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Arifianto, Fendy
State College Of Meteorology Climatology And Geophysics (STMKG)
Agriculture, Biological Sciences & Forestry Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Materials Science & Nanotechnology Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary

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Pendugaan PM2.5 Menggunakan Metode Geographically Temporally Weighted Regression di DKI Jakarta Ilil Firrizqi Nur Ilahi; Ervan Ferdiansyah; Fendy Arifianto
Jurnal Ilmu Lingkungan Vol 22, No 6 (2024): November 2024
Publisher : School of Postgraduate Studies, Diponegoro Univer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jil.22.6.1435-1440

Abstract

Pencemaran udara telah menjadi suatu permasalahan lingkungan serius yang sering dihadapi oleh kota-kota besar termasuk DKI Jakarta. Salah satu partikel pencemar udara yang diyakini berbahaya dan memiliki dampak serius pada gangguan pernapasan manusia karena ukurannya yang sangat kecil adalah PM2.5. Beberapa penelitian telah mengambil kesimpulan bahwa parameter meteorologi memiliki peran penting dalam penyebaran, peningkatan dan pengurangan konsentrasi PM2.5. Namun, konsentrasi PM2.5 akan berbeda bergantung pada topografi dan kondisi suatu wilayah. Sehingga, dalam melakukan pendugaannya dibutuhkan metode yang dapat memperhitungkan keragaman data secara spasial temporal dan menghasilkan nilai dugaan yang bersifat lokal yaitu metode Geographically Temporally Weighted Regression. Penelitian ini bertujuan untuk mengetahui korelasi atau pengaruh parameter meteorologi terhadap PM2.5 serta melakukan pendugaan nilai konsentrasi PM2.5 menggunakan metode GTWR di wilayah DKI Jakarta. Hasil menunjukkan bahwa parameter meteorologi berkorelasi atau memiliki pengaruh terhadap konsentrasi PM2.5 khususnya parameter suhu dan kelembaban. Pada perbandingan model terbaik menunjukkan bahwa metode GTWR merupakan metode pendugaan yang menghasilkan hasil yang lebih baik dari metode regresi linear berganda dengan nilai R2 sebesar 0,4156, RSS sebesar 844301,3 dan AIC sebesar 0,3410. Nilai R2 yang kecil dapat diakibatkan oleh faktor-faktor kompleks yang tidak dapat dipertimbangkan sepenuhnya, seperti aktivitas industri, transportasi, dan perubahan kebijakan lingkungan.
Evaluation of the Arima-Kalman model in predicting rainfall in Medan City in 2023 using observation data from 2013 – 2022 Lumbantoruan, Alva Josia; Darmawan, Yahya; Munawar, Munawar; Nardi, Nardi; Arifianto, Fendy; Ferdiansyah, Ervan
Indonesian Physics Communication Vol 22, No 1 (2025)
Publisher : Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jkfi.22.1.15-22

Abstract

This paper aims to evaluate the ARIMA-Kalman model in predicting rainfall in Medan City for the year 2023. The data used are historical observation data of rainfall from 2013 to 2022 that have been tested for stationary and homogeneity, which proved not to require additional correction. The analysis results show that the ARIMA-Kalman model can capture the general pattern of rainfall well, and shows superiority in producing predictions that are closer to the actual data, with a mean absolute error (MAE) value of 54.11, which is lower than the MAE of the ARIMA model which reaches 55.66. Although the ARIMA model has a smaller root mean square error (RMSE) (66.67 compared to 69.75 for ARIMA-Kalman), the ARIMA-Kalman model shows better consistency, especially in capturing significant fluctuations, such as the peak rainfall that occurred in July 2023. Therefore, ARIMA-Kalman is proven to be more accurate and reliable for predicting rainfall in Medan city, making it a better choice to support water resources planning and management.
Empirical orthogonal functions (EOF) analysis of spatial patterns of dominant variability in the Indian Ocean Manik, Willy Bonanja; Darmawan, Yahya; Munawar, Munawar; Nardi, Nardi; Arifianto, Fendy; Ferdiansyah, Ervan
Indonesian Physics Communication Vol 22, No 1 (2025)
Publisher : Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jkfi.22.1.23-26

Abstract

The Indian Ocean plays a crucial role in the global climate system, particularly in influencing the seasons in Indonesia. Sea surface temperature (SST) variability in the Indian Ocean affects rainfall patterns, extreme events, such as droughts and floods, in Indonesia. This study analyzes SST variability during the dry season (June – July – August, JJA) and rainy season (December – January – February, DJF) using satellite and reanalysis data from 1981 to 2023 with the empirical orthogonal function (EOF) method. The analysis shows that the dominant SST variability pattern during JJA is related to the Indian Ocean dipole (IOD), which influences rainfall and temperature patterns in Indonesia. In DJF, SST variability is more associated with the Asian-Australian monsoon, affecting rainfall patterns and the potential for floods. This research enhances the understanding of climate dynamics in the Indian Ocean and its impact on Indonesia, and it can be used to predict extreme climate events associated with SST variability.
Climate Suitability Analysis of Robusta Coffee and Its Projections in South Sumatera Province Whibowo, Gani Hesri; Arifianto, Fendy; Ferdiansyah, Ervan
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 13 No. 2 (2024): June 2024
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v13i2.512-524

Abstract

Climate suitability will support the growth of a plant such as Robusta coffee. This study aims to analyze the suitability of the Robusta coffee plant climate and its projection in South Sumatra. Climate suitability is assessed based on the weighting of air temperature, rainfall, number of dry months, altitude, soil texture, and slopes. This study used observation data on rainfall and air temperature at 48 rain post points in the Robusta coffee farming area. The projection uses scenarios shared socioeconomic pathways (SSP) 2-4.5 and 5-8.5 of the MIROC6 model with three projection periods of 2021-2030, 2031-2040, and 2041-2050. The results showed that baseline period 35% of the area as a very suitable class and 65% in fairly suitable class. Based on the projected results of scenario SSP2-4.5 period 1 to 3 have the same percentage of area, that is 91% in very suitable class and 9% in fairly suitable class. The projected results of the scenario SSP5-8.5 show an improvement but not better than scenario SSP2-4.5. The percentage of area very suitable class for periods 1 to 3 of 89%, 50%, and 85% respectively. Keywords: Climate suitability, Projection, Robusta coffee, SSP2-4.5, SSP5-8.5.
Peningkatan Kapasitas Perangkat Masyarakat dalam Pengolahan Data Spasial Menuju Masyarakat Tanggap Bencana Banjir di Kecamatan Pesanggrahan Jakarta Selatan Darmawan, Yahya; Munawar, Munawar; Sudarisman, Maman; Ferdiyansyah, Ervan; Arifianto, Fendy; Virgianto, Rista Hernandi; Amri, Sayful; Veanti, Desak Putu Okta
Jurnal Kreativitas Pengabdian Kepada Masyarakat (PKM) Vol 7, No 3 (2024): Volume 7 No 3 2024
Publisher : Universitas Malahayati Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/jkpm.v7i3.13681

Abstract

ABSTRAK Pengolahan data spasial diperlukan dalam administrasi dan manajemen pemerintahan di berbagai wilayah, termasuk Kecamatan Pesanggrahan, Jakarta Selatan. Namun, kemampuan pengolahan data spasial oleh perangkat pemerintahan di Kecamatan Pesanggrahan masih terbatas. Oleh karena itu, pelatihan ini bertujuan untuk meningkatkan kapasitas masyarakat dalam mengelola data spasial, khususnya terkait respons terhadap banjir di kecamatan tersebut. Peningkatan kapasitas dilakukan melalui kegiatan bimbingan teknis dan Forum Group Discussion (FGD) yang kemudian dievaluasi. Hasil survei sebelum dan setelah pelatihan menunjukkan peningkatan pemahaman masyarakat terkait tugas dan fungsi Badan Meteorologi, Klimatologi, dan Geofisika (BMKG), termasuk informasi yang disampaikan kepada masyarakat. Setelah pelatihan, terjadi peningkatan yang signifikan dalam pemahaman masyarakat, khususnya terkait pengolahan data spasial dengan Sistem Informasi Geografis (SIG) dan potensi bencana hidrometeorologi di Kecamatan Pesanggrahan. Kata Kunci: Sistem Informasi Geografis (SIG), Data Spasial, Kecamatan Pesanggrahan, Kapasitas Masyarakat  ABSTRACT Spatial data processing is crucial for governance in various regions, including Pesanggrahan Subdistrict, South Jakarta. However, the capability in spatial data processing among local government officials in Pesanggrahan Subdistrict is still limited. Therefore, this training aims to enhance the community's capacity in managing spatial data, especially in response to floods in the subdistrict. Capacity-building is conducted through technical guidance activities and Forum Group Discussions (FGD), followed by an evaluation. Pre-and post-training surveys show an improved understanding among the community regarding the roles and functions of the Meteorology, Climatology, and Geophysics Agency (BMKG), including the information conveyed to the public. After the training, there is a significant increase in the community's understanding, particularly in spatial data processing with Geographic Information System (GIS) and the potential risks of hydrometeorological disasters in Pesanggrahan Subdistrict. Keywords: Geographic Information System (GIS), Spatial Data, Pesanggrahan Subdistrict, Community Capacity
EVALUASI MODEL LONG SHORT-TERM MEMORY (LSTM) UNTUK PREDIKSI CURAH HUJAN DI ZONA MUSIM (ZOM) PROVINSI JAWA TENGAH Catur Handika; Fendy Arifianto; Yahya Darmawan; Ervan Ferdiansyah
Inovasi Fisika Indonesia Vol. 15 No. 2 (2026): Vol 15 No 2
Publisher : Prodi Fisika FMIPA Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/ifi.v15n2.p337-351

Abstract

Provinsi Jawa Tengah merupakan salah satu sentra produksi pertanian nasional yang juga rentan terhadap bencana hidrometeorologi, sehingga informasi curah hujan dasarian yang akurat diperlukan untuk mendukung sektor tersebut. Penelitian ini bertujuan menghasilkan kinerja model Long Short-Term Memory (LSTM) dalam memprediksi curah hujan dasarian pada 54 Zona Musim (ZOM) di Jawa Tengah. Data yang digunakan berupa curah hujan blending serta angin zonal (U850), angin meridional (V850), dan kelembaban relatif (RH850) lapisan 850 hPa dari ERA5. Data dibagi menjadi pelatihan tahun 1991–2015, validasi tahun 2016–2020, dan pengujian tahun 2021–2024. Seleksi prediktor menggunakan Light Gradient Boosting Machine (LightGBM) dan SHapley Additive exPlanations (SHAP), yang menunjukkan RH850 dan U850 sebagai prediktor dengan kontribusi terbesar. Hasil penelitian menunjukkan model LSTM mampu merepresentasikan pola temporal dan tren musiman curah hujan dengan baik, dengan korelasi keliling antara 0,53 hingga 0,81, RMSE antara 27,70 hingga 76,47 mm, MAE antara 19,41 hingga 55,63 mm, dan bias antara −17,27 hingga 13,47 mm. Namun performa model masih bervariasi setiap ZOM dan kurang optimal dalam mengestimasi puncak curah hujan ekstrem.
Co-Authors Amri, Sayful Arno, Giarno Asri Pratiwi Avrionesti, Avrionesti Catur Handika Dibyo Susanto Donni H, Yosafat Ervan Ferdiansyah Ervan Ferdiansyah Ervan Ferdiansyah Fendiarni Luthfi Maghfiroh Ferdiansyah, Ervan Ferdiyansyah, Ervan Gani Hesri Whibowo Giarno Giarno Hardiyanti Ismail Hilmy Muhammad Rosyid Ilil Firrizqi Nur Ilahi Impron Impron Lumbantoruan, Alva Josia M. Fatnan Hikami Manik, Willy Bonanja Munawar Munawar Nanny Nuraini Prima, Rajab R Lukman Heryadi Ramdhani, Muhammad Zaki Rista Hernandi Virgianto Silvia Yulianti Sudarisman, Maman Veanti, Desak Putu Okta Whibowo, Gani Hesri Yahya Darmawan Yahya Darmawan Yonny Koesmaryono