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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

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Forecasting Consumer Price Index in Personal Care Sector in Bukittinggi Using SVR with Grid Search and Radial Basis Function Kernel khairunnisa Pane; Fadhilah Fitri; Dina Fitria
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-iss3/373

Abstract

Inflation, measured by the Consumer Price Index (CPI), is vital for economic stability and policy making. In Bukittinggi, the Personal Care and Other Services sector shows notable CPI fluctuations, complicating accurate forecasting. This study uses Support Vector Regression (SVR) to predict monthly CPI data for this sector from 2020 to 2024. Data from Statistics Indonesia was normalized with Min-Max normalization to improve model accuracy and avoid scale distortion. Lag features were added to capture time dependencies, and data was split into training (80%) and testing (20%) sets. A linear SVR model was first applied but showed limited success due to the data’s non-linear nature. Therefore, the Radial Basis Function (RBF) kernel was used, with hyperparameters (C, sigma, epsilon, folds) optimized via Grid Search and cross-validation. The optimal settings (C=32, sigma=2, epsilon=0.1, k=10) yielded the lowest RMSE of 0.1099 in cross-validation and 0.0767 on testing. Results demonstrate that the RBF-SVR model effectively captures non-linear CPI patterns and outperforms the linear model. Evaluation metrics included RMSE, MSE, and MAE. The study concludes that SVR combined with Grid Search offers a robust forecasting method for sectors with complex CPI behavior, supporting local economic planning in Bukittinggi. Future research could investigate hybrid models and larger datasets to enhance prediction accuracy and adaptability to market changes.
Fuzzy Time Series Singh Method for Forecasting Tourist Arrivals at Kinantan Wildlife and Cultural Park Bukittinggi Olivin Adelia Huqmi; Fadhilah Fitri; Tessy Octavia Mukhti
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-iss1/376

Abstract

Tourism is a key sector in regional development, contributing to economic growth, job creation, and cultural preservation. In Bukittinggi, West Sumatra, the Kinantan Wildlife and Cultural Park (TMSBK) is a major tourist destination, known for its historical and educational value. Tourist visits to TMSBK show fluctuating trends influenced by seasonal factors, socio-economic conditions, and national or global events. These dynamics make accurate forecasting essential for effective tourism planning and management. This study aims to forecast monthly tourist visits to TMSBK using the Fuzzy Time Series (FTS) Singh method, which is suitable for uncertain and fluctuating time series data. The research used historical visitor data from 2021 to 2024 obtained from the Central Bureau of Statistics. The forecasting process included defining the universe of discourse, forming class intervals, fuzzifying historical data, establishing fuzzy logical relationships (FLR), and generating forecasts. The accuracy of the forecasts was measured using Mean Absolute Percentage Error (MAPE), with a result of 19.8%, indicating good predictive performance. The results show that the FTS Singh method successfully follows the fluctuation pattern of actual visitor data. This method provides valuable insights for destination managers in planning operations, promotional efforts, and service improvements. Therefore, the FTS Singh method can be considered a reliable tool to support sustainable tourism development and decision-making in Bukittinggi.
Comparison of Nadaraya-Watson Method with Local Polynomial in Modeling HDI and Poverty Relationship in Java Island Yoli Marda Novi; Fadhilah Fitri; Zamahsary Martha
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-iss3/380

Abstract

Poverty remains a critical issue in Indonesia, with the number of poor people reaching 24.06 million in September 2024. The Human Development Index (HDI), which indicates the level of human resource quality, is one of the factors influence poverty. This analysis focuses on the correlation involving HDI also this number of poor people in districts/cities in Java Island by comparing two kernel regresokesion methods, namely Nadaraya-Watson Estimator and Local Polynomial Estimator. Nonparametric regression was chosen thus it does not necessitate this presumption of a certain form of connection among variables, so it is more flexible in capturing complex relationship patterns. Secondary data from Statistics Indonesia (BPS) in 2024 was used in this study. Initial exploration shows, the data distribution does not have a clear pattern, so nonparametric methods are more suitable for use. Modeling is done using the optimal bandwidth obtained through the dpill function in R software. The analysis results show that the local polynomial estimator produces smoother regression curves and lower MSE values. In addition, comparison of different polynomial degrees shows that higher polynomial degrees tended to improve model performance. Among the tested polynomial degrees, the local polynomial with degree five (p=5) produced the lowest MSE value and the highest coefficient of determination. Therefore, the local polynomial estimator with degree 5 is the best method for modeling the relationship between the HDI and poverty levels in Java in 2024
Application of Fuzzy Time Series Cheng in Forecasting Bukittinggi's Consumer Price Index Afifah Nabilah; Fadhilah Fitri; Yenni Kurniawati
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-iss1/395

Abstract

The Consumer Price Index (CPI) is one of the main indicators used to measure inflation and assess the public’s purchasing power. Based on CPI monitoring in March 2025, Bukittinggi City recorded the highest year-on-year (y-o-y) inflation rate in West Sumatra at 0.50 percent, with a CPI of 106.99. This indicates significant price fluctuations, which require careful analysis and forecasting to support regional economic policymaking. This study aims to forecast the CPI of Bukittinggi City for April 2025 using the Fuzzy Time Series (FTS) Cheng method. The data used consists of monthly CPI values from January 2020 to March 2025, totaling 63 observations, obtained from the official website of Statistics Indonesia (BPS). The forecasting result using the FTS Cheng method for April 2025 shows a CPI value of 106.19. To evaluate the model's accuracy, the Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) were employed, yielding values of 0.82% and 0.90%, respectively. These values fall into the “very good” category based on standard forecasting accuracy criteria. The FTS Cheng method was selected due to its ability to accommodate data fluctuations and provide weighted relationships between fuzzy intervals, thus enhancing forecasting accuracy in dynamic economic conditions. However, this study is limited to univariate data and does not compare the FTS Cheng method with other forecasting models. This research provides valuable insights for local governments in designing effective economic strategies based on reliable predictive models.
Penerapan Partial Least Squares dan Pendekatan Robust dalam Analisis Diskriminan untuk Data Berdimensi Tinggi Rahmadina Adityana; Dodi Vionanda; Dony Permana; Fadhilah Fitri
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-iss3/396

Abstract

Classical discriminant analysis, namely linear discriminant analysis and quadratic discriminant analysis, is generally known to suffer from singularity problems when exprerienced with high-dimensional data and is not robust to outliers that make the data not multivariate normally distributed. This research focuses on investigating the classification performance of discriminant analysis on high-dimensional data by applying two approaches, namely the Partial Least Square (PLS) dimension reduction approach as a solution to high-dimensional data and a robust approach with the Minimum Covariance Determinant (MCD) estimator technique that is robust to outliers. The data used for this study is Lee Silverman Voice Treatment (LSVT) data. PLS forms five optimal latent variables that represent predictor variable information. Based on the assumption test of covariance homogeneity between groups, the test statistic value is greater than the chi-square table or the p-value is smaller than the significance level, which means that the assumption is unfulfilled, so quadratic discriminant analysis is applied. The evaluation results showed that the quadratic discriminant analysis analysis model with the MCD approach on the PLS transformed data was able to achieve 81% accuracy, 71% precision, 86% recall, and 77% F1-score. These values indicate that both approaches are able to maintain the efficiency of discriminant analysis classification performance on high-dimensional and multivariate non-normally distributed data.
Comparison of Kernel and Spline Nonparametric Regression (Case Study: Food Security Index of Jambi Province 2023) Rosa Salsabila Azarine; Septrina Kiki Arisandi; Fadhilah Fitri; Yenni Kurniawati
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-iss3/397

Abstract

Food security is one of the issues that plays an important role in national development, especially in regions with varying levels of economic welfare such as Jambi Province. One of the main factors affecting food security is food expenditure, which reflects the economic capacity of households to access food. The complex and non-linear relationship between Food Security Index (FSI) and Food Expenditure requires a flexible modeling approach in the analysis. This study aims to compare the performance of nonparametric regression Kernel ans Spline regression methods, namely the Nadaraya-Watson Estimator (NWE) and Local Polynomial Estimator (LPE) for Kernel Regression as well as Smoothing Spline and B-Spline for Spline Regression. The analysis was conducted using secondary data obtained from the Food Security and Vulnerability Map (FSVA) of 2023, with a total of 141 subdistricts in Jambi Province. The response variable is the Food Security Index (FSI), while the predictor variable is Food Expenditure. Model evaluation was conducted using the Mean Squared Error (MSE) and the coefficient of determination (R²). The results showed that the NWE method had the best performance with the smallest MSE value of 24.47690 and the highest R² value of 0.3332, meaning that approximately 33.32% of the variation in FSI could be explained by Food Expenditure. The LPE method showed nearly comparable performance, while Smoothing Spline and B-Spline exhibited higher prediction error rates. Therefore, the NWE method can be recommended as an effective nonparametric regression approach for modeling the relationship between food expenditure and food security.
Comparison of Nadaraya-Watson and Local Polynomial Methods in Analyzing the Relationship Between Consumer Price Index and Inflation in South Kalimantan Salwa Hifa Fadilah; Fadhilah Fitri; Fenni Kurnia Mutiya
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-iss3/401

Abstract

This study compares the performance of two nonparametric regression methods, namely Nadaraya-Watson and Local Polynomial, in analyzing the relationship between the Consumer Price Index (CPI) and inflation in South Kalimantan Province. Nonparametric approaches were chosen for their greater flexibility in capturing nonlinear relationships that conventional parametric models may fail to explain. The data were obtained from the Central Statistics Agency (BPS) for the period from January 2022 to December 2024, with missing values in the inflation variable handled through mean imputation. The optimal bandwidth was selected using the direct plug-in method (dpill).Visually, the Nadaraya-Watson method produced a more fluctuating curve that is highly sensitive to local variations, while the Local Polynomial method yielded a smoother and more stable curve. Quantitatively, the Local Polynomial method demonstrated better performance with lower MSE (0.1839), MAE (0.3507), and a higher R² (0.1811) compared to Nadaraya-Watson. These findings indicate that the Local Polynomial method is more effective in balancing curve flexibility and stability. This study also addresses a methodological gap by highlighting the relevance of nonparametric approaches in regional economic analysis. Future research is encouraged to explore alternative bandwidth selection methods and different kernel functions to improve estimation accuracy.
Comparison of Expectation-Maximization (EM) Algorithm and Kmeans for District/City Clustering in West Sumatera Province Based on Breadfruit Production Mayrita Addila Putri Mayrita; Fadhilah Fitri
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-iss3/403

Abstract

Breadfruit (Artocarpus altilis) is an important food source that is highly nutritious and plays a strategic role in West Sumatra Province. However, challenges such as pests, diseases and marketing constraints affect its cultivation and productivity. This study employed K-means and expectation-maximisation (EM) clustering methods to categorise regions according to their breadfruit cultivation characteristics. The elbow method identified three optimal clusters for K-means and seven for EM. Evaluating the quality of the clusters using the silhouette coefficient produced values of 0.47 and 0.37 for EM and K-Means respectively, indicating that EM produced tighter, more distinct clusters. These results suggest that EM is a more effective method for describing the variation in breadfruit production in West Sumatra. With this in mind, the research is expected to inform strategic decision-making aimed at increasing the productivity and added value of breadfruit crops in the area..
Panel Data Model Selection and Significant Determinants of New Family Planning Participants in West Sumatra Diah Triwulandari; Fadhilah Fitri
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-iss3/404

Abstract

Population issues in Indonesia are not limited to poverty, urbanization, population explosion, or high birth rates, but also include how small families can improve and maintain their quality of life. The main objective of the Family Planning program is to create happy and prosperous families with an ideal number of children. The West Sumatra Provincial Health Office report (2023) emphasizes that increasing the number of new family planning acceptors is an important priority to support the success of maternal, child, and family planning health programs, in line with the 2020–2024 RPJMN policy direction. Therefore, this study aims to develop the best panel data model and identify the factors that significantly influence the number of new family planning participants in West Sumatra Province. The secondary data used were obtained from the Statistics Indonesia (BPS) publication entitled West Sumatra Province in Figures from 2021 to 2024. The observation units in this study were 19 districts/cities in West Sumatra Province with a time series from 2020 to 2023. The results indicate that the best-selected model is the random effect model, with the number of couples of reproductive age proven to have a significant effect on the number of new family planning participants. The R-square value of 53.11% indicates that the model can explain 53.11% of the variation in the dependent variable, while the remaining 46.89% is influenced by other factors not included in the model.  
Logit and Complementary Log-Log Modeling in the Case of Factors Affecting Heart Failure Disease IGA MAWARNI; Asyifa Dwi Ayshah; Dhiyaa Fitri Yafe; 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-iss4/421

Abstract

Heart failure is one of the leading causes of morbidity and mortality globally. Heart disease is a disease caused by plaque that builds up in the coronary arteries that supply oxygen to the heart muscle. Research on heart failure disease aims to find out what factors affect heart failure disease and how much influence it has. This test was conducted using logistic regression method with logit modeling and complementary log-log modeling in analyzing data of 918 patients with heart failure disease. This study also takes which modeling is the best. The results of this analysis indicate that Age, Gender, Blood Sugar, and Chest Pain have significant effects on the likelihood of Heart Failure. Specifically, higher blood sugar levels and the presence of chest pain were found to increase the probability of heart failure, while gender and age showed varying effects across different age groups. Based on the model comparison, the Logit model demonstrated better fit and predictive accuracy than the Complementary Log-Log model, as reflected by its lower AIC value 897.43.
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