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Harisdianto, Harisdianto
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Chemistry Mathematics Physics

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Simple Linear Regression Analysis of Temperature Trends using R Language: Case Study of Palembang City 1991-2020 Alfikro, Ihsan; Harisdianto, Harisdianto; Adnan, Assaidah; Affandi, Azhar Kholiq; Setiabudidaya, Dedi
Jurnal Penelitian Sains Vol 26, No 2 (2024)
Publisher : Faculty of Mathtmatics and Natural Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56064/jps.v26i2.984

Abstract

Global warming is the most world priority of environment issues, which is indicate continously increment of  global mean temperature of Earth. Various research shown detrimental impact of Earth environment, due to increment of Earth temperature. Our study provide temperature trend of small scope observation unit, the urban area of Palembang City at latitude 2,9° and longtitude 104,7° for 30 years, from 1991-2020. Linear regression analysis shown the temperature increase on skin temperature (TS) and temperature at 2 meter range (T2M) are 0,4°C and 0,46°C, respectively. These variable can become indicator of global warming occurance on small scope of observation unit.Keywords: Global warming, Temperature trend, Linear regression
Downscaling Spasial Data Curah Hujan TRMM di Wilayah Sumatera Selatan dengan Algoritma Artificial Neural Network dan Random Forest Regression Harisdianto, Harisdianto; Affandi, Azhar Kholiq; Ariani, Menik; Suhadi, Suhadi
Jurnal Penelitian Sains Vol 25, No 3 (2023)
Publisher : Faculty of Mathtmatics and Natural Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56064/jps.v25i3.906

Abstract

Pemahaman tentang karakter distribusi curah hujan temporal dan spasial sangat penting dalam menunjang studi tentang ekologi, meteorologi, dan hidrologi. Penelitian ini memetakan kembali distribusi spasial curah hujan tahunan TRMM di wilayah Sumatera Selatan pada resolusi tinggi menggunakan teknik downscaling berbasis machine learning, Artificial Neural Network (ANN) dan Random Forest Regression (RFR). Prediktor yang digunakan yaitu NDVI, DEM, Longitude dan Latitude. Proses downscaling spasial curah hujan TRMM dengan model ANN memiliki akurasi R^2 0, 6494, RMSE 728 mm/tahun dan MAE 715 mm/tahun. Model RFR memiliki kinerja lebih baik dengan nilai R^2 0,6818, RMSE 695 mm/tahun dan MAE 683 mm/tahun.
Co-Authors Affandi, Azhar Kholiq Alfikro, Ihsan Assaidah, Assaidah Dedi Setiabudidaya Suhadi Suhadi