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All Journal Jurnal Infinity Jurnal Al Bayan: Jurnal Jurusan Pendidikan Bahasa Arab Jurnal KALAM Jurnal Theologia Journal of the Indonesian Mathematical Society JURNAL JPSD (Jurnal Pendidikan Sekolah Dasar) PROFIT : Jurnal Administrasi Bisnis Indonesian Journal of Science and Technology JURNAL MEDIA INFORMATIKA BUDIDARMA Al-I´lam: Jurnal Komunikasi dan Penyiaran Islam Edukasi Islami: Jurnal Pendidikan Islam JPPI | Jurnal Penelitian Pendidikan Islam Jurnal Literasiologi Al - Tadabbur: Jurnal Ilmu Al-Qur'an dan Tafsir Journal of Islam and Science INTELEKTIUM Islamic Business and Finance (IBF) Lani: Jurnal Kajian Ilmu Sejarah dan Budaya Jurnal Komunikasi Islam AL-ARKHABIIL: Jurnal Pengabdian Masyarakat Cerdika: Jurnal Ilmiah Indonesia Journal of Critical Realism in Socio-Economic (JOCRISE) IndoMath: Indonesia Mathematics Education Cendikia Pendidikan Al-Karim: International Journal of Quranic and Islamic Studies Jurnal Infinity
Muhammad Taqiyuddin
Universitas Brawijaya
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

 1 
Prediksi Cuaca Kabupaten Sleman Menggunakan Algoritma Random Forest Taqiyuddin, Muhammad; Bayu Sasongko, Theopilus
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7897

Abstract

Indonesia, as a tropical country, exhibits complex and varied weather patterns influenced by high temperatures, precipitation, and humidity throughout the year. This high weather variability often leads to uncertainties in weather forecasting, affecting sectors such as agriculture, transportation, and tourism. This study aims to predict the weather in Sleman Regency using the Random Forest algorithm to address forecasting uncertainties and provide more accurate predictions. The method involves collecting daily weather data from BMKG, conducting exploratory data analysis to understand data characteristics, and processing the data, including cleaning and normalization, before applying it to the Random Forest model. The study's goal is to improve the accuracy of weather predictions to support more precise and effective decision-making. Preliminary results show that the Random Forest model performs well with a Mean Absolute Error (MAE) of 0.060, Mean Squared Error (MSE) of 0.009, Root Mean Squared Error (RMSE) of 0.094, and R-squared of 0.691. The model evaluation indicates good performance in predicting weather in the study area. With these results, the developed weather prediction model holds significant potential to enhance sustainability and operational efficiency in various sectors reliant on weather conditions.
Prediksi Cuaca Kabupaten Sleman Menggunakan Algoritma Random Forest Taqiyuddin, Muhammad; Bayu Sasongko, Theopilus
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7897

Abstract

Indonesia, as a tropical country, exhibits complex and varied weather patterns influenced by high temperatures, precipitation, and humidity throughout the year. This high weather variability often leads to uncertainties in weather forecasting, affecting sectors such as agriculture, transportation, and tourism. This study aims to predict the weather in Sleman Regency using the Random Forest algorithm to address forecasting uncertainties and provide more accurate predictions. The method involves collecting daily weather data from BMKG, conducting exploratory data analysis to understand data characteristics, and processing the data, including cleaning and normalization, before applying it to the Random Forest model. The study's goal is to improve the accuracy of weather predictions to support more precise and effective decision-making. Preliminary results show that the Random Forest model performs well with a Mean Absolute Error (MAE) of 0.060, Mean Squared Error (MSE) of 0.009, Root Mean Squared Error (RMSE) of 0.094, and R-squared of 0.691. The model evaluation indicates good performance in predicting weather in the study area. With these results, the developed weather prediction model holds significant potential to enhance sustainability and operational efficiency in various sectors reliant on weather conditions.
Co-Authors Akmal, Azzam Alfatih Al-Mujaddidi, Shibghah Alandika, Ozy Vebry Andika, Joko Andika, Titin Apriani, Meta Asidin, Mohamad Tri Afriyadi Nur Aulia, Fairus Bhakti, Putri Ayu Kirana Darminto . Dewanti, Asri Hajar Dian Usdiyana El-Bahy, Muhammad Zhafir Elah Nurlaelah Firdausi, Faliqul Jannah Fitra Awalia Rahmawati Fitrianawati, Meita Fuad, Iwan Agustiawan Hamid Fahmy Zarkasyi Hapzi, MHD Hasep Saputra, Hasep Hastuti, Miryana Hidayat, M.Alif Ifronika - Ihsan, Nur Hadi Indra Rukmana, Indra Irama, Debi Jarman Arroisi Jarnawi Afgani Dahlan Khoirunnisa Khoirunnisa Kusumaningrum, Farida Latifah, Afiatul Luqman Hakim Handoko Marinda, Aflia Bela Masta, Al Azhary Naufal Rabbani, Muhammad Nur, Ifa Rodifah Nurizza, Shendy Septyaneu Nurma Yunita, Nurma Pebrianti, Aneu Prayudi, Soni Rofiah, Desy Rosmayanti, Imalia Dwi Rukmini, Diah Salim, Mohammad Syam’un Salim, Mohammad Syam’un Saroji, Ahmad Sasongko, Theopilus Bayu Satrianto, Rizki satrianto, Rizki Satrianto Scarayu, Hana Mutiara Setiawan, Jamil Sutarto Sutarto Syamsul Arifin Taher Al habsji Tiara, Meza Triana Harmini Ulfa, Nadia Wahyudi, Sidiq Wiranata, Erik Yulesni, Sopi Yuliza, Midya