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Tessy Octavia Mukhti
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LPPM Universitas Negeri Padang, Jalan Prof. Dr. Hamka, Air Tawar Barat, Kota Padang, Sumatera Barat 25131
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UNP Journal of Statistics and Data Science
Published by Universitas Negeri Padang
ISSN : -     EISSN : 2985475X     DOI : 10.24036/ujsds
Core Subject : Science, Education, Social,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences
UNP Journal of Statistics and Data Science is an open access journal (e-journal) launched in 2022 by Department of Statistics, Faculty of Science and Mathematics, Universitas Negeri Padang. UJSDS publishes scientific articles on various aspects related to Statistics, Data Science, and its application. Articles can be in the form of research results, case studies, or literature reviews. All papers were reviewed by peer reviewers consisting of experts and academicians across universities.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 11 Documents
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Search results for , issue "Vol. 4 No. 1 (2026): UNP Journal of Statistics and Data Science" : 11 Documents clear
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.
Forecasting Smallholder Oil Palm Yield in Riau Province through the SARIMA Approach Septrina Kiki Arisandi; Dony Permana; 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-iss1/436

Abstract

Oil palm stands as one of Indonesia’s major agricultural sectors that plays a vital role in regional economic growth, particularly within Riau Province. However, its production often fluctuates due to seasonal and environmental factors, making accurate forecasting essential for planning and policy formulation. This study aims to forecast smallholder oil palm production in Riau Province through the Seasonal Autoregressive Integrated Moving Average (SARIMA) Approach. The data consist of monthly oil palm production from January 2006 to December 2023 obtained from the Central Bureau of Statistics (BPS) of Riau Province. The modeling process includes identifying the model structure, estimating parameters, performing diagnostic checks, and evaluating forecasting accuracy using the Mean Absolute Percentage Error (MAPE). The best model selected was SARIMA (2,0,0)(0,1,1)[12] with an AIC value of 4980.12 and a MAPE of 11.27%, indicating a good level of accuracy. The model effectively captured both seasonal and long-term trend patterns in production. The forecast results suggest that peak production typically occurs in August–September, while the lowest occurs in February–March. The study concludes that the SARIMA model provides a robust statistical framework for predicting oil palm production and can be applied as a decision-support tool in agricultural and economic planning for the province
Forecasting the Consumer Price Index of Padang City in 2024 using the Autoregressive Integrated Moving Average Method Suci; Devi Yopita Sipayung; Dila Sari; Fajri Juli Rahman Nur Zendrato; Hadid Habiburrahman; Dwi Sulistiowati; Zilrahmi
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-iss1/437

Abstract

The Consumer Price Index (CPI), which changes, is influenced by fluctuations in the prices of goods and services in Padang City every year. This is triggered by various factors that are of primary concern to the government. This study uses the Autoregressive Integrated Moving Average (ARIMA) forecasting method to forecast CPI in 2024 by relying on monthly data on the Padang City CPI for the period 2020 to 2023 obtained from BPS. This analysis identifies the ARIMA model (0,2,1) as the best and most optimal model based on the AIC and BIC values, does not show any autocorrelation, and is normally distributed. The forecasting model used shows a smooth and stable increase in the CPI in the period from January to December 2024. This model provides a positive signal for people's purchasing power and economic stability in Padang City in 2024. The results obtained are expected to be used as a strategic tool for preparing future goods and services price planning with more precision.
Classification of Tuberculosis in Rumah Sakit Paru Sumatera Barat Using the C5.0 Algorithm Meliani Maya Sari; Zilrahmi; Dony Permana; Dwi Sulistiowati
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-iss1/444

Abstract

Tuberculosis (TB) remains a serious public health problem, including in West Sumatra Province, where the number of reported cases has continued to increase in recent years. Consequently, effective methods are required to support early detection and accurate classification of TB patients. This study aims to classify the tuberculosis status of patients at Rumah Sakit Paru Sumatera Barat by applying the C5.0 algorithm. The data used in this study consists of secondary data extracted from patient medical records collected from october to december 2024 with a total of 150 patient medical records. The dataset included eight predictor variables representing clinical symptoms and one target variable, namely sputum smear (BTA) examination results. The research process involved data preprocessing, after which the dataset was divided into training and testing subsets using a 70:30 ratio, a classification model was developed using the C5.0 algorithm, and its performance was evaluated using a confusion matrix. The findings indicate that the C5.0 algorithm achieved an accuracy of 91.11%, with a precision of 95.83%, sensitivity of 88.46%, and specificity of 94.74%. Night sweats were identified as the most influential variable in the construction of the decision tree. These findings indicate that the C5.0 algorithm demonstrates excellent performance and can be applied as a decision support method for classifying tuberculosis based on patients’ clinical symptoms
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 Ward Methods in Clustering Indonesian Provinces Based on Household Basic Service Access Mulya, Nurul; Fajri Juli Rahman Nur Zendrato; Muhammad Arief Rivano; Zamahsary Martha; 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-iss1/449

Abstract

Disparities in household basic service access across provinces in Indonesia remain a key issue in regional development. Basic services such as access to improved drinking water, proper sanitation, electricity, and adequate housing are essential indicators of household welfare, making regional classification necessary to identify similarities and disparities among provinces. This study aims to cluster Indonesian provinces based on household basic service access indicators and to compare the performance of the K-Means method and Hierarchical Clustering using the Ward approach. The analysis was conducted using numerical data with Euclidean distance as a measure of similarity. The optimal number of clusters was determined using the Silhouette plot and further validated using the Silhouette Coefficient. The results indicate that both K-Means and Ward methods produce two optimal clusters representing provinces with relatively high and relatively low levels of household basic service access. Centroid analysis reveals clear differences between clusters across all indicators, particularly in electricity access and sanitation. Furthermore, the evaluation of clustering quality shows that the Ward method yields a higher Silhouette Coefficient than the K-Means method, indicating more compact clusters and better separation between clusters. Therefore, the Ward method is considered more effective in mapping patterns of household basic service access across provinces. The findings of this study can support regional planning by providing a clearer understanding of disparities in household basic service access in Indonesia.
Stress Analysis of FMIPA UII Students in Practicum Report using Perceived Stress Scale and Robust Regression -, Abdullah Kafabih; Fahma Zuaf Zarir; -, Naufal Fahrezi; Edy Widodo
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-iss1/455

Abstract

Higher education requires students not only to master theoretical knowledge but also to apply concepts through practicum activities. At FMIPA UII, the preparation of practicum reports often becomes a source of pressure due to the large number of reports, tight deadlines, and the complexity of data analysis. This study aims to measure students’ stress levels during practicum report preparation using the PSS-10 and to analyze the effects of the number of reports, semester level, and organizational involvement. Primary data were collected from students of all study programs at FMIPA UII through a questionnaire survey. The analysis was conducted using Ordinary Least Squares (OLS) for assumption testing and subsequently robust regression (Huber M-estimation) due to the presence of heteroskedasticity and influential outliers. Descriptive results indicate an average PSS score of 17.95, categorized as moderate stress. However, the robust regression results show that the number of reports, semester level, and organizational involvement do not have a significant effect, either simultaneously or partially, on academic stress. These findings suggest that student stress is more likely influenced by other factors such as time management, coping strategies, social support, practicum design, and overall academic workload.
Analysis of the Open Unemployment Rate on Poverty in Java in 2024 Using Smoothing Spline Regression Leli, Nur; 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.
Cluster Analysis of Earthquakes on the Island of Sumatera in 2024 Using the DBSCAN Method Zahrani Asyati Zulika; 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-iss1/466

Abstract

Earthquakes are one of the most destructive and unpredictable natural disasters. Sumatera Island, being located along the Semangko Fault, typically experiences seismic movement due to contact between the Indo-Australian plate and the Eurasian plate. In this study, the DBSCAN method classifies earthquake incidents in Sumatera in 2024 into magnitude and depth categories. The data set, collected by the Meteorology, Climatology, and Geophysics Agency (BMKG), includes 163 earthquake events that occurred in Sumatera Island during 2024. The clustering process identified two main clusters: one representing deep earthquakes in inland areas and another consisting of shallow earthquakes along the western offshore region, near the megathrust zone. The Silhouette Coefficient was used to verify the clustering outcome, and the result was 0,58, which verifies a good formation of clusters. These findings provide insights into seismic patterns in Sumatera and can support disaster mitigation efforts.
Monthly Rainfall Forecasting in Pesisir Selatan Regency Using the Autoregressive Integrated Moving Average (ARIMA) Model Ulhusna, Nisa; Dwi, Sulistiowati; Fadhilah, Fitri
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-iss1/468

Abstract

Rainfall is a climate variable that plays a crucial role in agricultural planning, water resource management, and hydrometeorological disaster mitigation. Therefore, a forecasting method capable of adequately describing the temporal patterns of rainfall data is required. This study aims to forecast monthly rainfall in Pesisir Selatan Regency using the Autoregressive Integrated Moving Average (ARIMA) method. The data used in this study are monthly rainfall data for the period 2015–2024. The analysis stages include missing data imputation, Box–Cox transformation, stationarity testing using the Augmented Dickey–Fuller (ADF) test, model identification through ACF and PACF plots, parameter estimation, and model evaluation based on the Akaike Information Criterion (AIC), residual diagnostic tests, and forecasting accuracy using Mean Absolute Percentage Error (MAPE). The results show that the ARIMA(0,1,1) model is the best model, as indicated by the lowest AIC value and residuals that satisfy the white noise assumption. The forecasting accuracy evaluation yields a MAPE value of 55.05%, indicating that the model’s ability to capture monthly rainfall variability is still limited. Rainfall forecasting for the period January to December 2025 produces relatively constant forecast values, reflecting the limitations of the ARIMA(0,1,1) model in representing seasonal variations. Therefore, this model is more suitable as a baseline approach for rainfall forecasting in Pesisir Selatan Regency. Future studies are recommended to apply models that incorporate seasonal components or external variables to improve forecasting accuracy.

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