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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 50 Documents
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ANALISIS DETERMINAN KEJADIAN ISPA PADA BAYI DI KABUPATEN HULU SUNGAI SELATAN MENGGUNAKAN REGRESI LOGISTIK BINER Nur Azmi Khairinda; Dewi Anggraini; Meitria Syahadatina Noor
RAGAM: Journal of Statistics & Its Application Vol 2, No 1 (2023): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v2i1.10054

Abstract

The World Health Organization (WHO 2012) states that ISPA is the main disease that causes death in infants and occupies the first position in the number of illnesses in baby’s. Based on data from the South Kalimantan Provincial Health Office, Hulu Sungai Selatan District is the region with the highest infant mortality rate due to ISPA out of 13 districts/cities in South Kalimantan. This study aims to determine the factors that influence the incidence of ISPA in Hulu Sungai Selatan District using binary logistic regression analysis. Based on the results of binary logistic regression testing, it is known that nutritional status affects the incidence of ISPA in baby’s. Meanwhile, age, sex, immunization status and baby's weight at birth had no effect on the incidence of ISPA in baby’s in Hulu Sungai Selatan District. Therefore, there is a need for counseling for parents who have baby’s regarding protection from exposure to ISPA, especially for toddlers who are at risk of getting ARI, such as baby’s under one year old.Keywords: ISPA Incidence, Nutritional Status, Binary Logistic Regression
ANALISIS TINGKAT KEPUASAN PASIEN TERHADAP KEPUTUSAN PINDAH KELAS PERAWATAN PASIEN BPJS Aldianur Khairani; Dewi Anggraini; Dwidjo Susilo
RAGAM: Journal of Statistics & Its Application Vol 2, No 1 (2023): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v2i1.7652

Abstract

This study aims to analyze the effect of patient satisfaction with hospital services on the decision to move BPJS patient care classes at Ulin Hospital, Banjarmasin. The data collection technique used is quantitative, namely distributing questionnaires to BPJS patients. Participants in this study amounted to 75 people. Data analysis used ordinal logistic regression analysis. The level of patient satisfaction is measured using 4 factors, namely Tangible, Reliability, Assurance, and Empathy. The results showed that the patient satisfaction factors that had a significant effect were Assurance and Empathy. Overall, these two variables influenced the satisfaction assessment of patients treated at Ulin Hospital, Banjarmasin, by 27.5%.
PEMETAAN PERSEBARAN PENDERITA DEMAM BERDARAH DENGUE MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS DI KABUPATEN BANTUL TAHUN 2024 Andhy Sulistyo
RAGAM: Journal of Statistics & Its Application Vol 4, No 2 (2025): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v4i2.16808

Abstract

AbstractBackground: Dengue Hemorrhagic Fever (DHF) remains a major public health problem with fluctuating incidence in Indonesia, including Bantul District. In 2024, 672 DHF cases were reported, a sharp increase compared to 135 cases in the previous year. This situation highlights the need for spatial analysis to better understand the disease distribution and support effective control strategies.Objective: This study aims to map the distribution of DHF cases in Bantul District in 2024 and analyze spatial patterns using Geographic Information Systems (GIS).Methods: A descriptive quantitative study with a case study approach was conducted. The population consisted of all reported DHF cases in 2024 obtained from the Bantul District Health Office, including patient residential coordinates. Spatial analysis was performed using ArcGIS 10.3. Global Moran’s I was applied to assess spatial autocorrelation, the Average Nearest Neighbor (ANN) method was used to determine distribution patterns, and the Central Feature tool identified the central point of case concentration.Results: The analysis revealed that DHF cases were significantly clustered, as indicated by a positive Moran’s I value (p<0.05) and an ANN index <1. The central feature analysis showed that case concentrations were mainly located in the working areas of Pleret and Imogiri II primary health centers.Conclusion: The distribution of DHF cases in Bantul District in 2024 is not random but clustered in specific areas. These findings provide crucial evidence for prioritizing targeted interventions such as fogging, community education, and more focused source reduction programs. Keywords: DHF,GIS,ANN, Moran’s I,Bantul
PERMODELAN REGRESI NONPARAMETRIK SPLINE TERHADAP INFLASI DI PROVINSI KALIMANTAN SELATAN Geofani Setiawan; Fuad Muhajirin Farid; Nur Salam
RAGAM: Journal of Statistics & Its Application Vol 1, No 1 (2022): RAGAM: Journal of Statistics and Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v1i1.7337

Abstract

Inflation is a condition of increasing prices continuously for a certain time. One of the factors thought to influence inflation, namely the Consumer Price Index (CPI), the Consumer Price Index (CPI) is an indicator that can be said to be important in determining the level of economic stability of a country. Seeing the relationship between the Consumer Price Index (CPI) and inflation, this study aims to explain how the influence and how the best model of the Consumer Price Index (CPI) on inflation in South Kalimantan Province uses Spline Nonparametric Regression. The use of the Spline Nonparametric Regression method in this study is because the data used has significant fluctuations so that it is estimated that the data is not normal. In the process, the Spline Nonparamteric Regression method is used to obtain the estimated regression curve through a data fitting approach. This method is also very suitable for use with data that changes frequently, spline is a model that has statistical, visual interpretation and has the ability to be generalized to complex and complex statistical models. The result of this research is that the best model is found at one knot point and the Consumer Price Index (CPI) has an effect on the inflation variable by 13.23 percent.Keywords:  Inflation, Consumer Price Index, Spline Nonparametric Regression 
ANALISIS PENGARUH INDUSTRI MIKRO DAN KECIL TERHADAP PERTUMBUHAN EKONOMI DI INDONESIA DENGAN PENDEKATAN EKONOMETRIKA REGRESI SPASIAL DATA PANEL Jonathan Adi Winata; Fuad Muhajirin Farid; Selvi Annisa
RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v3i1.12799

Abstract

AbstractOne indicator to assess the economic condition of a country is Gross Domestic Product (GDP) at the national level or Gross Regional Domestic Product (GRDP) at the regional level. The sector that contributes the most to Indonesia's GDP is the manufacturing industry. One of the most crucial components within the manufacturing sector is the micro and small-scale industry (MSI). The presence of MSIs significantly contributes to economic development, closely tied to the geographical location among regions, thereby exerting spatial influence on the GRDP of a region. Hence, an analysis of GRDP considering spatial aspects is necessary, investigating the impact of the Micro and Small-scale Industry (MSI) sector on economic growth in Indonesia using spatial panel data regression. The spatial models constructed in this study include the Spatial Autoregressive Model (SAR) and Spatial Error Model (SEM) involving fixed-effect influence. This research aims to describe and identify the factors within MSIs that influence economic growth in each province of Indonesia. The results indicate that the appropriate model used is the Spatial Autoregressive Model Fixed Effect (SAR-FE). Overall, there are two independent variables significantly affecting economic growth, namely the number of micro and small-scale industries (X1) and inflation (X6). The results show that an increase in the percentage of these two variables will decrease the economic growth rate. Keywords:   Gross Regional Domestic Product, Economic Growth, Micro and Small Industries, Spatial Autoregressive Model Fixed Effect 
PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA DATA INDEKS HARGA KONSUMEN (IHK) 5 IBUKOTA PROVINSI DI PULAU KALIMANTAN Muhammad Aldi Relawanto; Yuana Sukmawaty; Dewi Sri Susanti
RAGAM: Journal of Statistics & Its Application Vol 2, No 2 (2023): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v2i2.11427

Abstract

Generalized Space Time Autoregressive (GSTAR) model is a development model from the generalized STAR (Space Time Autoregressive) model. GSTAR model have autoregressive order to see the effect of the time element and location weighting matrix to see the effect of the location element. Unlike the STAR model, it can assume that each location research has different characteristics. The purpose of this research is to apply the Generalized Space Time Autoregressive (GSTAR) model to the Consumer Price Index (CPI) data in Kalimantan Island, especially in the capital city of each province in Kalimantan Island to find out the best estimation model with the best location weight. The location weights used the distance inverse location weights and the normalized cross-correlation location weights by estimating the parameters of the GSTAR model using the Ordinary Least Square (OLS) method. The best estimated model can be seen from the smallest Akaikae’s Information Criterion (AIC) and Root Mean Square Error (RMSE) value. From the research results, it was found that the best GSTAR prediction model for CPI data for 5 cities in Kalimantan Island was the GSTAR(1,1)-I(1). These results are based on the GSTAR prediction model with the smallest AIC value and the data is differencing 1 time. The best location weight based on the smallest RMSE value for the GSTAR(1,1)-I(1) model is the normalized cross-correlation location weight.
KEHIDUPAN SOSIAL MASYARAKAT PASCA PANDEMI COVID-19 DI DESA HATUNGUN KECAMATAN HATUNGUN KABUPATEN TAPIN Kurnia Oktaviani; Fuad Muhajirin Farid; Syahrial Shaddiq
RAGAM: Journal of Statistics & Its Application Vol 2, No 1 (2023): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v2i1.10030

Abstract

COVID-19 is a virus that can be transmitted to everyone caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The World Health Organization (WHO) has officially declared COVID-19 a pandemic because the spread of this virus is very fast. The COVID-19 pandemic itself has been going on for more than 2 years and has had a real impact on the people of Indonesia. The three main impacts that are felt by the community are social interaction between communities, community economic activities and people's lifestyles. This study aims to find out and understand social interaction between communities, community economic activities and the lifestyle of the people in Hatungun Village during the post-COVID-19 pandemic. Data collection techniques used in this study using observation, interviews and documentation. The data analysis itself uses qualitative descriptive statistics. The results obtained state that there are no obstacles in social interaction between communities during the post-COVID-19 pandemic, in economic activity there are constraints that are still being felt today as a result of the paralysis of various sectors, especially in the economic sector which causes the price of basic commodities to increase while income decreases and spending online there is nothing to change a person's lifestyle if the person is wise in reacting to it.Keywords:    Social Society, COVID-19 Pandemic, Tapin District.
PETA KENDALI MULTIVARIAT np irma irma; Dewi Anggraini; Fuad Muhajirin Farid
RAGAM: Journal of Statistics & Its Application Vol 1, No 1 (2022): RAGAM: Journal of Statistics and Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v1i1.7387

Abstract

In general, a production process requires the control of multiple quality characteristics (variables). Multivariate control charts can be used to control many quality attributes and identify the root cause of an out-of-control condition or signal. It was used in the production of Hexagon Bolt M16x75mm. However, research regarding the theory of multivariate control charts remains limited. The np multivariate control chart is obtained by combining the processes of parameter estimate, control limit determination, and out-of-control signal detection.  Keywords:  Control Charts, Multivariate np, Out of Conrol Signal.
ANALISIS REGRESI ROBUST M ESTIMATOR UNTUK MENGETAHUI FAKTOR YANG MEMPENGARUHI LAMA STUDI MAHASISWA S1 STATISTIKA FMIPA UNIVERSITAS LAMBUNG MANGKURAT Widawati Annisa Putri; Fuad Muhajirin Farid; Selvi Annisa
RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v3i1.12798

Abstract

Robust regression is a statistical technique commonly used to model relationships between variables by minimizing the impact of outlier data. The use of robust regression M Estimator works well when there are outliers in the data. In this study, robust regression M estimator analysis will be applied to student study period data. The aim of this research is to determine the significant factors influencing the study period of Statistics undergraduate students at the Faculty of Mathematics and Natural Sciences, Lambung Mangkurat University. The results of the research show that the residual data characteristics are not normal and there are outliers in the data. Using the Robust Regression M Estimator, the F test results show that F calculated 6.2492 > F table 2.173112, which means rejecting H0, indicating that the independent variables collectively have a significant effect on the dependent variable. From the t-test, it is known that the Guidance Process for students while working on their final project, the Employment Status of students, and the GPA of students significantly affect the Study Period of students. Keywords:   Robust Regression M Estimator, Study Period of Students, ULM
ANALISIS INFLASI DI INDONESIA: PEMODELAN ARIMA DAN IMPLIKASI KEBIJAKAN EKONOMI ALFISYAHRINA HAPSERY; Artanti Indrasetianingsih; Rabiatul Adawiyah
RAGAM: Journal of Statistics & Its Application Vol 4, No 1 (2025): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v4i1.14570

Abstract

One of the important indicators on a country's economy is inflation. Inflation is an increase in the price of goods and services in general and continuously over a certain period of time. Various studies have been conducted to predict inflation, both using conventional methods and those using artificial intelligence. This research uses the ARIMA method specifically to help the government in monitoring fiscal and monetary policies so that they are more responsive to the threat of inflation, such as interest rate adjustments or basic commodity price policies. The main objective of this study is to obtain a model that can be used to predict inflation in Indonesia with a high degree of accuracy. The results of the descriptive analysis show that the highest inflation in Indonesia occurred during the monetary crisis, namely in February 1998, which was 12.76, while the highest average inflation occurred in 1998 at 4.818. ARIMA modeling for inflation results in an ARIMA([1,3,5,8,48],0,0) model with an outlier , the model satisfies the residual white noise assumption, but does not meet the normally distributed residual assumption. Based on the RMSE and MAPE values, the results show that the RMSE data out sample has a smaller RMSE value when compared to the in sample data, while the MAPE value is smaller in sample data when compared to the out sample data

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