Article Per Year (5 Year)
p-Index From 2021 - 2026
4.41
P-Index
This Author published in this journals
All Journal Martabe : Jurnal Pengabdian Kepada Masyarakat Eksakta : Berkala Ilmiah Bidang MIPA Pelita Eksakta Journal of Mathematics UNP UNP Journal of Statistics and Data Science
Fadhilah Fitri
Universitas Negeri Padang
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

Published : 34 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 34 Documents
Search

 1   2   3   4 
Vector Error Correction Model to Analyze the Impact of Exchange Rates and Money Supply on Inflation in Indonesia Faulina; Fadhilah Fitri; Nonong Amalita; Admi Salma
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss3/188

Abstract

This study analyzes inflation in Indonesia in relation to the influence of exchange rates and the money supply (M2), which pose challenges in controlling inflation amidst rapid economic growth. Data from the Ministry of Trade of the Republic of Indonesia (Kemendag) were used to investigate the relationship between exchange rates and the money supply (M2) on inflation using the Vector Error Correction Model (VECM). The results indicate that in the short term, inflation tends to decrease towards stability, with a strong exchange rate capable of reducing inflation, while an increase in the money supply slightly raises inflation. However, in the long term, inflation demonstrates a strong self-correction mechanism, with the influence of exchange rates and the money supply becoming limited. This model proves effective in forecasting inflation for the period from March to August 2024, with a Mean Absolute Percentage Error (MAPE) of 19.59%.
K-Medoids Cluster Analysis for Grouping Provinces in Indonesia Based on Agricultural Households ST2023 Riska 01; Zamahsary Martha; Dony Permana; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss3/193

Abstract

Agriculture plays a crucial role in Indonesia's national development, providing essential resources such as raw materials, household income, and contributing significantly to Gross Domestik Product (GDP). According to the 2023 Agricultural Census (ST2023), there has been an increase in the number of Agricultural Household Enterprises (RTUP) across various agricultural subsectors. However, the welfare of agricultural entrepreneurs remains low, with 48.68% of poor household heads working in this sector. Therefore, an analysis is needed to understand the patterns and characteristics of RTUPs in each province. This study aims to cluster the provinces in Indonesia based on the number of Agricultural Household Enterprises (RTUP) using K-Medoids cluster analysis. K-Medoids, an extension of K-Means, was chosen for its ability to handle outliers by using medoids as cluster centers instead of means. The research utilized data from the 2023 Agricultural Census, covering 38 provinces and eight variables representing different agricultural subsectors. The optimal number of clusters was determined using the Elbow method, resulting in four distinct clusters. The findings revealed that Cluster 1 consists of 12 provinces with moderate RTUP numbers, Cluster 2 includes 23 provinces with low RTUP numbers, Cluster 3 comprises one province with high RTUP numbers, and Cluster 4 contains two provinces with very high RTUP numbers. The cluster validation using the Davies-Bouldin Index (DBI) yielded a value of 0.722, indicating that the clustering results are optimal.
Comparison Of Extreme Learning Machine And Holt Winter’s Exponential Smoothing Methods In Railway Passenger Forecasting Meil Sri Dian Azma; Dony Permana; Fadhilah Fitri; Atus Amadi Putra
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss3/211

Abstract

Forecasting the number of passengers on the Pariaman Express train is an activity that is considered to have the potential to help PT KAI in maximizing passenger service facilities and comfort. It is estimated that the number of train passengers in Indonesia will always increase along with the increasing population of Indonesia. The high interest of users of this mode of transportation can be seen from historical data that continues to increase every year. PT KAI (Persero) as a single train transportation provider company needs to have several strategies in providing and meeting passenger needs every day. In the study of forecasting the number of passengers on the Pariaman Express train using the Holt Winters exponential smoothing method and one of the artificial neural network methods, namely the extreme learning machine. The purpose of this study was to determine the comparison of the accuracy values ​​of the forecast results produced by the two methods, and to find out which method is good to use in this forecast. The data used is data on the number of Pariaman Express train passengers from 2021-2023. The results of the study show that the comparison of the accuracy values ​​of the forecasting of the number of train passengers shows that the Holt Winter's and ELM methods have error values ​​above 10%, meaning that the Holt Winter's and ELM methods are good at forecasting for 4 periods. Holt Winter's has a MAPE value of 17.10% and ELM has a MAPE value of 20%.
Estimation of Poverty in North Sumatera in 2022 using Truncated and Penalized Spline Regression Kurnia Andrea Diva; Fadhilah Fitri; Dony Permana; Admi Salma
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss4/217

Abstract

The Sustainable Development Goals' main goal is to reduce poverty (SDGs). Low human capital is the cause of poverty. The Human Development Index is one indicator that can be used to assess human capital (HDI). Despite having the largest population on the island of Sumatra, North Sumatra continues to have the fifth highest poverty rate. Because the pattern of the relationship between poverty and HDI based on previous research is still unclear because the results are inconsistent, nonparametric regression modeling was used in this study because it is flexible in following the pattern of data relationships and can avoid model prespecific errors. This study aims to compare the Spline Truncated and Penalized Spline regression methods. The results of the comparison between the Truncated Spline regression model and the P-Spline regression model by looking at the smallest MSE value showed that a better estimator for modeling the Human Development Index in North Sumatera in 2022 is non-parametric regression using the truncated spline estimaor. where the best truncated spline modeling is at order 2 with one knot point located at X = 66.93 with a GCV value of 6.0543.
Implementation of CART Method with SMOTE for Household Poverty Classification in Mentawai Islands 2023 Rheizma Dewi Adiningtiyas; Admi Salma; Syafriandi Syafriandi; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss4/232

Abstract

Poverty is a condition in which individuals or groups are unable to fulfill their basic needs due to economic pressure or limited resources. The Classification and Regression Trees (CART) method is a classification technique in the form of a classification tree, which describes the relationship between independent and dependent variables. Data imbalance can lead to low sensitivity values and area under curve (AUC) values. One method that can overcome unbalanced data is to perform Synthetic Minority Oversampling Technique (SMOTE). SMOTE is a technique with the addition of artificial data in the minority class at a stage before analyzing the data. The purpose of this research is to compare the model without and with SMOTE in CART method. The use of SMOTE is applied to balance the amount of data on each poor household. The accuracy value of the method without SMOTE is 89% while with the SMOTE method is 79%. However, the sensitivity value has increased by 80%. Meanwhile, the AUC value in the CART method with SMOTE increased by 31%. So in this study it can be concluded that CART classification analysis with SMOTE is able to provide better performance compared to CART classification analysis without SMOTE.
Early Marriage Factors Indonesian Using Spatial Regression Analysis yazid permana; Dina Fitria; Yenni Kurniawati; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss4/239

Abstract

Marriage is a sacred union recognized socially and religiously to form a family, as regulated by Law No. 16 of 2019. The percentage of early marriages in Indonesia continues to rise, reaching 21.5% in 2022, placing Indonesia 8th in the world according to UNICEF 2023 data. The increase in early marriages has significant impacts on maternal and child health and often leads to high divorce rates, with 516,334 cases in 2022. The aim of this research is to provide information and knowledge for students about early marriage and spatial regression. The main factors influencing early marriages are low education levels, economic difficulties, and environmental factors. Research shows that early marriages are highest in Kalimantan and Sulawesi, with spatial effects influencing the percentage of early marriages between regions.Spatial regression analysis, such as the Spatial Autoregressive (SAR) model, is used to examine the interactions between regions affecting early marriage. Spatial autocorrelation tests and spatial dependency effects show a spatial dependency effect, making the SAR model with queen contiguity weights the most suitable. The resulting model is considered quite good considering the R-squared value of 40.97%. The best-formed model shows that the Open Unemployment Rate (TPT) of youth is a significant variable that greatly impacts the percentage of early marriages. Therefore, the central and provincial governments are expected to pay more attention to the open youth unemployment factor to control and reduce the rate of early marriages in Indonesia.
Error Correction Model Approach for Analysis of Original Regional Income in West Sumatra Herlena Purnama Sari; Fadhilah Fitri; Nonong Amalita; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss1/332

Abstract

In this research, an error correction model approach is used, namely looking at long-term and short-termrelationships. Meanwhile, Original Regional Income (PAD) is all regional income originating from original regionaleconomic sources. Sources of Original Regional Income according to Law Number 33 of 2004 Chapter V Article 6consist of Regional Taxes, Regional Levies, Separated Regional Wealth Management Results and Other Legal PAD.because this approach uses long-term and short-term relationships, it is known that only variables x1 and x3 have along-term relationship and variables x1 and x3 have a short-term relationship. so it can be concluded that not allindependent variables have a connection with the dependent variable
Analisis Klaster K-means dalam mengelompokan Kabupaten/Kota di Provinsi Sumatera Barat Berdasarkan Jenis Kekerasan Terhadap Perempuan Tahun 2023 Latifah Jayatri Febiola; Fadhilah Fitri; Fenni Kunia Mutiya
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss1/344

Abstract

Violence against women is a serious social issue and a violation of human rights. Women are often vulnerable to violence, whether physical, psychological, or sexual, which negatively impacts their physical and mental health. To understand the distribution of violence cases against women in West Sumatra Province, an analytical method is needed to classify regions based on the number of reported cases. K-Means Clustering is one of the clustering analysis methods used to group districts/cities based on similarities in the number of violence cases. This study aims to classify districts/cities in West Sumatra based on the number of female violence victims using the K-Means Clustering algorithm. The optimal number of clusters was determined using the silhouette method, resulting in three clusters. Cluster 3 has the highest average number of physical and sexual violence cases, consisting of four districts/cities: Solok Regency, Lima Puluh Kota, Solok City, and Payakumbuh City. Cluster 2 represents areas with a moderate level of violence, dominated by psychological abuse, and consists of five districts/cities. Meanwhile, Cluster 1 comprises ten districts/cities with the lowest recorded violence cases. This classification provides insight into the regional distribution of violence against women in West Sumatra, identifying areas that require more attention. The findings suggest that the government should prioritize regions with high levels of violence through stricter law enforcement, the provision of support services for victims, gender equality campaigns, and increased awareness of women's rights
Perbandingan metode Double Moving Average(DMA) dan Double Exponential Smoothing (Brown) Terhadap Tingkat Pengangguran Terbuka (TPT) di Kota Padang Panjang. Nufhika Fishuri; Fadhilah Fitri; Dony Permana
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss2/366

Abstract

The Open Unemployment Rate (TPT) is the percentage of unemployed people in the total labor force. The population included in the labor force is the population aged 15 years and over who has a job but is temporarily not working. Unemployment occurs because of a mismatch between the demand for employment and the qualifications of job seekers. Many job vacancies require graduates with a diploma or degree, so unemployment is one of the problems faced by Padang Panjang City. To overcome TPT in Padang Panjang City, one of the needs is to do forecasting to see how the TPT rate will occur in the coming year. This research uses a forecasting method by comparing the Double Moving Average (DMA) and Double Exponential Smoothing (DES) forecasting values of the Unemployment Rate in Padang Panjang City from 2006 to 2023. This forecasting is done to provide insight into the future condition of the workforce in Padang Panjang City. The results of the forecasting indicate that in 2024, there will be an increase of 0.42%, and for the next 2 years, there will be a decrease
Nonparametric Regression with Local Polynomial Kernel on Relationship Between Schooling Years and Unemployment Rate in Banten Bunga Miftahul Barokah; Fadhilah Fitri; Chairina Wirdiastuti
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/372

Abstract

The Open Unemployment Rate (TPT) is a key indicator in assessing the economic performance of Banten Province. One of the factors suspected to influence TPT is education, which is measured by the average years of schooling. This study aims to analyze the relationship between the average years of schooling and TPT using the Local Polynomial Kernel Nonparametric Regression method for the period 2017–2024. This method was chosen for its flexibility in modeling nonlinear relationships without requiring strict assumptions about the data. The optimal bandwidth parameter for smoothing was determined using the Direct Plug-In (DPI) method through the dpill function in the R software. The results show that the nonparametric model has a coefficient of determination (R²) of 0.2841, which is higher than that of the Ordinary Least Squares (OLS) linear regression model, which only reached 0.1710. This indicates that the nonparametric approach is better at capturing the complex relationship between education and unemployment. However, the low R² values in both models indicate the presence of other factors that influence the unemployment rate, such as economic conditions, labor market structure, and education policy. Therefore, increasing the average years of schooling alone may not be sufficient to significantly reduce the unemployment rate. More comprehensive policies are needed, such as job skill enhancement, vocational training, and economic strategies focused on job creation. The findings of this study are expected to provide useful insights for policymakers in formulating more effective strategies to address unemployment in Banten Province.
Co-Authors Afifah Nabilah Asyifa Dwi Ayshah Atus Amadi Putra Bunga Miftahul Barokah Chairina Wirdiastuti Defri Ahmad Devni Prima Sari Dhiyaa Fitri Yafe Diah Triwulandari Dila Sari Dina Fitria Dodi Vionanda Dony Permana Faddiah Gusti Handayani Faulina Fedisha Elfiri Fedisha Fenni Kunia Mutiya Fenni Kurnia Mutiya FIGO RAHMATULLAH Fitri Mudia Sari Fridgo Tasman haniyathul husna Herlena Purnama Sari Iga Mawarni Inna Auliya Iut Triutami khairunnisa Pane Kurnia Andrea Diva Latifah Jayatri Febiola Maulani Meutia Rani Mayrita Addila Putri Mayrita Meil Sri Dian Azma Melda Safitri Musthafa Imran Nonong Amalita Nufhika Fishuri Nur Leli Nurul Fiskia Gamayanti Olivin Adelia Huqmi Rahmadina Adityana Rahmat Kurniawan Rheizma Dewi Adiningtiyas Rifani Rizki Amelia Riska 01 Rosa Salsabila Azarine Salma, Admi Salsabilla Khairani Salwa Hifa Fadilah Sarmilah Mila Septrina Kiki Arisandi Suherman Suherman Syafriandi Syafriandi Tessy Octavia Mukhti Tessy Octavia Mukhti yazid permana Yoli Marda Novi Yuki Fitria Zamahsary Martha Zilrahmi, Zilrahmi