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All Journal UNP Journal of Statistics and Data Science
Fajri Juli Rahman Nur Zendrato
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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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.
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.
Co-Authors Devi Yopita Sipayung Dila Sari Dwi Sulistiowati Hadid Habiburrahman Muhammad Arief Rivano Mulya, Nurul suci Tessy Octavia Mukhti Zamahsary Martha Zilrahmi, Zilrahmi