4 results on '"Liadira Kusuma Widya"'
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2. Comparison of Satellite Imagery for Identifying Seagrass Distribution Using a Machine Learning Algorithm on the Eastern Coast of South Korea
- Author
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Liadira Kusuma Widya, Chang-Hwan Kim, Jong-Dae Do, Sung-Jae Park, Bong-Chan Kim, and Chang-Wook Lee
- Subjects
seagrass ,remote sensing ,support vector machines (SVM) ,classification models ,Naval architecture. Shipbuilding. Marine engineering ,VM1-989 ,Oceanography ,GC1-1581 - Abstract
Seagrass is an essential component of coastal ecosystems because of its capability to absorb blue carbon, and its involvement in sustaining marine biodiversity. In this study, support vector machine (SVM) technologies with corrected satellite imagery data, were applied to identify the distribution of seagrasses. Observations of seagrasses from satellite imagery were obtained using GeoEye-1, Sentinel-2 MSI level 1C, and Landsat-8 OLI satellite imagery. The satellite imagery from Google Earth has been obtained at a very high resolution, and was to be used within both the training and testing of a classification method. The optical satellite imagery must be processed for image classification, throughout which radiometric correction, sunglint, and water column adjustments were applied. We restricted the scope of the study area to a maximum depth of 10 m due to the fact that light does not penetrate beyond this level. When classifying the distribution of seagrasses present in the research region, the recently developed SVM technique achieved overall accuracy values of up to 92% (GeoEye-1), 88% (Sentinel-2 MSI level 1C), and 83% (Landsat-8 OLI), respectively. The results of the overall accuracy values are also used to evaluate classification models.
- Published
- 2023
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3. PERBANDINGAN STUDI UJI KUAT TEKAN BATAKO MANUAL DENGAN BAHAN TAMBAHAN LIMBAH KERAK PENGOLAHAN MINYAK TANAH
- Author
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Umayya Ulfah, Liadira Kusuma Widya, and Nunuk Candra Stiyanta
- Subjects
General Medicine - Abstract
Bahan bangunan batako semakin hari semakin mahal harganya maka perlu dilakukan satu langkah inovatif yang bisa menekan harga pembuatan bahan bangunan tersebut. Dengan pemanfaatan bahan bangunan yang lebih murah harganya, salah satunya yang dapat digunakan adalah kerak yaitu limbah dari pengolahan minyak tanah sebagai bahan tambahan pembuatan batako.Penelitian ini bertujuan untuk dapat mengetahui perbandingan uji kuat tekan batako manual dengan bahan tambah kerak limbah pengolahan minyak tanah. Penelitian ini dilakukan dengan metode eksperimental. Pada penelitian ini digunakan rancangan pebandingan campuran 1 pc : 6 ps (sebagai bahan kontrol), 1 Pc : 5,5 Ps : 0,5 Kr, 1 Pc : 5 Ps : 1 Kr, 1 Pc : 4,5 Ps : 1,5 Kr, 1 Pc : 4 Ps : 2 Kr dengan volume pasir lebih sedikit (kelompok eksperimen). Pemeriksaan benda uji terhadap kuat tekan dilakukan pada umur 7, 14, 21, dan 28 hari. Dimensi benda uji untuk pengujian kuat tekan 30 cm x 10 cm x 15 cm. Hasil kuat tekan yang terjadi pada masing-masing variasi masih memenuhi stndar yang ditetapkan oleh tabel mutu bata SNI-03-0348-1989, batako tipe konvensional atau batako tanpa penambahan kerak pada umur 7, 14, 21, dan 28 hari menunjukan nilai rata-rata 38,2 kg/cm2 โ 47,5 kg/cm2, batako tipe konvensional ini termasuk pada tingkat mutu III. Batako tipe A dengan penambahan kerak 0,5 takaran mendapatkan hasil rata-rata 69,9 kg/cm2 - 89,2 kg/cm2, batako tipe C 64,5 kg/cm2 - 86,5 kg/cm2 , batako tipe D 60,8 kg/cm2 โ 79,1 kg/cm2 , dengan rata-rata tersebut batako tipe A, tipe C,dan tipe D termasuk batako dengan tingkat mutu II. Sedangkan batako dengan tingkat mutu I terlihat pada batako tipe B dengan rata-rata 100,0 kg/cm2 -140,9 kg/cm2. Berdasarkan hasil penelitian dan pembahasan yang telah diuraikan sebelumnya, maka dapat diambil kesimpulan sebagai berikut. Kuat tekan batako dengan penambahan kerak lebih kuat dari pada batako konvensional. Batako dengan penambahan kerak lebih kuat dengan komposisi perbandingan 1 portland cement : 5 pasir : 1 kerak.
- Published
- 2022
4. Comparison of Spatial Modelling Approaches on PM10 and NO2 Concentration Variations: A Case Study in Surabaya City, Indonesia
- Author
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Liadira Kusuma Widya, Shih-Chun Candice Lung, Chin Yu Hsu, Lalu Muhamad Jaelani, Hsiao Yun Lee, Chih Da Wu, and Huey Jen Su
- Subjects
Asia ,010504 meteorology & atmospheric sciences ,Health, Toxicology and Mutagenesis ,media_common.quotation_subject ,Nitrogen Dioxide ,Air pollution ,lcsh:Medicine ,nitrogen dioxide (NO2) ,Land cover ,particulate matter (PM10) ,010501 environmental sciences ,medicine.disease_cause ,01 natural sciences ,Article ,Normalized Difference Vegetation Index ,Air Pollution ,Urbanization ,Statistics ,medicine ,geographically weighted regression (GWR) ,Cities ,0105 earth and related environmental sciences ,media_common ,Air Pollutants ,Variables ,lcsh:R ,Public Health, Environmental and Occupational Health ,land-use regression (LUR) ,Models, Theoretical ,Regression ,Indonesia ,Environmental science ,Particulate Matter ,geographic and temporal weighted regression (GTWR) ,Unit-weighted regression ,Predictive modelling ,Environmental Monitoring - Abstract
Because of fast-paced industrialization, urbanization, and population growth in Indonesia, there are serious health issues in the country resulting from air pollution. This study uses geospatial modelling technologies, namely land-use regression (LUR), geographically weighted regression (GWR), and geographic and temporal weighted regression (GTWR) models, to assess variations in particulate matter (PM10) and nitrogen dioxide (NO2) concentrations in Surabaya City, Indonesia. This is the first study to implement spatiotemporal variability of air pollution concentrations in Surabaya City, Indonesia. To develop the prediction models, air pollution data collected from seven monitoring stations from 2010 to 2018 were used as dependent variables, while land-use/land cover allocations within a 250 m to 5000 m circular buffer range surrounding the monitoring stations were collected as independent variables. A supervised stepwise variable selection procedure was applied to identify the important predictor variables for developing the LUR, GWR, and GTWR models. The developed models of LUR, GWR, and GTWR accounted for 49%, 50%, and 51% of PM10 variations and 46%, 47%, and 48% of NO2 variations, respectively. The GTWR model performed better (R2 = 0.51 for PM10 and 0.48 for NO2) than the other two models (R2 = 0.49&ndash, 0.50 for PM10 and 0.46&ndash, 0.47 for NO2), LUR and GWR. In the PM10 model four predictor variables, public facility, industry and warehousing, paddy field, and normalized difference vegetation index (NDVI), were selected during the variable selection procedure. Meanwhile, paddy field, residential area, rainfall, and temperature played important roles in explaining NO2 variations. Because of biomass burning issues in South Asia, the paddy field, which has a positive correlation with PM10 and NO2, was selected as a predictor. By using long-term monitoring data to establish prediction models, this model may better depict PM10 and NO2 concentration variations within areas across Asia.
- Published
- 2020
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