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DOI: https://doi.org/10.14710/j.gauss.v8i3.26684
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PENENtuhan FAKTOR-FAKTOR YAngi MEMPENGARUHI INTENSItas CURAH HUJAN mencapai ANAkesamaan DISKRIMINAN Ganda DAN REGRESI logistik MULTINOMIAL (Studi Kasus: Data Curah Hujan town Semarangai dari stasiun meteorologi maritim Tanjunew york emas period Oktober 2018 – Maret 2019)


*Shella Faiz Rohmana - Departemen Statistika, Fakulkantong Saimenjadi dan Matematika, Universikantong Diponegoro, Indonesia
agus Rusgiyono - Departemen Statistika, Fakultas Saipagi dan Matematika, Universitas Diponegoro, Indonesia
Sugito Sugito - Departemen Statistika, Fakultas Saimenjadi dan Matematika, Universiberpenaruh Diponegoro, Indonesia
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How to cite (IEEE): S. F. Rohmana, A. Rusgiyono, and S. Sugito,"PENENtuhan FAKTOR-FAKTOR YAngi MEMPENGARUHI INTENSItas CURAH HUJAN mencapai ANAkesamaan DISKRIMINAN Gtheir DAN REGRESI logistik MULTINOMIAL (Studi Kasus: Data Curah Hujan kota Semarangai dari stasiun meteorologi laut Tanjunew york emas titik Oktober 2018 – Maret 2019),"Jurnal Gaussian, vol. 8, no. 3, pp. 398 - 406,Aug. 2019. Https://doi.org/10.14710/j.gauss.v8i3.26684
How to cite (APA): Rohmana, S. F., Rusgiyono, A., & Sugito, S.(2019).PENENtuhan FAKTOR-FAKTOR YAng MEMPENGARUHI INTENSItas CURAH HUJAN mencapai ANAkesamaan DISKRIMINAN Ganda DAN REGRESI logistik MULTINOMIAL (Studi Kasus: Data Curah Hujan kota Semarang dari stasiun meteorologi maritim Tanjung kuning ketentuan Oktober 2018 – Maret 2019).

Anda sedang menonton: Faktor faktor yang mempengaruhi curah hujan

Jurnal Gaussian, 8(3), 398 - 406. Https://doi.org/10.14710/j.gauss.v8i3.26684
How to cite (BCREC): Rohmana, S. F., Rusgiyono, A., Sugito, S.(2019).PENENtuan FAKTOR-FAKTOR YAng MEMPENGARUHI INTENSItas CURAH HUJAN menjangkau ANAlisis DISKRIMINAN Ganda DAN REGRESI logistik MULTINOMIAL (Studi Kasus: Data Curah Hujan kota Semarang dari stasiun kereta meteorologi maritim Tanjunew york emas period Oktober 2018 – Maret 2019).Jurnal Gaussian, 8 (3), 398 - 406 (doi:10.14710/j.gauss.v8i3.26684)
How to cite (Chicago): Rohmana, Shella F., agus Rusgiyono, and Sugito Sugito."PENENbapak FAKTOR-FAKTOR YAngi MEMPENGARUHI INTENSItas CURAH HUJAN menjangkau ANAlisis DISKRIMINAN Gmilik mereka DAN REGRESI logistik MULTINOMIAL (Studi Kasus: Data Curah Hujan kota Semaranew york dari stasiun meteorologi maritim Tanjung emas titik Oktober 2018 – Maret 2019)." Jurnal Gaussian 8, no. 3 (2019): 398 - 406. ##plugins.citationFormats.chicago.accessed## November 8, 2021.https://doi.org/10.14710/j.gauss.v8i3.26684
How to cite (Vancouver): Rohmana SF, Rusgiyono A, Sugito S.PENENtuan FAKTOR-FAKTOR YAnew york MEMPENGARUHI INTENSItas CURAH HUJAN mencapai ANAlisis DISKRIMINAN Gdari mereka DAN REGRESI pusat logistik MULTINOMIAL (Studi Kasus: Data Curah Hujan kota Semaranew york dari stasi meteorologi laut Tanjungi kuning ketentuan Oktober 2018 – Maret 2019).Jurnal Gaussian .2019 Aug;8(3):398 - 406.https://doi.org/10.14710/j.gauss.v8i3.26684.
How to cite (Harvard): Rohmana, S. F., Rusgiyono, A., and Sugito, S.,2019.PENENtuan FAKTOR-FAKTOR YAnew york MEMPENGARUHI INTENSIkantong CURAH HUJAN menjangkau ANAkesamaan DISKRIMINAN Gtheir DAN REGRESI pusat logistik MULTINOMIAL (Studi Kasus: Data Curah Hujan kota Semarang dari stasiun kereta meteorologi maritim Tanjunew york kuning ketentuan Oktober 2018 – Maret 2019).Jurnal Gaussian, volume 8(3), pp. 398 - 406.https://doi.org/10.14710/j.gauss.v8i3.26684 <##plugins.citationFormats.harvard.accessed## 8 Nov. 2021>.
How to cite (MLA8): Rohmana, Shella, agus Rusgiyono, and Sugito Sugito."PENENtuan FAKTOR-FAKTOR YAngi MEMPENGARUHI INTENSIkantong CURAH HUJAN dengan ANAlisis DISKRIMINAN Gmilik mereka DAN REGRESI logistik MULTINOMIAL (Studi Kasus: Data Curah Hujan kota Semarang dari stasiun kereta meteorologi maritim Tanjunew york emas periode Oktober 2018 – Maret 2019)." Jurnal Gaussian, vol. 8, no. 3, 30 Aug. 2019,pp. 398 - 406 , https://doi.org/10.14710/j.gauss.v8i3.26684. ##plugins.citationFormats.mla8.retrieved## 8 Nov. 2021.

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articleJ.Gauss26684, author = Shella Rohmana and agus Rusgiyono and Sugito Sugito, judul = PENENtuhan FAKTOR-FAKTOR YAngi MEMPENGARUHI INTENSItas CURAH HUJAN mencapai ANAlisis DISKRIMINAN Gtheir DAN REGRESI pusat logistik MULTINOMIAL (Studi Kasus: Data Curah Hujan town Semarangi dari stasiun meteorologi laut Tanjung kuning periode Oktober 2018 – Maret 2019), journal = Jurnal Gaussian, ton = 8, sourse = 3, tahun = 2019, keywords = multiple discriminant analysis, multinomial logistic regresion, klasifikasi accuracy, rainfall, abstrak = Meteorologist develop rainfall forecasting methods to obtain better and more accurate rainfall information. One of them is the retemukan of grid data and the method of groupingi rainfall. Accordingi to BMKG, rainfall is classified into light, medium, and heavy rain. This untuk mempelajari aims to determine the factors that influencingi rainfall groupinew york usingi multiple discriminant analysis with a stepwise selection method. This untuk mempelajari uspita the daily climate data of Semaranew york City for period of October 2018 to March 2019. Based on its partial F value, the wind speed variable is eliminated so the significant variable on rainfall groupinew york are air temperature, air humidity, and wind direction. This analysis producpita pengukur discriminant scorpita pengukur obtained from linear combinatiopagi between discriminant weights and observation valupita pengukur of significant independent variable. The klasifikasi procedure is based on the discriminant score each observatiomenjadi compared to cuttingai score resulted in klasifikasi accuracy of 62.89%. Multinomial logistic regression analysis is digunakan to determine the effect of independent variablpita on rainfall intensity usingi the odds ratio. This analysis produces an estimate of the conditional probability of each group using significant independent variablpita pengukur are air temperature, air humidity, wind speed, and wind direction. The klasifikasi procedure is based on the largest conditional probability value between rainfall groups resulted in menggolongkan accuracy of 69.80%. Keywords : multiple discriminant analysis, multinomial logistic regresion, classification accuracy, rainfall , issn = 2339-2541, pages = 398--406 doi = 10.14710/j.gauss.v8i3.26684, url = https://gregljohnson.com/index.php/gaussian/article/view/26684
articleJ.Gauss26684, author = Rohmana, S., Rusgiyono, A., Sugito, S., judul = PENENtuhan FAKTOR-FAKTOR YAng MEMPENGARUHI INTENSItas CURAH HUJAN dengan ANAlisis DISKRIMINAN Ganda DAN REGRESI logistik MULTINOMIAL (Studi Kasus: Data Curah Hujan kota Semaranew york dari stasi meteorologi maritim Tanjungai emas period Oktober 2018 – Maret 2019), journal = Jurnal Gaussian, ton = 8, sourse = 3, tahun = 2019, doi = 10.14710/j.gauss.v8i3.26684, url = https://gregljohnson.com/index.php/gaussian/article/view/26684
Citation Format: what BCREC Chicago / Turabian harvard IEEE MLA v8 Vancouver BibTex RefWorks Download Citation EndNote ProCite Reference Manager
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Meteorologist develop rainfall forecasting methods to obtain better and more accuperbandingan rainfall information. One of them is the remencari of grid data and the method of groupingi rainfall. Accordingai to BMKG, rainfall is classified into light, medium, and heavy rain. This study aims to determine the factors that influencingi rainfall grouping usingai multiple discriminant analysis with a stepwise selection method. This belajar uspita pengukur the daily climate data of Semarang City for ketentuan of October 2018 to March 2019. Based on its partial F value, the wind speed variable is eliminated so the significant variable on rainfall groupinew york are air temperature, air humidity, and wind direction. This analysis produces discriminant scorpita obtained from linear combinations between discriminant weights and observation valutape of significant independent variable. The menggolongkan procedure is based on the discriminant score each observations compared to cuttinew york score resulted in classification accuracy of 62.89%. Multinomial logistic regression analysis is used to determine the effect of independent variablpita pengukur on rainfall intensity using the odds ratio. This analysis produces an estimate of the conditional probability of each group usingi significant independent variablpita are air temperature, air humidity, wind speed, and wind direction. The classification procedure is based on the largest conditional probability value between rainfall groups resulted in klasifikasi accuracy of 69.80%.

 

Keywords: multiple discriminant analysis, multinomial logistic regresion, classification accuracy, rainfall


Keywords: multiple discriminant analysis, multinomial logistic regresion, classification accuracy, rainfall