Browsing by Author "Bustami, Bustami"
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Item ANALISIS FAKTOR KEMATIAN BAYI MENGGUNAKAN REGRESI GENERALIZED POISSON(Elfitra, 2022-04) Astuti, Yolanda Silvi Sri; Bustami, BustamiInfant mortality is influenced by several factors, namely the consumption of pregnant vitamins, births assisted by medical personnel, infant weight and health services for babies after birth. The infant mortality rate in Riau Province is currently 24 per 1,000 live births. This research was completed by using generalized Poisson regression because, in infant mortality data, the data did not meet the equidispersion property, where the mean data was not the same as the data variance. The results showed that the significant factors causing infant mortality were infant weight, family life behavior and vitamin consumption in pregnant women.Item ANALISIS REGRESI SPASIAL FIXED EFFECT DATA PANEL PADA KASUS PERSENTASE KEMISKINAN DI PROVINSI RIAU(2021-02) Putri, Rizka Amalia; Bustami, BustamiThe poverty rate in Riau Province in 2014 reached 8.42%. This figure is still relatively high. Economic growth, total population, district / city minimum wages, and the open unemployment rate are considered to have contributed to the percentage of poverty in Riau Province. For this reason, testing of these variables is carried out. The data used is data from BPS Riau Province in 2011-2019 in the form of panel data which is a combination of cross section data and time series data, the analysis is carried out using spatial regression panel data which has 2 models, namely the Spatial Lag Model (SAR) and the Spatial Model Error (SEM). This study shows that the SAR fixed effect model is better with economic growth as a significant variable and the results of the calculation of the criteria for the goodness of the model are 99,81%.Item BOBOT SECARA GEOGRAFIS PADA KASUS DEMAM BERDARAH DENGUE DI INDONESIA TAHUN 2015(Elfitra, 2022-07) Romaslia, Susanti; Bustami, BustamiRelationship analysis on spatial data can be done by using the method Geographically Weighted Poisson Regression (GWPR), which is a local form of Poisson regression which is applied to spatial data, where location is considered. The purpose of this study was to determine the GWPR model and the factors that influence the number of cases of Dengue Hemorrhagic Fever (DHF) in Indonesia in 2015. The spatial weighting used is the Gaussian kernel function and the optimum bandwidth. The parameter estimation method for the GWPR model is Maximum Likelihood Estimate (MLE). The results of this study indicate that the maximum likelihood estimator is obtained using the Newton-Raphson iteration method and the factors that affect the number of DHF cases in Indonesia are local. Locally influencing factors are population density, number of health workers, number of health facilities, and amount of rainfall.Item IMPLEMENTASI ANALISIS KOMPONEN UTAMA UNTUK MEREDUKSI DIMENSI FAKTOR INFLASI BERDASARKAN INDEKS HARGA KONSUMEN KOTA DUMAI(2021-02) Desliana, Fitri Anggreani; Bustami, BustamiThe Consumer Price Index (CPI) is one of the important indicators that used as basis for determining the rate of inflation. In this study, the data that used are Dumai City’s CPI from January 2016 until December 2019. The CPI consist of seven subgroups that affect inflation in Dumai City. The seven subgroups are Foodstuff; Processed Food, Baverage, Cigaretts, and Tobacco; Housing, Electricity, Water, and Gas; Clothing; Health; Education, Recreation, and Sports; Transportation, Communication, and Financial Services. These factors will be reduced using Principal Component Analysis to identify the main factor that most contribute in determining the inflation rate. This study shows that seven dimension of factors can be reduced into two main factors, they are Primary Needs Factor and Complementary Needs Factor with a total variant 91.996%.Item MENGIDENTIFIKASI FAKTOR-FAKTOR YANG MEMPENGARUHI LAMA MASA STUDI MAHASISWA MENGGUNAKAN REGRESI COX PROPORTIONAL HAZARD(perpustakaan UR, 2021-07) Putri, Yolla Lizia Dwi; Bustami, BustamiThe study period is duration it takes students to complete their education starting from the time they enter college until they are graduated. In general, a student is said to graduate on time if he succeeds in taking a study period of no more than 4 years. This study aims to identify that affect the length of study period of the 2015 FMIPA UNRI students. The research variables consist of five subgroups which are factors that are thought to affect the students study period. The five subgroups are gender, (male and female), GPA, regional origin, (Pekanbaru and outside Pekanbaru), type of SMA, (SMA, SMK, and MAN), and parental occupation. Method that can be used to determine these factors is Survival Analysis through the Cox Proportional Hazard regression model. Through parameter estimation in the procedure of forming the Cox Proportional Hazard regression model. In this study, it was found that the factors that significantly influence the length of student study are GPA and parents work is as a farmerItem METODE PEMULUSAN EKSPONENSIAL HOLT-WINTERS UNTUK PERAMALAN DATA WISATAWAN MANCANEGARA KOTA PEKANBARU(perpustakaan UR, 2021-07) Haloho, Sarah Donda Betris; Bustami, BustamiTourism is an industrial economic phenomenon that is experiencing rapid development for the economy. Tourists need transportation to travel to the places they want to visit. This study aims to determine the right model for development of the number of tourists visiting Pekanbaru City via air transportation at Sultan Syarif Kasim II airport and forecasting the development of the number of tourists in the coming year. The data used comes from BPS Riau Province with the variable number of tourist arrivals in 2012-2019. The results of this study indicate that fluctuations occur every year and have a seasonal data pattern. The Holt-Winters Additive Exponential forecasting method is more effective to use because the resulting error value is minimal.Item PEMODELAN EKSPOR IMPOR PDRB PROVINSI RIAU TAHUN 2010 – 2019 MENGGUNAKAN VAR DAN VECM(perpustakaan UR, 2021-06) Ayu, Winda; Bustami, BustamiThis research aims to model the corresponding relationships GRDP import and export variables using Vector Autoregressive (VAR) and Vector Error Correction Model (VECM). The data used is sourced from Riau publication data in figures from the Central Statistics Agency for Riau Province in 2010 – 2019. The data analysis in this study uses Rstudio 4.0.2 software. Based on the analysis conducted, it is found that the endogenous variables that most influence changes in exports are the import and GRDP variables in the previous period. The endogenous variables that most influence changes in imports are the export variables of the previous period, while those that most influence changes in GRDP are the import variables and the export variables in the previous period. The results showed that the best model for export, import and GRDP variables was the VECM model.Item PEMODELAN REGRESI POISSON INVERSE GAUSSIAN TERHADAP GIZI BURUK PADA BALITA DI INDONESIA(Elfitra, 2022-06) Susilawati, Nurdiah; Bustami, BustamiMalnutrition is an important concern for the health and growth of toddlers because it can cause death at a very early age. The factors that influence malnutrition can be modeled with Poisson Inverse Gaussian regression is used to overcome Poisson data that has overdispersion, the variance is greater than the mean. The variables used were low birth weight babies, rural slum households, toddlers who received complete immunizations, toddlers who received exclusive breastfeeding, and the poor. This study aims to find the best model using Poisson Inverse Gaussian regression. Based on the results of the analysis, the Poisson Inverse Gaussian regression model is obtained 𝜇̂ = exp(−0.0591 + 0.1060𝑋1 − 0.0061𝑋3 + 0.0384𝑋5) with variables that significantly influence the percentage of low birth weight and the percentage poor people.Item PENERAPAN REGRESI KOMPONEN UTAMA ROBUST S-ESTIMATOR UNTUK ANALISIS PENGANGGURAN DI KOTA DUMAI(2021-02) Sari, Rizki Ayu Fitrian; Bustami, BustamiThe least squares method is a method for estimating parameters. This method is not appropriate for data that contains outliers, therefore, a robust regression regression method is used. Robust regression is a regression method that is used when there are outliers that can affect the model. In this study, the robust method used is the S-estimator. This study applies robust S-estimator regression to data containing outliers. A better model is selected based on the RSE and ̅ . The model is applied for the study of the number of unemployed in Dumai City 2006-2019. The independent variables are average length of schooling, net enrollment rate, school enrollment rate, human development index, population growth rate, and the poor. Based on the regression equation used, the study show that the factors affecting the unemployment rate are the average length of schooling and the poor population using the S-estimate regression method.Item PENERAPANMETODE DEKOMPOSISI UNTUK PERAMALAN HARGA SAHAMPT BANKCENTRAL ASIA TBK(Elfitra, 2023-07) Aulia, Feby Sukma; Bustami, BustamiThe stock price is an indicator of the success of a company. The higher and more stable the stock price, the better the company's value in the market. Shares are proof of ownership of capital invested by investors. The purpose of this study is to predict the stock price of PT Bank Central Asia Tbk using the decomposition method. The decomposition method is a time series data forecasting method that uses four components, namely seasonality, trend, cycles, and errors provided that the data contains trend and seasonal patterns. This study uses economic and business data regarding the closing price of shares of PT Bank Central Asia Tbk for the period 2018 – 2022. The results of this study obtained that the multiplicative model decomposition method is an excellent model with a MAPE acquisition of 3.81%. Stock price forecasting for the next period has increased with a tendency to decrease every June.Item PENERAPANMETODE SINGLE EXPONENTIAL SMOOTHING PADA PERAMALAN INFLASI KOTA PEKANBARU(Elfitra, 2023-07) Aprilania, Cindy; Bustami, BustamiForecasting using the single exponential smoothing method is a time series forecasting that has a horizontal data pattern. In this study the data used is inflation data. Based on the pattern of time series data, the data obtained then analyzed with single exponential method with several alpha smoothing constant values. Measurement of forecasting accuracy for selecting the best model can be done by selecting the lowest forecasting error value using the Mean Absolute Percentage Error (MAPE). This study shows that the single exponential smoothing method with a smoothing constant alpha 0,3 has the minimum MAPE value compared to other alpha smoothing constants. Forecasting results using the exponential smoothing method with smoothing constant alpha 0,3 for the next period shows that inflation rate in Pekanbaru City will decrease.Item PENGGUNAAN METODE PEMULUSAN EKSPONENSIAL UNTUK PERAMALAN NILAI EKSPOR NON MIGAS DI INDONESIA(Elfitra, 2021-12) Thahira, Nisha; Bustami, BustamiThe exponential smoothing forecasting method is one of the time series data forecasting methods. This study uses non-oil and gas export data in Indonesia for the period January 2015-May 2021 indicating that there are elements of trends and seasonality. Based on the characteristics of the time series, the data obtained were then analyzed using Holt's double exponential smoothing method and triple exponential smoothing consisting of multiplicative and Holt-Winters additives. The best model selection can be done by choosing the minimum forecast error value. This study shows that the additive Holt- Winters method is the best forecasting method that has a minimum forecast error value. The forecast results using this method indicate that the value of non-oil exports will increase for the next period.Item PERAMALAN KEMUNCULAN TITIK PANAS DI INDONESIA MENGGUNAKAN ANALISIS INTERVENSI FUNGSI PULSE(Elfitra, 2023-10) Tampubolon, Putri Soraya; Bustami, BustamiThe forest area in Indonesia is decreasing, caused by forest fires either intentionally through land clearing or unintentionally due to climate change. Hotspots are an indicator used in detecting fires on a land. Efforts can be made to overcome the fire problem by forecasting the number of hotspots in Indonesia using intervention analysis. The intervention method is used to examine data that has increased in an extreme manner. There are two functions in the intervention method, namely step and pulse functions. This study uses pulse function intervention on hotspot occurrence data in Indonesia because the intervention is temporary and only occurs at a certain time. The results of this study showed that the intervention SARIMA model with order 𝑏 = 0, 𝑠 = 0, 𝑟 = 1 is a good model in forecasting the occurrence of hotspots in Indonesia with a MAPE value of 8.06%.Item SMALL AREA ESTIMATION DENGAN METODE EBLUP FAY-HERRIOT PADA TINGKAT KEMISKINAN DI PROVINSI SUMATERA UTARA(perpustakaan UR, 2021-07) Nainggolan, Damianus; Bustami, BustamiNational socio-economic survey in March 2019 showed that the number of poor people in North Sumatra Province was 1,282 million people or 8,83 percent of the total population. The number of poor people and the percentage of poor people are obtained by direct estimation based on the survey conducted. Direct estimation in a small area has a poor accuracy because the estimator have a large variance. The method that can be used to solve this problem is indirect estimation with Small Area Estimation (SAE). This study uses SAE with the Empirical Best Linear Unbiased Prediction (EBLUP) Fay-Herriot method to estimate the parameter of the poverty level in North Sumatra Province by city. The data used is the poverty percentage of 33 cities using seven co-variables namely the human development index, gross enrollment ratio for elementary, junior, high school, and college, income per capita, and life expectancy in North Sumatra Province obtained from the survey results from the BPS. This study shows that SAE with the EBLUP Fay-Herriot method produces a better estimation value than the direct estimation