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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 213 Documents
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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.
Analysis Analysis of The Influence of Job Resources and Leadership Quality on Job Satisfaction Using Structural Equation Modeling Azizah Apriyerni; Nisa Ulhusna; Rahmadani; Mira Meilisa
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-iss1/469

Abstract

Job Satisfaction is a essential factor influencing employee performance, commitment, and organizational sustainability. Low levels of Job Resources and suboptimal Leadership Quality are common causes of decreased job satisfaction across various institutions. This study aims to analyze the effect of job resources and leadership quality on Jjob Satisfaction using the Structural Equation Modeling (SEM) method. The research data were obtained from a Likert-scale survey (1-8) consisting of three latent variabless and their respective indicators, and wer analyzed through Confirmatory Factor Analysis (CFA) and Structural Model assesment. The result of the CFA indicate that all indicators meet the criteria for validity and reliability, with factor loadings above 0.50, a Composite Reliability (CR) value of 0.9667, and an Average Variance Extracted (AVE) value of 0.6769. the Goodness of Fit evaluation shows that the final model is highly acceptable, as reflected by a low Chi-square/df value, RMSEA = 0.005, and CFI, TLI, GFI, and NFI value of 1.000. the Structural analysis further demonstrates that Job Resources have a positive and significant impact on Job Satisfaction. Simultaneously, both variables contribute significantly to explaining variations in Job Satisfaction. This study highlights that enhancing Job Resources and improving Leadership Quality are crucial strategies to strengthen employee Job Satisfaction. The findings provide empirical insight that can assist organizations in developing more effective and sustainable human resource management policies

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