Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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All Journal UNP Journal of Statistics and Data Science
Devi Yopita Sipayung
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Application of K-Modes Clustering Method to Identify Low Birth Weight Factors in Central Sulawesi Province Aprotama, Celsy; Yenni Kurniawati; Muhammad Arief Rivano; Devi Yopita Sipayung
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-iss2/357

Abstract

Low birth weight (LBW) has long-term effects on maternal and child health, with a high prevalence in Central Sulawesi Province. This study aims to identify factors influencing the occurrence of LBW in the region using the k-modes clustering method. The data used in this research is derived from the 2017 Indonesian Demographic and Health Survey. The analyzed variables include the husband's education level, miscarriage rate, maternal smoking habits, child's gender, husband's occupation, type of residence, and wealth index. The analysis revealed two distinct clusters. The first cluster mainly consisted of husbands with a secondary education level or equivalent to junior high school, working in the agricultural sector, residing in urban areas, and having a medium wealth index. In contrast, the second cluster was dominated by husbands with only primary education or equivalent to elementary school, living in rural areas, and having a very low wealth index. The findings of this study emphasize the need for comprehensive efforts to improve education, enhance environmental conditions, and expand healthcare access to reduce poverty and lower the incidence of LBW in Central Sulawesi. This research also contributes to initiatives aimed at improving maternal and child health in the region.
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.
Co-Authors Aprotama, Celsy Dila Sari Dwi Sulistiowati Fajri Juli Rahman Nur Zendrato Hadid Habiburrahman Muhammad Arief Rivano suci Yenni Kurniawati Zilrahmi, Zilrahmi