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Dina Fitria
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Journal : UNP Journal of Statistics and Data Science

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Analysis of Factors Influencing the Number of Families at Risk of Stunting in Merangin Regency Using Mixed Geographically Weighted Regression Fadlan Rafly, Muhammad; Zilrahmi; Dony Permana; Dina Fitria
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-iss4/236

Abstract

The number of families at risk of stunting is among the significant concerns that have been a negative impact on developing superior human resources in Merangin Regency. The number of families at risk of stunting is sought to be solved by identifying the contributing components. MGWR is among the methods that may be employed to obtain a specific model that affects each obesrvasion location locally and a comprehensive model that is global. Multiple linear regression and GWR are used to create models MGWR used when data has the influence of spatial heterogeneity. This project aims to develop an MGWR model which will be used to calculate the amount families at risk of stunting in each sub-district in Merangin Regency who are at risk of stunting in 2022. A fixed gaussian kernel weighting matrix is used in MGWR modeling. At the very least CV of 0.6152241, A fixed gaussian kernel is utilized as the weighting function. The results indicate that the model obtained has an accuracy rate of 99.18%, which means that the predictor variables can explain the model by that percentage. Families with insufficient access to drinking water is one factor that significantly affects how many families are at risk of stunting, families with inadequate sanitation, maternal age less than 20 years and families with babies under five years old.
Early Marriage Factors Indonesian Using Spatial Regression Analysis permana, yazid; 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.
Classification of Poor Households in Padang City Using the Naïve Bayes Algorithm with Synthetic Minority Oversampling Technique kartika, anice; Dina Fitria; Syafriandi Syafriandi; Tessy Octavia Mukhti
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-iss4/241

Abstract

Poverty is a condition where a person is unable to meet minimum basic needs or a condition caused by the influence of development policies that have not been able to reach all levels of society. In Indonesia, the government has designed various programs to overcome poverty, but these programs are often not on target. One method to improve the effectiveness of the program is through proper classification of poor and non-poor households. This study uses the Naïve Bayes classification method which is popular in data mining to predict data categories based on the probability distribution of its features. However, challenges arise when the data is unbalanced between different classes. To overcome this, the Synthetic Minority Oversampling Technique (SMOTE) method is used to balance the data. Based on the analysis that has been carried out To determine the performance of Naïve Bayes using SMOTE and without SMOTE in classifying poor households in Padang City in 2023, classification using the Naïve Bayes method without SMOTE produced an accuracy value of 98%, precision of 0%, and recall of 0%. Meanwhile, the classification using the Naïve Bayes method with SMOTE produces an accuracy value of 90%, precision of 87%, and recall of 92% and the results of the criteria for poor households in Padang City in 2023 using Naïve Bayes can be seen from the results that the probability of poor households is much greater than that of non-poor households, therefore the data is classified as  group of households that are classified as poor.
Library Book Lending Recommendation Using Association Rules with Frequent Pattern Growth (FP-Growth) Algorithm Kamil, Fakhri; Dony Permana; Dodi Vionanda; Dina Fitria
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-iss4/284

Abstract

College libraries are libraries managed by higher education institutions such as university libraries. The library functions as an information center management forum for students which includes learning resource functions, access functions, librarian functions, ethical functions, and evaluation functions.  Students prefer to read through e-books rather than reading books or library collections. Limited knowledge of literature is the cause of students choosing to look for books on search engines rather than in the library. Managed book loan circulation history data will be able to improve library services that can assist in finding library collections. Book recommendation services using association rules, can find patterns of borrowing behavior of book titles that have the highest association as the most recommended titles to be borrowed together. The FP-Growth or Frequent Pattern Growth is an algorithm of associations rule that is able to generate association rules as personalized book borrowing recommendations. The results of book recommendations found as many as 50 rules that meet the chi-square assumption test where the recommendation items are independent. The results of 50 rules for book title choices that can be used by students as suggestions for determining books that have a relationship to be borrowed together to enrich references. For students who wish to borrow the books 'Professional Teacher: Mastering Teaching Methods and Skills' is recommended to also borrow the book 'Participatory Learning Methods and Techniques'. With the book recommendation service, the library provides advice to students in choosing related book titles to borrow at the library.
Perbandingan Metode Naïve Bayes Dan K-Nearest Neighbors Dalam Mengklasifikasikan Indeks Pembangunan Manusia Menurut Kabupaten/ Kota di Indonesia Tahun 2022 Anggara, Rudi; Tessy Octavia Mukhti; Yenni Kurniawati; Dina Fitria
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-iss4/319

Abstract

The Human Development Index (HDI) is an indicator used to measure the success of efforts to improve the quality of human life in a particular region. Indonesia's HDI has increased every year, but the HDI in several districts/cities in Indonesia remains in the low category. The low HDI in these districts/cities is due to unequal development between regions in Indonesia. This disparity in development is influenced by HDI indicators as well as other factors. To address this issue, a decision system is needed to determine HDI categories using the Naive Bayes and KNN methods. Naive Bayes is applied with the assumption of Gaussian distribution, while KNN is implemented with the optimization of the nearest K value. Model performance evaluation is conducted to determine the best accuracy of the two methods using a confusion matrix. The analysis results show that the Naïve Bayes model outperforms the KNN algorithm in classifying the Human Development Index (HDI) by district/city in Indonesia for the year 2022, with Naïve Bayes achieving an accuracy of 93%. Therefore, the Naïve Bayes algorithm show good performance in terms of accuracy.
Mapping Indonesian Provinces Based on Leading Plantation Commodities with Export Potential Using Multidimensional Scaling Analysis Putri Yeni, Dicha; Tessy Octavia Mukhti; Yenni Kurniawati; Dina Fitria
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-iss4/327

Abstract

Indonesia, as an agrarian country, benefits significantly from its plantation subsector, which contributes substantially to the national economy. However, the processing of plantation products in Indonesia remains largely limited to raw or semi-finished goods, resulting in low added value and restricted income for both farmers and the nation. This study aims to map Indonesia's provinces based on the production of key plantation commodities with high export potential, utilizing the Multidimensional Scaling (MDS) analysis method. The research focuses on commodities such as pepper, palm oil, coconut, rubber, coffee, cocoa, clove, and tea. It seeks to group 34 Indonesian provinces based on similarities in plantation production, providing valuable insights for policymakers to enhance production and increase export value. The analysis calculates inter-provincial similarities to determine distances between objects and evaluates the accuracy of the MDS mapping using STRESS and R2 values. The findings indicate that 12 provinces share similarities in cocoa production, while 7 provinces are closely aligned in the production of pepper, rubber, and coffee. Furthermore, 5 provinces exhibit similarities in palm oil production, and 9 provinces demonstrate commonalities in the production of coconut, clove, and tea. The analysis achieved a STRESS value of 0.024 (2.4%) and an R2 value of 0.9994, indicating that the MDS mapping is highly reliable. However, the results do not fully align with field data, suggesting the need for orthogonal transformation through Principal Component Analysis (PCA) to improve accuracy.
Peramalan Harga Bawang Merah di Kota Padang Menggunakan Metode SARIMA Larissa, Dwika; Fitri, Fadhilah; Dina Fitria
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-iss1/330

Abstract

The fluctuation of shallot prices in Padang City has become a major concern for consumers, producers, and the government. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) method to forecast shallot prices from January 2020 to August 2024, using monthly time-series data. The analysis identifies ARIMA(1,1,2)(0,1,1)12 as the optimal model for predicting shallot prices in Padang City, effectively capturing seasonal and non-seasonal patterns. Predictions for the period from September 2024 to August 2025 indicate a price increase trend, peaking in May 2025 before declining. The findings are expected to serve as a reference for planning production, distribution, and price control of shallots.
Classification of Factors Affecting Preeclampsia in Pregnant Women at RSUP. Dr. M. Djamil Padang using the CART Algorithm YUSWITA, AULIA; Dina Fitria; Dony Permana; Admi Salma
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-iss1/341

Abstract

Preeclampsia is a pregnancy-specific disease characterized by hypertension and proteinuria that occurs after 20 weeks of gestation. Preeclampsia itself is caused by various factors that can influence the occurrence of preeclampsia in pregnant women, including age, parity, history of hypertension, obesity, and kidney disorders. This study aims to determine the risk factors influencing preeclampsia based on preeclampsia diagnosis at RSUP Dr. M. Djamil Padang by classifying each variable using a decision tree. This research employs the CART (Classification and Regression Tree) algorithm. The CART algorithm has a binary nature and can analyze response variables that are either categorical or continuous, handle data with missing values, and produce an interpretable tree structure. The study results indicate that the primary risk factor for preeclampsia is parity. The model developed using the CART algorithm was tested using a confusion matrix, yielding an accuracy of 54%, a precision of 33.3% in correctly classifying patients with mild preeclampsia (PER), and a recall of 23.8% in classifying patients with severe preeclampsia (PEB).
Peramalan Total Nilai Ekspor Indonesia Menggunakan Metode Singular Spectrum Analisis Ronald Rinaldo; Yenni Kurniawati; Dony Permana; Dina Fitria
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-iss2/370

Abstract

Forecasting export data presents unique challenges due to seasonal fluctuations and complex global economic dynamics. Inaccurate forecasts may lead to misguided economic policies, particularly in the export sector, which plays a critical role in national economic growth. This study aims to forecast the total export value of two major sectors in Indonesia from January to December 2024 using the Singular Spectrum Analysis (SSA) method. Forecasting is essential in supporting economic policy planning and strategic decision-making. SSA is chosen for its ability to decompose time series data into interpretable components such as trend, seasonality, and noise. The forecasting model's performance is evaluated using the Mean Absolute Percentage Error (MAPE), which provides an intuitive accuracy interpretation in percentage terms. The optimal parameter for SSA was found at L=28L = 28L=28, yielding a MAPE of 16.63%, indicating good forecasting accuracy. The forecasted export values show that the highest export is expected in December 2024 (USD 39,578.67 million), and the lowest in January 2024 (USD 21,689.14 million). These findings suggest that SSA is effective in forecasting economic time series data, particularly Indonesia’s export values. This study contributes to the practical application of SSA in economics and serves as a reference for future research and policymakers in formulating export strategies.
Forecasting Consumer Price Index in Personal Care Sector in Bukittinggi Using SVR with Grid Search and Radial Basis Function Kernel Pane, khairunnisa; 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.
Co-Authors Adinda Dwi Putri Admi Salma Admi Salma Aldi Prajela Anggara, Rudi Atus Amadi Putra Belia Mailien Cindy Caterine Yolanda Defri Ahmad DHEA PUTRI RIZKIA Diva Aliyah Dodi Vionanda Dony Permana Elvina Catria Fadhilah Fitri Fadhillah Fitri Fadlan Rafly, Muhammad Fitri, Fadhilah Hefiani Mustika Hasanah Herman Ahmad Hidayatul Fikra Imam Akbar Indah Lestari Ishak Effendi Jimmi Darma putra Kamil, Fakhri kartika, anice Larissa, Dwika Lifia Zullani M. Helmi Manah Tarman Manja Danova Putri Maulana, Heru Moh. Wahyu Aminullah Muhammad Subhan Muhammad Tibri Syofyan Muhni Pamuji, Muhni Muthia Sakhdiah Nadha Ovella Syaqhasdy nazhiroh, hanifah Nonong Amalita Nurul Afifah Pane, khairunnisa permana, yazid Putra, M. Farel Rusde Putri Rivani Putri Yeni, Dicha Rama Denata Relis Stardo Retsya Lapiza Revina Rahmadani rios Rizqa Fajriaty Fitri MY Ronald Rinaldo Saddam Al Aziz Shavira Asysyifa S Sherly Amora Jofipasi SRI RAHAYU Suherman Suherman Suwanda Risky Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Velya Rahma Putri Vini Oktariani Wahda Aulia Assara Yenni Kurniawati Yenni Kurniawati Yudi Irwansi Yunistika Ilanda Yuslan Basir Yusmet Rizal YUSWITA, AULIA Yuwon Yuwon Zamahsary Martha Zilrahmi, Zilrahmi