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All Journal Pelita Eksakta Journal of Mathematics UNP UNP Journal of Statistics and Data Science Mathematical Journal of Modelling and Forecasting Indonesian Journal of Statistics and Its Applications
Dina Fitria
Departemen Statistika Universitas Negeri Padang
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics 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.
Inflation Prediction In Indonesia Using Extreme Learning Machine and K-Fold Cross Validation Wahda Aulia Assara; Zamahsary Martha; Dony Permana; 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-iss3/412

Abstract

Inflation rate forecasting is an important aspect in supporting economic policies and price control by the government. This study aims to evaluate the performance of the Extreme Learning Machine (ELM) algorithm in forecasting the inflation rate in Indonesia and provide inflation prediction results for 2025. The data used is historical data on Indonesia's inflation rate for the period 2003–2024. The analysis process begins with data normalization to ensure a uniform scale, followed by data partitioning using 10-Fold Cross Validation. The ELM model was built with 30 hidden neurons, a sigmoid activation function, and a regularization parameter of 0.8. The test results show that the ELM algorithm has superior performance. This is evidenced by the average MAPE value of 1.71%, RMSE of 0.0359, and coefficient of determination (R²) of 0.9833, indicating very high accuracy. The inflation prediction for January to December 2025 is in the range of 1.517%–1.761%, with an average approaching 1.663%, indicating a relatively stable pattern throughout the year. Based on these results, the ELM algorithm can be used as an effective alternative method for forecasting time series data, particularly in the context of inflation. This research is expected to serve as a reference for the government in establishing inflation control policies and for other researchers interested in applying artificial intelligence models to economic analysis.
Modeling Infant Mortality in West Pasaman Regency With Negative Binomial Regression to Overcome Overdispersion Vinna Sulvia; Fitri Mudia Sari; Dina Fitria
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-iss4/424

Abstract

Infant mortality serves as a vital indicator of public health and an essential benchmark of development progress. Although the general trend shows a decline, several sub-districts in West Pasaman Regency continue to report relatively high infant mortality rates, raising concerns about the effectiveness of current health services. This study seeks to examine the determinants of infant mortality using count data regression models. The data were obtained from the publication West Pasaman Regency in Figures 2025 by Statistics Indonesia (BPS), consisting of one response variable, the number of infant deaths, and five independent variables: the percentage of Low Birth Weight (LBW), the proportion of deliveries assisted by medical personnel, the proportion of pregnant women enrolled in the K4 program, the number of health workers, and the number of health facilities. The initial analysis employed a Poisson regression model, which assumes equidispersion, but the results revealed evidence of overdispersion. To address this issue, negative binomial regression was adopted as an alternative approach. Model evaluation using the Akaike Information Criterion (AIC) and the Likelihood Ratio Test confirmed that the negative binomial regression provided a better fit than Poisson regression. The results indicate that the percentage of LBW and the number of health facilities significantly influence infant mortality. Low birth weight (LBW) had a positive association with infant mortality, consistent with theory, while the positive effect of health facilities differed from expectations, possibly due to issues of quality, distribution, or reverse causality. 
Comparison Performance of SARIMA and Exponential Smoothing Holt-Winter’s models for Forecasting turnover PT. Indah Logistik Cargo Padang Silvia Triana; Dina Fitria; Yenni Kurniawati; Admi Salma
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-iss4/432

Abstract

Forecasting is an important part of corporate decision making. With forecasting, companies can predict future conditions and demand so that they can make appropriate and strategic decisions. PT. Indah Logistik Cargo Padang's turnover data contains trend and seasonal elements that are forecasted using a time series model. This study was conducted to determine the best model for forecasting PT. Indah Logistik Cargo Padang's revenue in the coming period. The methods used in this study are the SARIMA method and Holt-Winter's Exponential Smoothing. The best model was obtained from the results of a comparative analysis of the two methods, as seen in the forecasting error rate determined by the mean absolute percentage error value. For forecasting the revenue of PT. Indah Logistik Cargo Padang, the best model used was SARIMA with a MAPE value of 3.9%.
K-Means Clustering of Jambi Province Based on Economic Growth in 2023 Fathina Nafisa Putri; Dina Fitria; Admi Salma
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-iss1/434

Abstract

  Economic growth describes a region’s economic condition. In Jambi Province, although recovery after the COVID-19 pandemic has been visible, gaps between districts and cities still exist due to income inequality, poverty, unemployment, and differences in human capital quality shown by the Human Development Index. This study aims to group districts/cities in Jambi Province based on economic growth and its determinants using the k-means clustering method. The analysis resulted in five clusters with distinct characteristics. Cluster 1, located in the central region, is characterized by relatively low economic growth and human capital, along with a high poverty rate. Cluster 2, covering areas in the western highlands and eastern region, shows strong human capital and a low poverty rate. Cluster 3, in the western part of the province, is marked by low poverty and unemployment rates. Cluster 4, situated in the northeastern coastal area, has the highest Gross Regional Domestic Product (GRDP) per capita and the lowest unemployment rate but struggles with a high poverty rate and weak human capital. Meanwhile, Cluster 5, representing the provincial capital area, demonstrates robust economic growth and strong human capital, although unemployment remains a key issue. These findings highlight the heterogeneity of regional conditions, suggesting that development policies must be tailored to each cluster to promote inclusive growth and equitable welfare.
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.
Comparison of K-Means and K-Medoids in Clustering Regency/City in West Sumatra Province Based on Environmental Indicators Silfi Robiati; Dina Fitria; Dodi Vionanda; Dwi Sulistiowati
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p191-201

Abstract

The Environmental Quality Index is an index that describes the condition of environmental management results nationally, and generalises from all regencies/cities and provinces in Indonesia. Although the Environmental Quality Index of West Sumatra Province has increased, there are still regencies/cities in West Sumatra Province have decreasing Environmental Quality Index. Therefore, it is necessary to conduct further analysis, one of which is to form a group of regencies/cities into a group according to their similarities or characteristics. This study aims to compare the K-Means and K-Medoids methods in grouping regencies/cities in West Sumatra Province based on environmental quality indicators in 2023. The data used in this research is secondary data, which is orginally the publication of Central Bureau of Statistics namely Sumatera Barat Dalam Angka in 2024. The research compares the K-Means cluster method and the K-Medoids cluster method. It concludes K-Means better than K-Medoids methods based on DB index with three clusters. First cluster has 12 regencies/cities with a high average air quality index, the second cluster has 6 regencies/cities that have small amounts of waste, and the third cluster has 1 city with a high average water quality index and land quality index, but a large amount of waste.   Keywords: Cluster, Comparison, Environmental, K-Means, K-Medoids
Classification of Rice Growth Phase Using Regression Logistic Multinomial Model and K-Nearest Neighbors Imputation on Satellite Data Fayyadh Ghaly; Yenni Kurniawati; Nonong Amalita; Dina Fitria
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p1-9

Abstract

One of the efforts made by the government to maintain food security is to provide statistical data on rice production through accurate calculation of harvest areas using the area sampling framework approach. Although area sampling framework surveys produce accurate estimates, the costs required are quite high when applying this method. To overcome this problem, one solution that can be applied is to utilize satellite imagery to monitor the greenness index of plants using the enhanced vegetation index. However, in real conditions, the Landsat-8 optical satellite is susceptible to cloud cover, which results in missing data. This study aims to model the phase of rice plants using the regression logistic multinomial model by utilizing Landsat-8 satellites and k-nearest neighbors imputation handling to overcome missing data. The results showed that the model had varying performance in each phase, with an average balanced accuracy of 66.45%. This figure shows that the model can classify the area sampling framework data imputed using the k-nearest neighbors imputation method well. The model shows optimal performance in the late vegetative and generative phases but is less effective in detecting the harvest, puso, and non-rice paddy phases.
Application of Algorithm Learning Vector Quantization for Air Quality Classification Roufsaldiaz Nawfal; Dina Fitria; Chairina Wirdiastuti
Mathematical Journal of Modelling and Forecasting Vol. 3 No. 2 (2025): December 2025
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v3i2.48

Abstract

This study aims to classify air quality using the Learning Vector Quantization (LVQ) algorithm based on the Air Quality and Pollution Assessment dataset obtained from Kaggle. The dataset comprises 5,000 observations, of which 4,000 were used for training and 1,000 for testing. The analytical process includes data preprocessing (normalization), the construction and training of the LVQ model, and performance evaluation using a confusion matrix. The experimental results demonstrate that the LVQ model successfully classified 903 of 1,000 test samples, yielding an overall accuracy of 90.3%. This level of accuracy indicates that the LVQ algorithm can capture relevant patterns in air quality variables and perform reliable classification across different air quality categories. The findings suggest that LVQ can serve as a potential foundation for developing automated air quality monitoring and decision-support systems. Future studies are encouraged to compare LVQ with other machine learning classification techniques to build a more optimal model and to gain deeper analytical insights.
Extended Cox Model for Analyzing Factors Influencing Time to First Employment After Graduation in West Sumatra M. Anfasa Prana Karil; Zilrahmi; Rita Diana; Tessy Octavia Mukhti; Dina Fitria; Retno Lis Megawati
UNP Journal of Statistics and Data Science Vol. 4 No. 3 (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-iss3/587

Abstract

The transition from education to employment has become one of the employment challenges in West Sumatra Province. This study aims to analyze the factors affecting the duration of obtaining a first job using the Extended Cox Proportional Hazard model. The data used were obtained from the August 2025 National Labor Force Survey (Sakernas) with variables including age, gender, educational attainment, regional classification, training, and work experience. The results show that age, educational attainment, and regional classification significantly affect the duration of obtaining a first job. Age has a positive effect that decreases over time, while higher educational attainment tends to increase job waiting time. Individuals living in rural areas tend to obtain jobs faster than those in urban areas. Meanwhile, gender and work experience are not significant, whereas training is significant through its interaction with time. Overall, the duration of obtaining a first job is influenced by individual factors, regional characteristics, and time-varying effects of the variables
Co-Authors anice kartika Asra Dinul Haq AULIA YUSWITA Chairina Wirdiastuti Defri Ahmad Dicha Putri Yeni Diva Aliyah Dodi Vionanda Dodi Vionanda Dony Permana Dwi Sulistiowati Dwika Larissa Faddiah Gusti Handayani Fadhilah Fitri Fadhilah Fitri Fakhri Kamil Fathina Nafisa Putri Fayyadh Ghaly Fitri Mudia Sari Fridgo Tasman Hanifah Nazhiroh khairunnisa Pane M. Anfasa Prana Karil M. Farel Rusde Putra Muhammad Fadlan Rafly Nonong Amalita Retno Lis Megawati Revina Rahmadani Rita Diana Ronald Rinaldo Roufsaldiaz Nawfal Rudi Anggara Salma, Admi Silfi Robiati Silvia Triana Sindy Amelia Putri SRI RAHAYU Suherman Suherman Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Vinna Sulvia Wahda Aulia Assara yazid permana Yenni Kurniawati Yuvani Oksarianti Zamahsary Martha Zilrahmi, Zilrahmi