cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Fuad Muhajirin Farid
Contact Email
fuad.farid@ulm.ac.id
Phone
+6285730029903
Journal Mail Official
ragam.statistika@ulm.ac.id
Editorial Address
Jalan A. Yani Km.36, Kampus ULM Banjarbaru, Banjarbaru, Kalimantan Selatan, Indonesia 70714
Location
Kota banjarmasin,
Kalimantan selatan
INDONESIA
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
 1   2   3   4   5 
ESTIMASI PARAMETER RANDOM EFFECT MODEL PADA REGRESI PANEL MENGGUNAKAN METODE GENERALIZED LEAST SQUARE (STUDI KASUS: KEMISKINAN DI PROVINSI KALIMANTAN SELATAN) Ariandy Hermawan; Yuana Sukmawaty; Aprida Siska Lestia
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.v1i1.7419

Abstract

Poverty is a condition that concerns the inability to meet the most minimum demands of life, especially from the aspects of consumption, income, education, and health. The problem of poverty is very complex and multidimensional in nature, as it relates to social, economic, cultural and other aspects. This study focuses on observation areas in South Kalimantan Province, with the PPM value in 2021 reaching (4.83%) still above the target goal of the Regional Medium-Term Development Plan (RPJMD) of South Kalimantan Province (3.96- 4.01%), so that further interventions are still needed to be able to reduce PPM in poverty cases. This study aims to estimate the parameters of the panel regression model used to analyze factors that are suspected to affect poverty cases in South Kalimantan Province in 2016-2020. The Random Effect Model (REM) is the best model used in this study, assuming that there are differences in slopes and interceptions caused by residual due to differences between individual units and between time periods. The process of estimating parameters on REM is determined through the Generalized Least Squares (GLS) Estimator method . From the results of the data processing, it was obtained that the model is influenced by economic growth, life expectancy, open unemployment rate, and labor force participation rate. From the results of the analysis of 2 (two) models, it was tested significantly and affected poverty in South Kalimantan Province in 2016-2020.  Keywords:   Poverty, Data Regression Panel, Generalized Least Square Method (GLS).
PENERAPAN ALGORITMA BACKPROPAGATION DENGAN ADAM OPTIMIZER DALAM MEMPREDIKSI HARGA BITCOIN TERHADAP USD Adi Andrian; Oni Soesanto; Sigit Dwi Prabowo
RAGAM: Journal of Statistics & Its Application Vol 3, No 2 (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.v3i2.13354

Abstract

Bitcoin is a currency that implements an online transaction system without involving banks. To investors, Bitcoin is seen as a promising investment instrument due to its consistent price increase every year. However, it is important to note that Bitcoin is also a high-risk investment instrument, requiring specific techniques to consider when making buy or sell decisions. Backpropagation is a method in Artificial Neural Networks known for its good ability to generate predictions. This method can adjust network weights to reduce prediction errors and does not require assumption testing to apply this method to data. The aim of this research is to implement the Backpropagation algorithm with the Adam Optimizer to predict Bitcoin prices against USD. This method will perform computational calculations to produce predictions close to the actual values. The research results in an optimal model with 5 input layers, 5 hidden layers, and 1 output layer. The training and testing data are divided with a ratio of 70% to 30%, and the maximum number of epochs used is 3000. The accuracy results using the backpropagation method optimized with the Adam Optimizer produced predictions with an average value of 46,346.91, a MAPE of 0.6962%, a MSE of 160,159.53, and a RMSE of 400.20.
PENGARUH PERUBAHAN TAHUN TERHADAP PRODUKSI PERTANIAN DI INDONESIA MENGGUNAKAN PENDEKATAN REPEATED MEASURES MANOVA Selly Rizkiyah; Indira Zein Rizqin; Milla Akbarany Baktiar Putri; Muhammad Nasrudin; Trimono Trimono
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.v4i1.14970

Abstract

This study aims to analyze significant differences in rice paddy production in Indonesia based on year factors using the Repeated Measures MANOVA method. The data used includes harvest areas, productivity, and total rice production from various provinces during the period 2020-2024.  The results showed that there was a significant relationship between the variables tested, so the independence assumption in the MANOVA method was not met. Therefore, Repeated Measures-MANOVA was used as an alternative approach that is more suitable for repeated data. The analysis showed that there were significant differences in rice production by year, with a p-value of <0.05 in all multivariate statistics. The results highlight the importance of efficient crop land management and increased productivity to support the sustainability of the agricultural sector. The Repeated Measures-MANOVA approach proved effective in identifying variations in production based on time factors and can be a relevant analytical tool.
ANALISIS PERBANDINGAN MODEL REGRESI LINIER BERGANDA, SPATIAL DURBIN ERROR MODEL (SDEM), DAN SPATIAL LAG X (SLX) DALAM PERMODELAN DATA INDEKS PEMBANGUNAN MANUSIA (IPM) DI PROVINSI KALIMANTAN SELATAN Dimiyati Dimiyati; Nurul Latipah; Yuana Sukmawaty
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.v3i1.11622

Abstract

This research aims to determine the comparison of multiple linear regression models, spatial durbin error model (SDEM), and spatial lag In this research there are three independent variables, namely poverty severity (2022), population density (2022) and pure participation rate (2019), while the dependent variable is the human development index (2022). This research data is secondary in nature, namely obtained from the website of the South Kalimantan Central Statistics Agency. Based on the results and discussion, it is concluded that the best model from the comparison of multiple linear regression models, spatial durbin error model (SDEM), and spatial lag x (SLX) in modeling human development index (HDI) data in South Kalimantan province is the spatial durbin error model (SDEM). This is because the spatial durbin error model (SDEM) has the smallest AIC value compared to the multiple linear regression model, and spatial lag x (SLX). 
PENERAPAN MANOVA DALAM ANALISIS HUBUNGAN ANTARA LUAS WILAYAH DENGAN CAKUPAN VAKSINASI COVID-19 DI PROVINSI KALIMANTAN SELATAN Muhammad Fadhil Rasyidin; Dewi Anggraini; Hidayatullah Muttaqin
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.v2i2.11331

Abstract

In Indonesia, the problem with the spread of COVID-19 is 1.35 million with 1.17 million recovered and 36,721 deaths as of March 5, 2021. From the data on the spread of COVID-19, it can be seen from the length of time that has passed, the number of cases has increased. Sinovac (CoronaVac) is a vaccine for COVID-19 produced by the Sinovac company, using inactivated virus technology or utilizing attenuated viruses. The coverage of vaccines for dose 1 and dose 2 in South Kalimantan Province is low compared to other provinces, even though South Kalimantan Province is a province that is classified as having the smallest area among other provinces included in Kalimantan Island. The purpose of the study was to find out the relationship between area and coverage of COVID-19 vaccination in South Kalimantan Province. This study uses One-Way Manova because it analyzes one predictor variable, in the form of area and three response variables simultaneously, in the form of COVID-19 vaccination coverage based on the vaccination target category: health human resources, public officers and the elderly. The results of the study using the One-Way MANOVA method showed the Pillai's Trace value of 0.020. The results of the multivariate significance test obtained by Wilk's Lambda   so that it rejects  which means that the significant model or area () has an influence on vaccination coverage (). Based on alleged multivariate regression model and the results of the MANOVA test, both are directly proportional, namely there is a significant relationship that area area has an influence on COVID-19 vaccination coverage. Large areas have vaccination coverage that tends to be low when compared to small areas. Vaccination distribution for a small area can be said to be more efficient than other broad categories and for elderly vaccine recipients, it is lower than the category of vaccine recipients for public officials and health human resources
PEMODELAN KEPADATAN PENDUDUK DENGAN PENDEKATAN REGRESI POISSON TERGENERALISASI Alfin Dratama Maulana; Dewi Anggraini; Yuana Sukmawaty
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.v2i1.9893

Abstract

Kebanyakan penduduk cenderung meningkat dan ketersediaan lahan semakin berkurang setiap tahun akibat migrasi. Upaya peningkatan kualitas penduduk akan lebih sulit dilakukan di daerah dengan kepadatan tinggi, sehingga dapat menimbulkan masalah baik di bidang sosial ekonomi, ketersediaan lahan, maupun kebutuhan pangan. Oleh karena itu, perlu diketahui faktor-faktor yang mempengaruhi kepadatan penduduk. Salah satu pendekatan untuk memprediksi pola hubungan antara faktor dan kepadatan penduduk, dapat menggunakan regresi Poisson. Regresi Poisson merupakan salah satu regresi nonlinier yang sering digunakan untuk memodelkan variabel dalam bentuk bilangan bulat. Penelitian ini bertujuan untuk mendeskripsikan karakteristik penduduk setiap provinsi di Indonesia,mengestimasi parameter model regresi Poisson dan menjelaskan faktor-faktor yang berpengaruh signifikan terhadap masalah kepadatan penduduk. Faktor-faktor yang diduga berpengaruh terhadap kepadatan penduduk adalah jumlah penduduk, luas wilayah, jumlah SD, jumlah SMP, jumlah SMA, jumlah puskesmas, jumlah koperasi dan jumlah kematian. . Hasil penelitian ini menunjukkan bahwa model regresi Poisson digeneralisasikan , bahwa faktor-faktor yang mempengaruhi kepadatan penduduk setiap provinsi di Indonesia pada tingkat kepadatan penduduk (Y) dan jumlah penduduk  mempunyai hubungan yang searah, artinya setiap pertambahan satu penduduk maka pertambahan penduduk bertambah sebesar 9,7 %, sedangkan luas wilayah mempunyai hubungan kebalikan sebesar 0,0136%, artinya semakin sempit satu satuan luas, maka tingkat kepadatan penduduk bertambah sebesar 0,0136%.
SEGMENTASI PELANGGAN MENGGUNAKAN METODE K-MEANS CLUSTERING BERDASARKAN MODEL RFM (RECENCY, FREQUENCY, MONETARY) Muhammad Hafidz Anshary; Oni Soesanto; Ayatullah Ayatullah
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.v1i1.7382

Abstract

Companies or entrepreneurs must better understanding customers data in all aspects, including detecting similarities and differences among customers, predicting their behavior, and offering better options and opportunities to customers. Customer segmentation is carried out to obtain this information, which is part of CRM (Customer Relationship Management). One of the general models in the application of customer segmentation is the RFM (Recency, Frequency, and Monetary) model. This research method uses a combination of the RFM model and clustering. RFM is used as a description of customer behavior in conducting transactions. Clustering is a process that is widely used and is designed to categorize data. Clustering uses the K-Means Algorithm to determine the number of clusters using the Elbow and Silhouette methods. The application of RFM analysis and the K-Means resulted in two customer segments, namely potential customers and non-potential customers. Potential customers have the characteristics of frequent transactions and also large expenses. Non-potential customers have the characteristics of infrequent transactions and also standard expensesKeywords:  Customer Segmentation, RFM Model, K-Means Clustering
ANALISIS REGRESI LOGISTIK ORDINAL UNTUK MEMODELKAN TINGKAT.KEPARAHAN.PENYAKIT HIV/AIDS.DI RUMAH SAKIT DAERAH IDAMAN BANJARBARU Thaibatun Nissa; Dewi Sri Susanti
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.v3i1.12797

Abstract

HIV is a virus that infects the immune system cells, thereby damaging the human immune system. AIDS is a collection of symptoms that arise due to the compromised immune system of the human body as a result of a positive infection by the HIV virus. HIV/AIDS remains a complex and significant global health issue. Despite advancements in treatment and prevention, the severity of HIV/AIDS remains a primary focus in healthcare management efforts. This study aims to determine the factors influencing the severity of HIV/AIDS patients at the Regional Hospital of Idaman Banjarbaru using ordinal logistic regression analysis. Ordinal logistic regression is employed to understand the relationship between the dependent variable (severity of the disease) and independent variables, where the dependent variable is ordinal in scale. The data used for this analysis is secondary data extracted from the inpatient medical records of the Idaman Banjarbaru Regional Hospital, comprising a total of 68 cases of HIV/AIDS. Assumed factors influencing the severity of patients include gender, age, duration of hospitalization, education, employment status, marital status, and place of residence. The analysis results indicate a significant relationship between the severity of HIV/AIDS patients and marital status. The highest likelihood of patients experiencing HIV/AIDS is in the divorced response category with a stage 3 category, where the probability value is 0.943. Individuals in the married and divorced categories are 1.53 times more likely to experience HIV/AIDS with a stage 4 status and complications ranging from 3 to 5. Keywords:   Severity of Disease, HIV/AIDS, Ordinal Logistic Regression, Odds Ratio
Segmentation of West Java Province Based on Socio-Economic Indicators Using K-Means and Agglomerative Clustering Methods Tiara Valentina
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.v4i1.15589

Abstract

Regional segmentation based on socio-economic indicators is a crucial approach in data-driven development planning. Through accurate segmentation, governments can design more targeted policies aligned with the specific characteristics of each region. This study aims to compare two clustering methods, namely K-Means and Agglomerative Clustering, in grouping regions within West Java Province based on socio-economic indicators such as poverty rate, open unemployment rate, and Human Development Index (HDI). The analysis was conducted using the Python programming language on the Google Colab platform. Cluster performance was evaluated using the Elbow Method to determine the optimal number of clusters and the Silhouette Score to assess cluster quality. The results indicate that Agglomerative Clustering produces more consistent and interpretable segmentations, particularly in reflecting the socio-economic similarities between regions. However, in terms of computational efficiency, the K-Means method performs better due to its faster processing time. These findings offer valuable insights for regional policymakers in setting development priorities more effectively, grounded in the actual socio-economic conditions of each area
Pemodelan Regresi Global (Glm) dan Regresi Spasial (Sar Dan Sdm) Pada Kasus Indeks Pembangunan Manusia Di Provinsi Kalimantan Selatan Ana Marliana; Jonathan Adiwinata
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.v2i2.11614

Abstract

The Human Development Index (HDI) is a parameter that functions to assess the success of the quality of human life. The factors used in the research are the severity of poverty, population density and net participation rate. The research carried out aims to see what factors influence the HDI in South Kalimantan Province in 2022, the HDI value for South Kalimantan Province is below the Indonesian HDI value and quite a few regencies/cities in South Kalimantan Province have HDI values below the HDI value. Indonesia. The statistical analysis used is a spatial approach, where the SAR and HR spatial regression models will be searched. The Global Regression Model (GLM) obtained in this study is  ,  while the Spatial Autoregressive (SAR) model is   and Spatial Durbin Model is The best model that can be obtained is the Spatial Durbin Model (SDM) with an AIC value of 52,82654 and an  value of 95,86%.

Page 1 of 5 | Total Record : 50

 1   2   3   4   5 

Filter by Year

2022 2025


Reset
Filter By Issues
All Issue Vol 4, No 2 (2025): RAGAM: Journal of Statistics & Its Application Vol 4, No 1 (2025): RAGAM: Journal of Statistics & Its Application Vol 3, No 2 (2024): RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application Vol 2, No 2 (2023): RAGAM: Journal of Statistics & Its Application Vol 2, No 1 (2023): RAGAM: Journal of Statistics & Its Application Vol 1, No 1 (2022): RAGAM: Journal of Statistics and Its Application