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ANALYSIS OF CLASSIFICATION METHODS FOR MAPPING SHALLOW WATER HABITATS USING SPOT-7 SATELLITE IMAGERY IN NUSA LEMBONGAN ISLAND, BALI Kuncoro Teguh Setiawan; Gathot Winarso; Andi Ibrahim; Anang Dwi Purwanto; I Made Parsa
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 19, No.1 (2022)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3748

Shallow water habitat maps are crucial for the sustainable management purposes of marine resources. The use of a better digital classification method can provide shallow water habitat maps with the best accuracy rate that is able to indicate actual conditions. Experts use the object-based classification method as an alternative to the pixel-based method. However, the pixel-based classification method continues to be relied upon by experts in obtaining benthic habitat conditions in shallow water. This study aims to analyze the classification results and examine the accuracy rate of shallow-water habitats distribution using SPOT-7 satellite imagery in Nusa Lembongan Island, Bali. Water column correction by Lyzenga 2006 was opted, while object-based and pixel-based classification was used in this study. The benthic habitat classification scheme uses four classes: substrate, seagrass, macroalgae, and coral. The results show different accuracy is obtained between pixel-based classification with maximum likelihood models and object-based classification with decision tree models. Mapping benthic habitats in Nusa Lembongan, Bali, with object-based classification and decision tree models, has higher accuracy than the other with 68%.

Pengaruh Loan To Deposit Ratio, Capital Adequacy Ratio Dan Debt To Equity Ratio Terhadap Return On Assets Bank Swasta Pada PT. Bank Central Asia, Tbk Periode 2014-2023 Andi Ibrahim; Charles Mantiri, Sandro; Syafrinah Wulandari; Rudi Sanjaya
TEKNOBIS : Jurnal Teknologi, Bisnis dan Pendidikan Vol. 2 No. 3 (2024): TEKNOBIS : Teknologi, Bisnis Dan Pendidikan
Publisher : Shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstrak−Tujuan dari penelitian ini adalah untuk mengetahui Pengaruh Loan To Deposit Ratio, CapitalAdequacy Ratio dan Debt To Equity Ratio Terhadap Return On Assets Bank Swasta Pada PT Bank CentralAsia Tbk Periode 2014-2023. Metode yang digunakan dalam penelitian ini adalah metode kuantitatif denganmengambil data laporan keuangan pada PT Bank Central Asia Tbk. Analisis data yang digunakan adalahanalisis data statistik deskriptif, analisis regresi berganda, uji asumsi klasik dan uji hipotesis. Hasil daripengujiannya ditemukan bahwa variabel Loan To Deposit Ratio berpengaruh signifikan terhadap Return OnAssets, variabel Capital Adequacy Ratio tidak berpengaruh signifikan terhadap Return On Assets, variabel DebtTo Equity Ratio tidak berpengaruh signifikan terhadap Return On Assets, dan ketiga variabel LDR,CAR danDER secara simultan memiliki pengaruh terhadap Return On Assets.Kata Kunci: Loan To Deposit Ratio, Capital Adequacy Ratio, Debt To Equity Ratio, Return On Assets. Abstract−The aim of this research is to determine the influence of the Loan To Deposit Ratio, CapitalAdequacy Ratio and Debt To Equity Ratio on Return On Assets of PT Bank Central Asia Tbk for the 2014-2023period. The method used in this research is a quantitative method by taking financial report data at PT BankCentral Asia Tbk. The data analysis used is descriptive statistical data analysis, multiple regression analysis,classical assumption testing and hypothesis testing. The results of the test found that the Loan To Deposit Ratiovariable had a significant effect on Return On Assets, the Capital Adequacy Ratio variable had no significanteffect on Return On Assets, the Debt To Equity Ratio variable had no significant effect on Return On Assets,and the three variables LDR, CAR and DER simultaneously has an influence on Return On Assets.Keywords: Loan To Deposit Ratio, Capital Adequacy Ratio, Debt To Equity Ratio, Return On Assets.

Determination of Sentinel-2 spectral reflectance to detect oil spill on the sea surface Pingkan Mayestika Afgatiani; Fanny Aditya Putri; Argo Galih Suhadha; Andi Ibrahim
Sustinere: Journal of Environment and Sustainability Vol. 4 No. 3 (2020): pp. 144-223 (December 2020)
Publisher : Center for Science and Technology, IAIN Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22515/sustinere.jes.v4i3.115

Oil spill is one of the most common marine environmental problems. Oil spills can be caused by leakage at oil refineries at sea or disposal of vessel waste. This event has an impact on various sectors, such as fisheries, tourism, and marine ecosystems. This study aims to determine the spectral reflectance of Sentinel-2 response to detecting oil spill on the sea. Oil identification in the sea can be made visually by looking at colored patterns at sea level. Sentinel-2 image reflectance was obtained by processing the image using the Google Earth Engine platform. The results were clipped according to the area of â€‹â€‹interest and divided to get a value between 0 and 1. Bands combination is possible to identify the oil spill visually. The silvery pattern saw in the red-green-blue combination, but it is arduous to estimate its distribution because of the silvery pattern seen for thick oil. The combination of SWIR-NIR-red bands proved effective in showing the distribution of oil with a deep black pattern. Spectral measurements in the field were undertaken by taking samples in the areas of oil spills and clean water bodies. The oil layer had a lower reflectance than the clean water body. The blue band gave a high response, but the red band gave less response. In the NIR and SWIR bands, the reflectance of oil was lower than the water body. In conclusion, the SWIR - NIR - RED band combination is better used to determine oil spills due to it shows the characteristics of oil generally, either thin or thick oil.

DETECTION OF WATER-BODY BOUNDARIES FROM SENTINEL-2 IMAGERY FOR FLOODPLAIN LAKES Azura Ulfa; Fajar Bahari Kusuma; A. A. Md. Ananda Putra Suardana; Wikanti Asriningrum; Andi Ibrahim; Lintang Nur Fadlillah
International Journal of Remote Sensing and Earth Sciences Vol. 19 No. 2 (2022)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3827

The impact of climate and human interaction has resulted in environmental degradation. Consistent observations of lakes in Indonesia are quite limited, especially for flood-exposure lake types. Satellite imagery data improves the ability to monitor water bodies of different scales and the efficiency of generating lake boundary information. This research aims to detect the boundaries of flood-exposure type lake water bodies from the detection model and calculate its accuracy in Semayang Melintang Lake using Sentinel-2 imagery data. The characteristics of water, soil, and vegetation objects were investigated based on the spectral values of the composite image bands from the Optimum Index Factor (OIF) calculation, to support the lake water body boundary detection model. The Object-Based Image Analysis (OBIA) method is used for water and non-water classification, by applying the machine learning algorithms random forest (RF), support vector machine (SVM), and decision tree (DT). Model validation was conducted by comparing spectral graphs and lake water body boundary model results. The accuracy test used the confusion matrix method and resulted in the highest accuracy value in the SVM algorithm with an Overall Accuracy of 95% and a kappa coefficient of 0.9. Based on the detection model, the area of Lake Semayang Melintang in 2021 is 23392.30 ha. This model can be used to estimate changes in the area of the flood-exposure lake consistently. Information on the boundaries of lake water bodies is needed to control the decline in the capacity and inundation area of flood-exposure lakes for management and monitoring plans.

EFFECT OF ATMOSPHERIC CORRECTION ALGORITHM ON LANDSAT-8 AND SENTINEL-2 CLASSIFICATION ACCURACY IN PADDY FIELD AREA Fadila Muchsin; Kuncoro Adi Pradono; Indah Prasasti; Dianovita; Kurnia Ulfa; Kiki Winda Veronica; Dandy Aditya Novresiandi; Andi Ibrahim
International Journal of Remote Sensing and Earth Sciences Vol. 20 No. 1 (2023)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3845

Landsat-8 and Sentinel-2 satellite imageries are widely used for various remote sensing applications because they are easy to access and free to download. A precise atmospheric correction is necessary to be applied to the optical satellite imageries so that the derived information becomes more accurate and reliable. In this study, the performance of atmospheric correction algorithms (i.e., 6S, FLAASH, DOS, LaSRC, and Sen2Cor) was evaluated by comparing the object's spectral response, vegetation index, and classification accuracy in the paddy field area before and after the implementation of atmospheric correction. Overall, the results show that each algorithm has varying accuracy. Nevertheless, all atmospheric correction algorithms can improve the classification accuracy, whereby those derived by the 6S and FLAASH yielded the highest accuracy.

ANALYSIS OF CLASSIFICATION METHODS FOR MAPPING SHALLOW WATER HABITATS USING SPOT-7 SATELLITE IMAGERY IN NUSA LEMBONGAN ISLAND, BALI Kuncoro Teguh Setiawan; Gathot Winarso; Andi Ibrahim; Anang Dwi Purwanto; I Made Parsa
International Journal of Remote Sensing and Earth Sciences Vol. 19 No. 1 (2022)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3748

Shallow water habitat maps are crucial for the sustainable management purposes of marine resources. The use of a better digital classification method can provide shallow water habitat maps with the best accuracy rate that is able to indicate actual conditions. Experts use the object-based classification method as an alternative to the pixel-based method. However, the pixel-based classification method continues to be relied upon by experts in obtaining benthic habitat conditions in shallow water. This study aims to analyze the classification results and examine the accuracy rate of shallow-water habitats distribution using SPOT-7 satellite imagery in Nusa Lembongan Island, Bali. Water column correction by Lyzenga 2006 was opted, while object-based and pixel-based classification was used in this study. The benthic habitat classification scheme uses four classes: substrate, seagrass, macroalgae, and coral. The results show different accuracy is obtained between pixel-based classification with maximum likelihood models and object-based classification with decision tree models. Mapping benthic habitats in Nusa Lembongan, Bali, with object-based classification and decision tree models, has higher accuracy than the other with 68%.

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