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All Journal MAJALAH ILMIAH GLOBE International Journal of Remote Sensing and Earth Sciences (IJReSES)
Umam, Iddam Hairuly
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Earth & Planetary Sciences Education

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PRELIMINARY STUDY OF A RADIO FREQUENCY INTERFERENCE FILTER FOR NON-POLARIMETRIC C-BAND WEATHER RADAR IN INDONESIA (CASE STUDY: TANGERANG WEATHER RADAR) Ali, Abdullah; Umam, Iddam Hairuly; Leijnse, Hidde; Sa'adah, Umi
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 18, No 2 (2021)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3727

Abstract

C-Band weather radar that operates at a frequency of 5 GHz is very vulnerable to radio frequency interference (RFI) because it is located on a free used frequency. RFI can cause image misinterpretation and precipitation echo distortion. The new allocation for free spectrum recommended by the World Radio Conference 2003 and weather radar frequency protection in Indonesia controlled by the Balai Monitoring Spektrum Frekuensi (BALMON) have not provided permanent protection against weather radar RFI. Several RFI filter methods have been developed for polarimetric radars, but there have been no studies related to RFI filters on non-polarimetric radars in Indonesia. This research aims to conduct an initial study of RFI filters on such radars. Four methods were applied in the initial study. The Himawari 8 cloud mask was used to eliminate interference echo based on VS, IR, and I2 channels, while the nature of false echo interference that does not have a radial velocity value was used as the basis for the application of the Doppler velocity filter. Another characteristic in the form of consistent echo interference up to the maximum range was used as the basis for applying a beam filling analysis filter with reflectivity thresholds of 5 dBZ and 10 dBZ, with beam filling of more than 75%. Finally, supervised learning Random Forest (RF) was also used to identify interference echo based on the characteristics of the sampling results on reflectivity, radial velocity, and spectral width data. The results show that the beam filling analysis method with a threshold of 5 dBZ provides the best RFI filter without eliminating echo precipitation.
PEMODELAN DISTRIBUSI SPASIAL KASUS POSITIF COVID-19 MENGGUNAKAN ALGORITMA GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) Studi Kasus Provinsi DKI Jakarta: (Modeling Spatially Distribution of COVID-19 using Geographically Weighted Regression (GWR) Case Study DKI Jakarta Province) Ali, Abdullah; Umam, Iddam Hairuly; Mannesa, Masita Dwi Mandini; Sa’adah, Umi
Majalah Ilmiah Globe Vol. 24 No. 1 (2022): GLOBE VOL 24 NO 1 TAHUN 2022
Publisher : Badan Informasi Geospasial

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

Abstract

Wabah novel corona virus 2019 (Covid-19) yang pertama kali ditemukan di Wuhan, China menjadi sebuah pandemi global yang berdampak sangat signifikan terhadap seluruh aspek kehidupan. Kasus positif Covid-19 di Indonesia pertama kali terkonfirmasi pada tanggal 2 Maret 2020 di Provinsi DKI Jakarta yang kemudian ditetapkan sebagai episentrum transmisi Covid-19 di Indonesia. Berbagai penelitian dilakukan untuk melakukan upaya mitigasi, mengetahui dampak dan penyebab, hingga investigasi berberapa faktor yang sangat terkait dengan transmisi Covid-19. Distribusi kasus positif Covid-19 sangat terkait dengan lokasi, sehingga salah satu algoritma yang tepat untuk melakukan investigasi adalah model Geographically Weighted Regression (GWR). Model GWR merupakan pengembangan dari model regresi global dimana nilai bobot variabel prediktor dihitung pada setiap lokasi pengamatan. Penelitian ini bertujuan untuk memodelkan distribusi spasial kasus positif Covid-19 menggunakan model GWR dengan fungsi Kernel Adaptif Bisquare. Variabel prediktor yang ditentukan melalui studi literatur adalah tingkat kepadatan penduduk, jumlah fasilitas kesehatan, dan kelompok usia rentan. Pemilihan rentang kelompok usia rentan dilakukan menggunakan algoritma Lasso-Cross Validation (Lasso-CV). Hasil model GWR menunjukkan koefisien determinasi sebesar 0,908 yang artinya 90,8% kasus positif Covid-19 di DKI Jakarta dipengaruhi oleh variabel yang digunakan, sedangkan 9,2% kasus positif Covid-19 dipengaruhi oleh faktor lain di luar penelitian. Wilayah dengan koefisien determinasi tertinggi terdapat pada wilayah Jakarta Utara bagian barat, Jakarta Barat bagian utara, dan Jakarta Timur, sedangkan nilai koefisien determinasi terendah terdapat pada wilayah Jakarta Pusat.
Co-Authors Leijnse, Hidde Mannesa, Masita Dwi Mandini Raden Mohamad Herdian Bhakti Sa'adah, Umi Sa’adah, Umi