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Dina Fitria
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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.
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.
The Influence of Village-Owned Enterprises (BUMDes) on Community Welfare and Economic Literacy Dina Fitria; Manah Tarman
Journal of Practice Learning and Educational Development Vol. 5 No. 3 (2025): Journal of Practice Learning and Educational Development (JPLED)
Publisher : Global Action and Education for Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58737/jpled.v5i3.600

Abstract

This study aims to determine the influence of Village-Owned Enterprises (BUMDes) on the welfare of the community in Aeng Tabar Village, Tanjung Bumi District, Bangkalan Regency. BUMDes is a village economic institution that plays an important role in encouraging local economic growth through collective management of village potential by the village government and community. This research uses a quantitative approach with survey methods. The research sample consisted of 50 respondents from a total population of 1,434 people, taken using probability sampling. The data collection technique uses a closed questionnaire with a Likert scale. Data analysis was carried out using SPSS 23 with validity, reliability, normality, simple linear regression and t tests. The research results show that BUMDes has a significant influence on community welfare with a tcount value of 12.506 > ttable 2.010 and sig. 0.000 < 0.05. Thus, BUMDes management does not have a significant effect on community welfare. There is an increase in people's income, quality of education, health and access to basic services. In addition, the role of Economic Literacy is evident, as the community’s understanding of financial management, savings, investment, and utilization of local resources strengthens the positive impact of BUMDes programs. This research strengthens the theory of community empowerment, local economic development, institutions, social capital theory, and the importance of economic literacy in sustaining welfare improvement.
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