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All Journal Martabe : Jurnal Pengabdian Kepada Masyarakat Eksakta : Berkala Ilmiah Bidang MIPA Pelita Eksakta Journal of Mathematics UNP Rangkiang Mathematics Journal 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

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Penalized Spline Regression Modeling on the Human and Cultural Development Index (IPMK) for 2022 Sarmilah Mila; Fadhilah Fitri; Musthafa Imran
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/425

Abstract

Human and cultural development is a multidimensional phenomenon whose relationship with socioeconomic factors is often complex and nonlinear, making it challenging to model with conventional parametric approaches. This study aims to model the influence of socioeconomic variables on the Human and Cultural Development Index (IPMK) across 34 provinces in Indonesia in 2022 using the nonparametric Penalized Spline (P-spline) regression method within a Generalized Additive Model (GAM) framework. Secondary data from the Central Statistics Agency (BPS) were used, with predictor variables including School Participation Rate (APS), percentage of access to safe drinking water, Gini Ratio, per capita expenditure, average years of schooling (RLS), and open unemployment rate (TPT). Initial data exploration via scatterplots confirmed nonlinear relationship patterns between the predictor variables and IPMK. The best model was obtained using a first-order cubic spline with 10 knot points, selected based on the minimum Generalized Cross Validation (GCV) criterion. The modeling results demonstrated excellent performance, with an Adjusted R² value of 0.842 and a Deviance Explained of 92.3%. Significance analysis indicated that access to safe drinking water, per capita expenditure, average years of schooling, and the open unemployment rate significantly influence IPMK. Visual interpretation of the significant spline curves revealed informative relationship patterns, such as the diminishing returns effect of per capita expenditure. This study concludes that the P-spline approach is effective and interpretable for modeling complex nonlinear relationships in development data, providing a richer evidence base for policy formulation.
Analisis Pengaruh Penggunaan ChatGPT Terhadap Prestasi Akademik Mahasiswa Dengan Motivasi Sebagai Variabel Intervening Menggunakan Metode SEM-PLS Salsabilla Khairani; Yenni Kurniawati; Dony Permana; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/430

Abstract

This study aims to analyze the factors that influence student academic achievement through the use of ChatGPT using the Structural Equation Modeling (SEM) method based on the Partial Least Square (PLS) approach. In this study, three main factors were identified as elements that can influence the use of ChatGPT, namely knowledge about ChatGPT (PTC), willingness to use the technology (KUMT), and concerns that may arise (KYDT), as well as learning motivation as an intervening variable. The total sampling method was used in this study, where the entire population that met the criteria was designated as respondents. The research population included students in the Statistics Study Program at Padang State University in semesters 4–8 who had used ChatGPT for at least six months, with a total of 216 student respondents. Data were collected through a survey using an online questionnaire. Based on the analysis that has been carried out, the results of the study show that the variables of knowledge about ChatGPT (PTC) and willingness to use the technology (KUMT) have a significant positive effect on learning motivation, while concerns that may arise (KYDT) have no significant effect. Furthermore, only the variable of concerns that may arise (KYDT) had a significant direct effect on academic achievement, while the results of the mediation effect test showed that only the variable of willingness to use the technology (KUMT) had a significant indirect effect on academic achievement through learning motivation.
Memprediksi Nilai Ekspor Provinsi Sumatera Barat Menggunakan Metode Autoregressive Integrated Moving Average Faddiah Gusti Handayani; Fadhilah Fitri; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 4 No. 1 (2026): 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/vol4-iss1/445

Abstract

  The export sector in Indonesia is a key driver of national economic growth, particularly through increased foreign exchange earnings and regional development. West Sumatra is one of the provinces that notably contributes to the country's export performance due to its abundant natural resources. This research aims to forecast export values for the upcoming 16 months, spanning from September 2025 to December 2026. The study employs the ARIMA method, which is suitable for various time-series patterns, including those involving non-stationary data. Based on the analysis, the ARIMA (3,1,0) model is identified as the most suitable, achieving a MAPE of 3.90%. The forecast indicates a slight downturn from August to September 2025, followed by a steady upward trend through December 2026, reflecting a stable and positive export outlook. The findings of this research are expected to provide valuable insights for local governments and industry stakeholders in designing more effective export policies.
Analysis of the Open Unemployment Rate on Poverty in Java in 2024 Using Smoothing Spline Regression Nur Leli; Fadhilah Fitri; Nonong Amalita
UNP Journal of Statistics and Data Science Vol. 4 No. 1 (2026): 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/vol4-iss1/464

Abstract

Poverty and unemployment are two major issues in economic development that are interrelated and remain a serious concern in Indonesia. Java Island, as the center of economic activity and population in Indonesia, contributes relatively significantly to the national economy, but still faces issues of welfare inequality, including high unemployment rates in several regions and the persistence of people living below the poverty line. Therefore, analyzing the relationship between the Open Unemployment Rate and the Percentage of the Poor in Java Island is important to understand the socio-economic dynamics that occur. The analysis was carried out using the nonparametric regression method with a smoothing spline estimator. Based on the analysis results, an optimum model was obtained with a value of lambda of 0.04829734. The smoothing spline curve shows a negative relationship pattern, where an increase in the Open Unemployment Rate is followed by a decrease in the percentage of the poor. The Mean Square Error (MSE) value of 11.31277 indicates that the model has a relatively moderate level of prediction error and is able to represent the relationship pattern between variables quite well.
Enhancing Technology-Based Learning through Wordwall Application: A Case Study at SMAN 1 Ampek Angkek Tessy Octavia Mukhti; Fadhilah Fitri; Widia Kemala Sari
Pelita Eksakta Vol 8 No 1 (2025): Pelita Eksakta, Vol. 8, No. 1
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol8-iss01/260

Abstract

The explosive growth of technology necessitates educators staying current with the latest innovations, especially for technology-based learning in the sciences. However, at SMA Negeri 1 Ampek Angkek, it was discovered that several teachers have varying levels of technological proficiency, face resource constraints, and struggle to develop interactive learning materials. As a result, students often lose interest in learning. To solve this problem, a training session on using the Wordwall application, a web-based educational tool that includes interactive tools like puzzles, flashcards, and quizzes, was held. The workshop aimed to help teachers create more interactive and interesting teaching methods, as well as improve their ability to use educational technology. As a result, the training session improved the quality of teaching at SMA Negeri 1 Ampek Angkek, making the classroom atmosphere more dynamic and interesting for students.
Comparison of Linear Regression and Polynomial Local Regression in Modeling Prevalence of Stunting Fadhilah Fitri; Mawanda Almuhayar
Rangkiang Mathematics Journal Vol. 4 No. 1 (2025): Rangkiang Mathematics Journal
Publisher : Department of Mathematics, Universitas Negeri Padang (UNP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/rmj.v4i1.81

Abstract

Stunting is one of the main focuses of the government in Indonesia. This is because nutritional status is one of the benchmarks of community welfare. Stunting can be influenced by various societal aspects such as health, economy, social status, and education. One factor that is thought to be closely related to stunting is the level of education. Therefore, the prevalence of stunting and the level of education will be modeled; in this case, the mean years of schooling is used. Modeling uses two approaches: parametric through linear regression and nonparametric through local polynomial regression. This study compares both models to see which method better explains the stunting phenomenon. The comparison is made through the determination coefficient value or R2, Root Mean Square Error or RMSE, and the fitted curve plot. The results of R2 and RMSE for both models were obtained. The linear regression model has an R2 of 32.94% and an RMSE of 4.84. Meanwhile, for the local polynomial model, it is R2 43.44% and RMSE 4.32. Based on these results, it can be concluded that local polynomial regression is better at modeling the relationship between the prevalence of stunting and mean years of schooling in Indonesia. This finding confirms that the polynomial local regression method can capture phenomena that occur for data that do not follow a particular pattern.
Nonparametric Fourier Series Regression for Unemployment Analysis in Banten Province Bunga Miftahul Barokah; Fadhilah Fitri; Chairina Wirdiastuti
Rangkiang Mathematics Journal Vol. 5 No. 1 (2026): Rangkiang Mathematics Journal
Publisher : Department of Mathematics, Universitas Negeri Padang (UNP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/rmj.v5i1.90

Abstract

The Open Unemployment Rate (OUR) is a vital indicator of regional economic performance, particularly in Banten Province, which faces disparities in education and poverty. This study models the unemployment rate using two predictors: average years of schooling and poverty level, through a nonparametric Fourier series regression for the 2017–2024 period. This method provides greater flexibility in capturing the nonlinear and fluctuating patterns often observed in socio-economic data. The analysis used secondary data from Statistics Indonesia (BPS), beginning with descriptive statistics and data visualization. Models were evaluated using Generalized Cross-Validation (GCV) and the coefficient of determination (R²). The optimal model was found at K = 3, with a GCV of 2.4057 and an R² of 0.5155. The model effectively captured the non-linear relationships between unemployment, education, and poverty. Although the R² value is moderate, this indicates that including additional explanatory variables could enhance the model’s performance. These findings support the use of Fourier series regression as an alternative approach for labor market analysis, especially when linear methods fall short and provide insights for developing more targeted employment policies.
Co-Authors Afifah Nabilah Asyifa Dwi Ayshah Atus Amadi Putra Bunga Miftahul Barokah 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 Mawanda Almuhayar 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 Widia Kemala Sari yazid permana Yoli Marda Novi Yuki Fitria Zamahsary Martha Zilrahmi, Zilrahmi