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RAGAM: Journal of Statistics and Its Application
Published by Universitas Lambung Mangkurat
ISSN : -     EISSN : 29628539     DOI : https://doi.org/10.20527/ragam.vXXX
Core Subject : Economy, Humanities, Health, Science, Education,
Humanities Computer Science & IT Economics, Econometrics & Finance Mathematics Public Health
RAGAM Journal publishes scientific articles in the field of statistics and its applications, including: * Biostatistics * Parametric and nonparametric statistics * Quality control * Econometrics and business * Industrial statistics * Time series analysis * Spatial statistics * Data mining * Computational statistics * Applications of statistics in the medical, economic, social, environmental, industrial, technological, and other related fields
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 64 Documents
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A COMPARATIVE STUDY OF K-MEANS AND SINGLE LINKAGE FOR REGIONAL CLUSTERING IN WEST KALIMANTAN IN 2024 BASED ON EDUCATIONAL INDICATORS Ridho Alamsyah; Andina Putri Maharani; Dwi Vika Mutia; Sofiah Nur Alifah; Sofie Malika; Muhammad Riefky
RAGAM: Journal of Statistics & Its Application Vol 5, No 2 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i2.19477

Abstract

Education is a strategic investment in human resource development, as evidenced by several indicators, including the Average Years of Schooling (AYS), Expected Years of Schooling (EYS), Net Enrolment Rate (NER), and Gross Enrolment Ratio (GER). West Kalimantan Province faces the challenge of providing equal educational quality across regions. This issue has led to a low Human Development Index (HDI), ranking 30th of 34 provinces in Indonesia in 2024. These regional differences make it hard for the government to decide on the right policy actions. This study aims to group districts and cities in West Kalimantan based on their education quality indicators. It also compares the performance of two clustering methods, K-Means and Single Linkage, to find out which one produces the best results. The data used is secondary data from 2024, sourced from BPS West Kalimantan Province. The analysis included descriptive statistics, a data adequacy test, a multicollinearity test, and a cluster quality evaluation using the Elbow method based on Within-Cluster Sum of Squares (WCSS). At three clusters, K-Means produces a WCSS value of 26.6143, while Single Linkage produces a WCSS value of 33.7651. Therefore, K-Means with three clusters is selected as the optimal clustering method because it produces a lower WCSS value than Single Linkage. The K-Means clustering results consist of three groups with different characteristics based on the four education variables used in the study. These results can support local governments in formulating education policies tailored to the characteristics of each cluster.Keywords:   Clustering,  Education,  K-Means
MODELING FACTORS THAT INFLUENCE POVERTY LEVEL IN SUMATERA USING SPATIAL REGRESSION MODELS Yekti Widyaningsih; Deandra Sakirah Putri
RAGAM: Journal of Statistics & Its Application Vol 5, No 2 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i2.18241

Abstract

Poverty is a condition of a person's inability to fulfill basic needs such as food, clothing, shelter, school and health. Poverty is still a major unresolved problem in Indonesia, including the island of Sumatra. Based on BPS data in 2022, the number of poor people in Sumatera is the second after Java. This research was conducted with the aim of analyzing the factors that explain the level of poverty in Sumatera. The method that will be used is spatial regression modeling, including the General Nested Spatial Model (GNSM), Spatial Autoregressive Model (SAR), Spatial Error Model (SEM) and Spatial Autoregressive and Moving Average Model (SARMA). The dependent variable is Poverty Level (Miskin) and the independent variables include Population Density (KP), Average Years of Schooling (RLS), Human Development Index (IPM), Open Unemployment Rate (TPT) and Regency Minimum Wage (UMK). Based on the result of this study, the Spatial Autoregressive and Moving Average Model is the best model, with the smallest AIC value, namely 305.32. The only factor that has a significant influence on the poverty level is the Human Development Index (IPM).
A COMPARATIVE ANALYSIS OF BAYESIAN AND FREQUENTIST METHODS FOR INDEPENDENT K-SAMPLE TESTING Pardomuan Robinson Sihombing
RAGAM: Journal of Statistics & Its Application Vol 5, No 2 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i2.19876

Abstract

Regional socioeconomic inequality remains a critical development challenge in Indonesia, with significant variations observed in Sumatra, Java-Bali, and other regions. This study conducted a comprehensive methodological comparison between Bayesian and frequentist approaches to test the average differences among three independent regional groups using five economic and social indicators from 2024-2025: Human Development Index (HDI), Gini ratio, poverty rate, unemployment rate, and economic growth. The authors evaluated Bayesian ANOVA against parametric ANOVA F and four nonparametric alternatives, including the Kruskal-Wallis test, permutation test, and median test, by assessing its performance under different assumption violations. Statistical analysis showed that HDI and Gini ratios showed significant regional differences across all methods, while poverty rates showed method-dependent outcomes, and unemployment and economic growth showed no significant regional differences. These findings demonstrate the importance of method selection based on assumption testing and highlight the advantages of the Bayesian approach for regional analysis with small samples. Policymakers should prioritize targeted interventions for disadvantaged regions while addressing income inequality in more developed regions.
IMPLEMENTATION OF THE MULTILINGUAL SENTIMENT METHOD IN PUBLIC SENTIMENT PERSPECTIVES TOWARD EDUCATION POLICY DIRECTIONS IN INDONESIA Fajar Widianto
RAGAM: Journal of Statistics & Its Application Vol 5, No 2 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i2.18091

Abstract

The current development of information technology has increased the dynamics of public opinion regarding the direction of education policy in Indonesia, which is spread across various digital platforms such as social media and news portals. The linguistic diversity used by the public, including Indonesian, regional languages, and mixed foreign languages, poses a challenge for conventional sentiment analysis, which is generally monolingual. This study aims to implement the Multilingual Sentiment Method to analyze and map public sentiment perspectives toward education policies in Indonesia more comprehensively and accurately. The research methodology encompasses data collection utilizing web scraping and APIs, data preprocessing (cleaning, case folding, tokenization, stopword removal, and normalization), automatic labeling, and the application of Natural Language Processing (NLP) using multilingual pre-trained models. Model performance evaluation is conducted using accuracy, distribution, precision, and prediction metrics. Data visualization analysis is also performed to illustrate sentiment distribution and emerging issue trends. The results indicate that the multilingual approach successfully improves classification performance compared to monolingual methods, particularly in detecting sentiments in mixed-language texts. The distribution of public sentiment is dominated by neutral and positive categories, whereas negative sentiments emerge regarding issues of technical implementation and equitable access to education. In conclusion, the implementation of the Multilingual Sentiment Method is effective in providing real-time mapping of public opinion, thereby supporting the formulation of education policies that are more responsive and data-driven.

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