Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Parameter: Journal of Statistics
Nurfitra
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Analysis of The Effect of Life Expectancy (AHH) and Per Capita Expenditure on The Human Development Index (HDI) in Central Sulawesi Province in 2019 Sakinah, Nur; Ihlasia, Nurmasyita; Nurfitra; Sagap, Marni; Rachman, Rohis; Handayani, Lilies
Parameter: Journal of Statistics Vol. 2 No. 3 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i3.15373

Abstract

A measurement of a nation's human resource condition is the human development index (HDI). The three components of the human development index are living standards, often known as economics, and health. In Central Sulawesi Province in 2019, this study seeks to ascertain the impact of life expectancy (AHH) and per capita spending on the human development index (HDI). Secondary data from the Central Statistics Agency (BPS) of Central Sulawesi Province, corroborated by additional sources, was used in this study. The multiple linear regression analysis methods were the analysis technique used in this study.The findings demonstrated a positive and significant impact of partially variable Life Expectancy (AHH) and per capita spending variables on the Human Development Index (HDI). The Human Development Index (HDI) in Central Sulawesi Province is thereafter significantly impacted by the combination of the two independent factors in 2019.
Modeling of Poverty Level in Central Sulawesi Using Nonparametric Kernel Regression Analysis Approach Sakinah, Nur; Nurfitra; Ihlasia, Nurmasyita; Handayani, Lilies
Parameter: Journal of Statistics Vol. 2 No. 3 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i3.15743

Abstract

Poverty is defined as a person's inability to meet their basic needs. The level of poverty that exists can be used to assess the good or bad of a country's economy. The kernel regression method is used in this study to model the poverty rate in Central Sulawesi in 2020. According to the findings of this study, comparing poverty rate predictions for the Gaussian Kernel function and the Epanechnikov Kernel function with optimal bandwidth can be said to use different kernel functions with optimal bandwidth for each - each of these kernel functions will produce the same curve estimate. So, in kernel regression, the selection of the optimal bandwidth value is more important than the selection of the kernel function. Because of the use of various kernels functions with optimal bandwidth values results in almost the same curve estimation.
FORECASTING INDONESIAN ISLAMIC BANK (BSI) SHARE PRICES USING THE FUZZY TIME SERIES CHENG METHOD Nurfitra; Sofia, Ayu
Parameter: Journal of Statistics Vol. 3 No. 2 (2023)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2023.v3.i2.16920

Abstract

Shares were one of the most popular financial market instruments. In Indonesia, stock market activity continued to increase so that stock investment was in great demand by the public, especially in the banking sector. Indonesia had a majority Muslim population. Based on this, Indonesia had good potential in the field of Islamic finance, especially Islamic banking. One of the Islamic banks that had achieved positive performance was Bank Syariah Indonesia (BSI). BSI's stock price every day from February 1, 2021, to January 11, 2023, tended to experience a downward trend and fluctuated, making it difficult for investors to see the prospects of a company in the future. For this reason, a forecasting technique was needed. A good forecasting method used for data with trend patterns both down and up was Cheng's Fuzzy Time Series (FTS) method. So, this study used Cheng's FTS method to predict BSI's share price in the future. The calculation of the accuracy of the prediction results in this study used Mean Absolute Percentage Error (MAPE). The results showed that the forecasted value of BSI's share price for the period January 12, 2023, to January 31, 2023, was constant at 1,353.267 million with a MAPE value of 3.09%.
Co-Authors Ihlasia, Nurmasyita Lilies Handayani Nur Sakinah Rachman, Rohis Sagap, Marni Sofia, Ayu