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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika KOMPUTA : Jurnal Ilmiah Komputer dan Informatika M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Varian International Journal of Advances in Data and Information Systems Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia International Journal of Data Science, Engineering, and Analytics (IJDASEA) Indonesian Community Journal eProceedings of Engineering
Regita Putri Permata
Telkom University
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Journal : Jurnal Varian

 1 
Daily Rainfall Forecasting with ARIMA Exogenous Variables and Support Vector Regression Regita Putri Permata; Rifdatun Ni'mah; Andrea Tri Rian Dani
Jurnal Varian Vol 7 No 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3202

Abstract

There is a seasonal element every year, with the dry season often lasting from May to October and the rainy season lasting from November to April. However, climate change causes the changing of the rainy and dry seasons to be erratic, so it is necessary to anticipate weather conditions. Prediction of rainfall is used to see natural conditions in the future with time series modeling. The rainfall modeling method at the six Surabaya observation posts used is the Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) and Support Vector Regression. The exogenous variable used is the captured seasonal pattern of rainfall. The SVR model uses input lags from the ARIMAX model and parameter tuning uses the Kernel Radial Based Function. Selection of the best model uses the minimum RMSE value. The results showed that the average occurrence of rain at the six rainfall observation posts occurred in January, February, March, April, November and December. The ARIMAX method in this study is well used to predict rainfall in Gubeng and rainfall in Wonorejo. The SVR input lag ARIMAX method is good for predicting rainfall for Keputih, Kedung Cowek, Wonokromo and Gunung Sari. Nonparametric methods are better used to forecast rainfall data because they are able to capture data patterns with greater volatility than parametric methods, one of which is the SVR method.
Mengeksplorasi Masalah Kejahatan dari POV Statistik dengan Regresi Binomial Negatif Dani, Andrea Tri Rian; Fathurahman, M.; Ni'matuzzahroh, Ludia; Putri Permata, Regita; Putra, Fachrian Bimantoro
Jurnal Varian Vol. 8 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i2.4445

Abstract

Criminality is a complex issue in Indonesia that is very important to the government, law enforcement agencies, and society. The underlying causes of Indonesia's crime problem are complex and impacted by various circumstances. The aim of this research is to model the crime problem in Indonesia and determine the influencing factors.  The method used in this research is Negative Binomial Regression. The results of the study show that the negative binomial regression model can be used to model criminal problems because the variance value is more significant than the average. Based on the parameter significance test results, both simultaneously and partially, the open unemployment rate, Gini ratio, average years of schooling, and prevalence of inadequate food consumption significantly affect the crime rate, with an Akaike’s Information Criterion Corrected (AICc) value of 698,098. These findings suggest that addressing economic inequality, unemployment, education, and food security could help reduce crime in Indonesia. Policies aimed at improving job opportunities, reducing income disparity, and enhancing education and food security are crucial in mitigating crime. This study provides valuable insights for policymakers and law enforcement agencies, offering a foundation for more targeted and effective crime prevention strategies. Future research could employ the robust Poisson Inverse Gaussian Regression method to avoid the overdispersion problem. 
Daily Rainfall Forecasting with ARIMA Exogenous Variables and Support Vector Regression Permata, Regita Putri; Ni'mah, Rifdatun; Dani, Andrea Tri Rian
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3202

Abstract

There is a seasonal element every year, with the dry season often lasting from May to October and the rainy season lasting from November to April. However, climate change causes the changing of the rainy and dry seasons to be erratic, so it is necessary to anticipate weather conditions. Prediction of rainfall is used to see natural conditions in the future with time series modeling. The rainfall modeling method at the six Surabaya observation posts used is the Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) and Support Vector Regression. The exogenous variable used is the captured seasonal pattern of rainfall. The SVR model uses input lags from the ARIMAX model and parameter tuning uses the Kernel Radial Based Function. Selection of the best model uses the minimum RMSE value. The results showed that the average occurrence of rain at the six rainfall observation posts occurred in January, February, March, April, November and December. The ARIMAX method in this study is well used to predict rainfall in Gubeng and rainfall in Wonorejo. The SVR input lag ARIMAX method is good for predicting rainfall for Keputih, Kedung Cowek, Wonokromo and Gunung Sari. Nonparametric methods are better used to forecast rainfall data because they are able to capture data patterns with greater volatility than parametric methods, one of which is the SVR method.
Co-Authors Adzanil Rachmadhi Putra Alifah, Amalia Nur Alifianti, Tarisma Dwi Putri Almaasah Zatri, Nasywaa Amri Muhaimin Andrea Tri Rian Dani Astikhatul Mufaidah Dani, Andrea Tri Rian Faradila Efaranti , Inge Helisyah Nur Fadhilah Helisyah Nur Fadhilah M. Fathurahman Muhaimin, Amri Nadifah, Rofiatun Ni'matuzzahroh, Ludia Ni’mah, Rifdatun Putra, Fachrian Bimantoro Rifdatun Ni’mah Rindra Syaifullah Ryanta Meylinda Savira Salsabiila, Annas Thasya Haafizhah Sanjaya, I Made Wisnu Adi Savira, Ryanta Meylinda Sembiring, Fikrie Hartanta Shefira Eka Putri Sri Hidayati Syaifullah, Rindra Taqhsya Dwiyana , Ananda Yohanes Setiawan