Urbanisasi Dan HIV di Kota Bandung (Perspektif Geografi Kesehatan)

Ismail, Arif; Dede, Moh.; Widiawaty, Millary Agung

doi:10.22435/bpk.v48i2.2921

Cited by 15 publications

(12 citation statements)

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“…2). The result is entered in an error matrix, models are suitable if it meets requirements -minimum 80 percent [27]. The validity level of forecasting data from CA, ANN, and ANN-CA based on overall accuracy and overall Kappa [28].…”

Section: Discussionmentioning

confidence: 99%

Spatial dynamics model of land use and land cover changes: A comparison of CA, ANN, and ANN-CA

Dede

Asdak

Setiawan

2021

regist. j. ilm. teknol. sist. inf.

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Land use and land cover (LULC) changes through built-up area expansion always increases linearly with land demand as a consequence of population growth and urbanization. Cirebon City is a center for Ciayumajakuning Region that continues to grow and exceeds its administrative boundaries. This phenomenon has led to peri-urban regions which show urban and rural interactions. This study aims to analyze (1) the dynamics of LULC changes using cellular automata (CA), artificial neural network (ANN), and ANN-CA; (2) the influential factors (drivers); and (3) change probability in the period 2030 and 2045 for Cirebon’s peri-urban. We used logistic regression as quantitative approach to analyze the interaction of drivers and LULC changes. The LULC data derived from Landsat series satellite imagery in 1999-2009 and 2009-2019, validation of dynamic spatial model refers to 100 LULC samples. This research shows that LULC changes are dominated by built-up area expansion which causes plantations and agricultural land to decrease. The drivers have a simultaneous effect on LULC changes with r-square of 0.43, where land slope, distance from existing built-up area, distance from CBD, and accessibility are significant triggers. LULC simulation of CA algorithm is the best model than ANN and ANN-CA based on overall accuracy and overall accuracy (0.96, 0.75, 0.73 and 0.95, 0.66, 0.66 respectively), it reveals urban sprawl through the ribbon and compact development. The average probability of built-up area expansion is 0.18 (2030) and 0.19 (2045). If there is no intervention in spatial planning, this phenomenon will decrease productive agricultural lands in Cirebon's peri-urban.

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Section: Discussionmentioning

confidence: 99%

Spatial dynamics model of land use and land cover changes: A comparison of CA, ANN, and ANN-CA

Dede

Asdak

Setiawan

2021

regist. j. ilm. teknol. sist. inf.

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“…The interactions between the variables can be determined partially through the Pearson correlation test, whilst the level of significance refers to the r-value, and p -value [ 54 ]. The spatial distribution of each variable is revealed through Moran’s I spatial autocorrelation [ 55 ] (Eq. 3).…”

Section: Methodsmentioning

confidence: 99%

Spatial differentiation and determinants of COVID-19 in Indonesia

2022

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Background The spread of the coronavirus disease 2019 (COVID-19) has increasingly agonized daily lives worldwide. As an archipelagic country, Indonesia has various physical and social environments, which implies that each region has a different response to the pandemic. This study aims to analyze the spatial differentiation of COVID-19 in Indonesia and its interactions with socioenvironmental factors. Methods The socioenvironmental factors include seven variables, namely, the internet development index, literacy index, average temperature, urban index, poverty rate, population density (PD) and commuter worker (CW) rate. The multiple linear regression (MLR) and geographically weighted regression (GWR) models are used to analyze the impact of the socioenvironmental factors on COVID-19 cases. COVID-19 data is obtained from the Indonesian Ministry of Health until November 30th 2020. Results Results show that the COVID-19 cases in Indonesia are concentrated in Java, which is a densely populated area with high urbanization and industrialization. The other provinces with numerous confirmed COVID-19 cases include South Sulawesi, Bali, and North Sumatra. This study shows that the socioenvironmental factors, simultaneously, influence the increasing of confirmed COVID-19 cases in the 34 provinces of Indonesia. Spatial interactions between the variables in the GWR model are relatively better than those between the variables in the MLR model. The highest spatial tendency is observed outside Java, such as in East Nusa Tenggara, West Nusa Tenggara, and Bali. Conclusion Priority for mitigation and outbreak management should be high in areas with high PD, urbanized spaces, and CW.

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“…Prediksi suhu permukaan juga menggunakan sejumlah variabel spasial lain yang berperan sebagai driving force yakni kepadatan bangunan, kerapatan vegetasi, dan kepadatan jaringan jalan. Kepadatan bangunan diperoleh berdasarkan algoritma Normalized Difference Building Index (NDBI), sedangkan kerapatan vegetasi berasal dari algoritma Normalized Difference Vegetation Index (NDVI) dengan data pembanding yang berasal dari citra satelit Sentinel-2 MSI (Govaerts & Verhulst, 2010;Fariz & Trida, 2017;Ismail et al, 2020). Keduanya menghasilkan informasi spasial ketika data citra satelit dimasukkan algoritma sebagaimana Persamaan 4 dan Persamaan 5.…”

Section: Metodeunclassified

Prediksi Suhu Permukaan Menggunakan Artificial Neural Network-Cellular Automata Di Wilayah Cirebon Dan Sekitarnya

Dede

Widiawaty

Ramadhan

et al. 2021

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Ekspansi lahan terbangun melalui kehadiran pemukiman dan kawasan industri berimplikasi pada berkurang lahan bervegetasi di wilayah Cirebon dan sekitarnya. Kondisi menyebabkan peningkatan suhu yang berpotensi memunculkan urban heat island. Perubahan suhu dapat diprediksi menggunakan pemodelan spasial dinamis sebagai bagian dari proses perubahan lansekap. Penelitian ini bertujuan untuk memprediksi dinamika suhu permukaan di wilayah Cirebon dan sekitarnya menggunakan algoritma Artificial Neural Network-Cellular Automata (ANN-CA) dengan melibatkan variabel spasial seperti kepadatan bangunan, kerapatan vegetasi, dan kepadatan jaringan jalan. Sumber data yang digunakan dalam penelitian ini berasal dari citra Landsat-5 TM dan Landsat-8 OLI pada tahun 1999, 2009, serta 2019, sedangkan data jaringan jalan berasal dari OpenStreetMaps. Suhu permukaan diperoleh dari kanal termal yang diolah menggunakan Radiative Transfer Equation. Sementara variabel lainnya diperoleh dari Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) dan line density. Penelitian ini menunjukkan suhu permukaan hasil pemodelan ANN-CA pada tahun 2019 memiliki rerata sebesar 22,61 °C. Model ini memiliki overall accuracy 0,63 dan overall kappa sebesar 0,52. Bila dibandingkan dengan nilai aktual, model ini memiliki nilai r-square mencapai 0,80 dengan selisih sebesar 0,54 °C yang layak untuk prediksi suhu permukaan di masa mendatang. Model ANN-CA menunjukkan sebaran suhu permukaan yang lebih tinggi berpusat pada wilayah kota dan peri-urban. Kajian terkait prediksi suhu permukaan diharapkan dapat menjadi perhatian utama dalam mewujudan resiliensi perkotaan.

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Urbanisasi Dan HIV di Kota Bandung (Perspektif Geografi Kesehatan)

Cited by 15 publications

References 0 publications

Spatial dynamics model of land use and land cover changes: A comparison of CA, ANN, and ANN-CA

Spatial dynamics model of land use and land cover changes: A comparison of CA, ANN, and ANN-CA

Spatial differentiation and determinants of COVID-19 in Indonesia

Prediksi Suhu Permukaan Menggunakan Artificial Neural Network-Cellular Automata Di Wilayah Cirebon Dan Sekitarnya

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