Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Journal of Science and Technology
Fitri Rahmah Ul Hasanah
Universitas Andalas
Computer Science & IT Mathematics

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

Found 3 Documents
Search

 1 
Penerapan Model Regresi Logistik Terhadap Indeks Pembangunan Manusia (IPM) di Provinsi Sumatera Barat Tahun 2019 – 2021 Fitri Rahmah Ul Hasanah; Mutia Yollanda
Journal of Science and Technology Vol 2, No 2: September 2022
Publisher : UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/jostech.v2i2.4383

Abstract

The human development index is an indicator that can measure the success of a region in developing human quality. The success of human development can be measured by how fundamental problems in society can be overcome, such as poverty, unemployment, and lack of access to public facilities. The increase in HDI in an area can be determined by several factors, including life expectancy (UHH) and the unemployment rate. One of the models that can be used to determine the factors that significantly affect the human development index is logistic regression, where logistic regression is an approach to making a predictive model in the form of the probability of a variable.The data used in this study are HDI, UHH, and unemployment rates in West Sumatra Province in 2019–2021. Based on the multicollinearity test, there is no relationship between UHH and the unemployment rate. This study was conducted to determine the factors that significantly affect the human development index of districts/cities in West Sumatra Province. Based on the results obtained, UHH has dramatically affected the HDI of districts/cities in West Sumatra Province over the last three years.
Analisis Pengaruh Variabel Moneter Terhadap Perkembangan Ekonomi Negara ASEAN Mutia Yollanda; Fitri Rahmah Ul Hasanah
JOSTECH Journal of Science and Technology Vol 3, No 1: Maret 2023
Publisher : UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/jostech.v3i1.5741

Abstract

This study aims to determine the effect of inflation and exchange rates on ASEAN countries' gross domestic product (GDP). The data used is secondary data accessed through the world bank from 2019 to 2021, which consists of 30 data. The independent variables used in this data are exchange rates and inflation, while the dependent variable used is gross domestic product (GDP). The panel data regression used in this study includes the common effect model, the fixed effect model and the random effect model. Based on the Chow and Hausman tests conducted, the best model in this study was the random effect model (REM). The best equation in this research is given by the equation . his study uses robustness because the normality test on the classical assumption is not fulfilled. Based on the test results, the USD exchange rate and inflation variables have a significant effect on the GDP variable. While the inflation variable has no significant effect on the GDP variable.
Predictive Analysis Pendidikan Menggunakan Machine Learning di Sumatera Barat Fitri Rahmah Ul Hasanah; Muhammad Kivlan Reftreka Nugraha
JOSTECH Journal of Science and Technology Vol 3, No 1: Maret 2023
Publisher : UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/jostech.v3i1.5728

Abstract

This study uses big data, namely the socio-economic 2019 data. The focus of this research is Education. Education is an important factor in life, but in West Sumatra using the socio-economic 2019 data it is still in the low category and far from the national average. In this study will predict the factors that influence education in West Sumatra with the data classification method. Methods for classifying large amounts of data have been developed, including Machine Learning. Machine Learning is a field of technology that is currently being widely used to create algorithms with large data (big data). The machine learning method used is Naive Bayes and Bagging. Furthermore, the two models were tested and showed that the Naive Bayes model gave the best performance compared to the Bagging model based on the values of accuracy, sensitivity and specificity. So the Naive Bayes model is the best machine learning model for predicting the factors that affect education, namely household members, gender and regional classification.
Co-Authors Muhammad Kivlan Reftreka Nugraha Mutia Yollanda