hiola,
Mithen Lullulangi*
,
Haruna,
Rahmansah
Issue:
Volume 9, Issue 4, December 2025
Pages:
157-166
Received:
24 August 2025
Accepted:
11 September 2025
Published:
30 September 2025
Abstract: Safe and proper sanitation is one of the important aspects in efforts to improve the quality of public health. However, in the lowland areas of South Sulawesi Province, low-income communities still face challenges in building and using environmentally safe toilets. The objectives of this study were to: (1) assess public knowledge in building environmentally friendly toilets, (2) assess public attitudes towards building healthy toilets, (3) assess the level of public motivation, and (4) assess public behavior in building environmentally safe toilets in the lowland areas of South Sulawesi Province. This study was conducted in several lowland areas involving 300 heads of families from low-income communities selected using the purposive sampling method. The variables studied included public knowledge, attitudes, motivation, and behavior towards building healthy toilets. Data analysis was conducted using descriptive statistics. The results of the study showed that: (1) the level of knowledge of low-income communities in building environmentally safe toilets was in the high category, (2) public attitudes towards the importance of healthy toilets were also in the high category, (3) public motivation to build healthy toilets was high, and (4) real community behavior in building environmentally safe toilets also showed a high category. These findings indicate that although economic limitations are a challenge, community awareness and commitment to maintaining environmental sanitation are quite good.
Abstract: Safe and proper sanitation is one of the important aspects in efforts to improve the quality of public health. However, in the lowland areas of South Sulawesi Province, low-income communities still face challenges in building and using environmentally safe toilets. The objectives of this study were to: (1) assess public knowledge in building enviro...
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Research Article
A Multi-Temporal Land Cover Analysis of Kathmandu Using Google Earth Engine and ENVI: A Comparative Study of SVM and RF Algorithms
Issue:
Volume 9, Issue 4, December 2025
Pages:
167-182
Received:
22 September 2025
Accepted:
5 October 2025
Published:
28 October 2025
DOI:
10.11648/j.ajese.20250904.12
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Abstract: Rapid urbanization and land cover change have emerged as major environmental concerns in developing regions, particularly within the Kathmandu District of Nepal. This study aims to analyze multi-temporal land cover changes and compare the performance of two machine learning algorithms—Support Vector Machine (SVM) and Random Forest (RF)—across two platforms: Google Earth Engine (GEE) and ENVI. Sentinel-2 satellite imagery from 2017, 2020, and 2023 was utilized to classify four major land cover classes (water, bareland, built-up, and vegetation) using supervised classification techniques. Preprocessing included cloud masking, filtering, and subsetting, while training samples were generated from high-resolution Google Earth Pro and Copernicus Sentinel imagery. Accuracy was assessed using user’s accuracy, producer’s accuracy, overall accuracy, and the Kappa coefficient derived from confusion matrices. Results indicate a steady increase in built-up areas (from 24.75% in 2017 to 37.06% in 2023) and bareland, alongside a marked decline in vegetation. The RF algorithm in GEE achieved the highest performance with an overall accuracy of 98.43% and Kappa coefficient of 0.9773 in 2023, demonstrating strong stability across all years. SVM, while slightly less consistent, achieved 97.6% user accuracy and 98.8% producer accuracy for the water class in 2023, outperforming RF in that category. ENVI-based SVM models attained an overall accuracy of 91.96% and Kappa coefficient of 0.8862, performing well for vegetation but showing slightly lower robustness than RF in GEE. In conclusion, the integration of cloud-based (GEE) and desktop (ENVI) remote sensing platforms with machine learning algorithms proved highly effective for large-scale urban monitoring. The findings highlight rapid urban expansion and vegetation loss in Kathmandu and offer valuable insights for sustainable urban planning and environmental management.
Abstract: Rapid urbanization and land cover change have emerged as major environmental concerns in developing regions, particularly within the Kathmandu District of Nepal. This study aims to analyze multi-temporal land cover changes and compare the performance of two machine learning algorithms—Support Vector Machine (SVM) and Random Forest (RF)—across two p...
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