RAGAM: Journal of Statistics and Its Application
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
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<![CDATA[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 ]]>
<![CDATA[Dimiyati Dimiyati]]>;
<![CDATA[Nurul Latipah]]>;
<![CDATA[Yuana Sukmawaty]]>
<![CDATA[ RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application ]]>
Publisher : <![CDATA[Universitas Lambung Mangkurat ]]>
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DOI: 10.20527/ragam.v3i1.11622
<![CDATA[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). ]]>
<![CDATA[ANALISIS REGRESI LOGISTIK ORDINAL UNTUK MEMODELKAN TINGKAT.KEPARAHAN.PENYAKIT HIV/AIDS.DI RUMAH SAKIT DAERAH IDAMAN BANJARBARU ]]>
<![CDATA[Thaibatun Nissa]]>;
<![CDATA[Dewi Sri Susanti]]>
<![CDATA[ RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application ]]>
Publisher : <![CDATA[Universitas Lambung Mangkurat ]]>
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DOI: 10.20527/ragam.v3i1.12797
<![CDATA[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]]>
<![CDATA[ANALISIS PENGARUH INDUSTRI MIKRO DAN KECIL TERHADAP PERTUMBUHAN EKONOMI DI INDONESIA DENGAN PENDEKATAN EKONOMETRIKA REGRESI SPASIAL DATA PANEL ]]>
<![CDATA[Jonathan Adi Winata]]>;
<![CDATA[Fuad Muhajirin Farid]]>;
<![CDATA[Selvi Annisa]]>
<![CDATA[ RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application ]]>
Publisher : <![CDATA[Universitas Lambung Mangkurat ]]>
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DOI: 10.20527/ragam.v3i1.12799
<![CDATA[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 ]]>
<![CDATA[ANALISIS REGRESI ROBUST M ESTIMATOR UNTUK MENGETAHUI FAKTOR YANG MEMPENGARUHI LAMA STUDI MAHASISWA S1 STATISTIKA FMIPA UNIVERSITAS LAMBUNG MANGKURAT ]]>
<![CDATA[Widawati Annisa Putri]]>;
<![CDATA[Fuad Muhajirin Farid]]>;
<![CDATA[Selvi Annisa]]>
<![CDATA[ RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application ]]>
Publisher : <![CDATA[Universitas Lambung Mangkurat ]]>
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DOI: 10.20527/ragam.v3i1.12798
<![CDATA[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]]>
<![CDATA[PEMETAAN JUMLAH KRIMINALITAS DI KALIMANTAN SELATAN MENGGUNAKAN METODE CO-KRIGING DAN INVERSE DISTANCE WEIGHTING ]]>
<![CDATA[Muhammad Haidir Gazali]]>;
<![CDATA[Dewi Sri Rahkmawati]]>;
<![CDATA[Maisarah Maisarah]]>
<![CDATA[ RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application ]]>
Publisher : <![CDATA[Universitas Lambung Mangkurat ]]>
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DOI: 10.20527/ragam.v3i1.12806
<![CDATA[AbstractCriminality is any form of action that harms economically and psychologically and violates the law, social norms and religion. South Kalimantan is in 21st position with an average of 3,282 criminal cases per year in the period 2018 to 2020. One method to interpolate data in unsampled locations is the co-kriging and inverse distance weighting (IDW) method. This study aims to estimate the number of crimes in South Kalimantan with the co-kriging and IDW methods, compare the results to determine the best method, and present a thematic map of the distribution of crime in South Kalimantan. In the co-kriging method, the number of crimes is the primary variable and population density is the secondary variable. The result of this research is that between the two methods, IDW is better for interpolation in estimating the crime value than the co-kriging method. The IDW method provides good interpolation results whose interpolation value is close to the value of the real data. Although co-kriging has the potential to provide accurate results if the cross-variogram model can be formed properly, the constraints faced in this study indicate that IDW remains a more stable and reliable choice in estimating crime in South Kalimantan. Keywords: Crime, Co-kriging, Inverse Distance Weighting]]>
<![CDATA[QUICK ROBUST CLUSTERING USING LINKS (QROCK) UNTUK PENGELOMPOKAN DESA KABUPATEN BANJAR ]]>
<![CDATA[Muhammad Rizki Shofari]]>;
<![CDATA[Oni Soesanto]]>
<![CDATA[ RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application ]]>
Publisher : <![CDATA[Universitas Lambung Mangkurat ]]>
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DOI: 10.20527/ragam.v3i1.12805
<![CDATA[AbstractAccurate village profile planning needs to be done with mapping based on its characteristics. Village grouping based on the characteristics of village facilities and potential or its characteristics based on the Building Village Index indicator can help determine priorities in village development. In this study, mixed data was used, with numerical data grouping using Hierarchical Agglomerative Nesting (AGNES) algorithm and categorical data with Quick Robust Clustering Using Links (QROCK). The resulting clusters are then combined using the QROCK Ensemble algorithm (algCEBMDC). The data is sourced from the 2021 Village Potential Data Collection (PODES) by the Central Statistics Agency in 277 villages in Banjar Regency, including 18 numerical variables and 29 categorical variables. The results of the study obtained optimal clusters based on the ratio of within-group standard deviation (SW) to between-group standard deviation (SB) resulting in a ratio of 4.82.10-9 with a threshold of 0.4 to 0.9 resulting in 6 clusters. The best cluster results are cluster 4 (4 villages) and cluster 3 (14 villages), then cluster 2 (villages) and cluster 1 (186 villages), and clusters that need development priorities are cluser 5 (2 villages) and cluster 6 (1 village) which are outliers based on processing results. Keywords: Village Grouping, Cluster, AGNES Algorithm, Quick Robust Clustering Using Links (QROCK), AlgCEBMDC]]>
<![CDATA[PEMODELAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) PADA KEJADIAN STUNTING DIiKABUPATEN BARITO KUALA TAHUN 2022 ]]>
<![CDATA[Azkia Azkia]]>;
<![CDATA[Dewi Sri Susanti]]>
<![CDATA[ RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application ]]>
Publisher : <![CDATA[Universitas Lambung Mangkurat ]]>
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DOI: 10.20527/ragam.v3i1.12796
<![CDATA[Stunting is a condition of malnutrition in toddlers that causes their height to be lower than other children their age. In 2022, South Kalimantan Province has a stunting prevalence of 24.6% and ranks fifteenth in Indonesia. Barito Kuala Regency, one of the regions in South Kalimantan Province, has the highest stunting rate at 33.6% which is included in the Chronic- Acute category (≥ 20%). This study uses the GWNBR model to characterize the factors that cause stunting in Barito Kuala Regency. The GWNBR model will make it easier for researchers to find out the factors that affect stunting in each sub-district. The weight matrix used is a fixed kernel function and an adaptive kernel function. The predictor variables used were the percentage of infant history of complete basic immunization, history of exclusive breastfeeding in infants <6 months, history of low birth weight babies, new visits to pregnant women (K1), sixth antenatal care (ANC) visit (K6), history of pregnant women who received blood supplement tablets, history of infants 6-11 months who received vitamin A, active posyandu and households with access to appropate sanitary facilities.(healthy latrines). The best model results obtained with adaptive gaussian weighting with an AIC value of 167.25. Keywords: Stunting Cases, GWNBR model.]]>
<![CDATA[PEMODELAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) MENGGUNAKAN PEMBOBOT KERNEL PADA KASUS TINGKAT PENGANGGURAN TERBUKA DI KALIMANTAN ]]>
<![CDATA[Viona Oktafiani]]>;
<![CDATA[Dewi Sri Susanti]]>;
<![CDATA[Yeni Rahkmawati]]>
<![CDATA[ RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application ]]>
Publisher : <![CDATA[Universitas Lambung Mangkurat ]]>
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DOI: 10.20527/ragam.v3i1.12822
<![CDATA[AbstractUnemployment is one of the serious problems in Indonesia's economic development. This unemployment describes human resources that have not been utilized optimally, as a result of which people's productivity and income have not been maximized, this can also be one of the causes of poverty and other social problems. This study aims to find out the general picture of the open unemployment rate in the Kalimantan region, get the best model and factors that influence the open unemployment rate and illustrate it through thematic maps. The study began with testing assumptions and spatial effects then continued with testing global regression modeling and Geographically Weighted Regression. The weighting function used in this study is adaptive gaussian kernel. The variable that has a positive effect on the open unemployment rate in the Kalimantan region is population density. While the variable that negatively affects the open unemployment rate is the Labor Force Participation Rate. Keywords: Open Unemployment Rate, Kalimantan Island, Spatial, GWR]]>
<![CDATA[PENGARUH GOOD CORPORATE GOVERNANCE TERHADAP NILAI PERUSAHAAN DENGAN PROFITABILITAS SEBAGAI VARIABEL MODERATING ]]>
<![CDATA[Gelu Savira Dwi Cahyani]]>;
<![CDATA[Muhammad Riza Hafizi]]>;
<![CDATA[Enriko Tedja Sukmana]]>;
<![CDATA[Fuad Muhajirin Farid]]>;
<![CDATA[Muhammad Noval]]>
<![CDATA[ RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application ]]>
Publisher : <![CDATA[Universitas Lambung Mangkurat ]]>
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DOI: 10.20527/ragam.v3i1.11903
<![CDATA[Good Corporate Governance is good and correct corporate governance to create added value for all stakeholders. A company's value is good if corporate governance is implemented well. Implementing good GCG will increase profits and reduce the risk of losses in the future so that it can increase company value. This research aims to examine the influence of Good Corporate Governance on Company Value with Profitability as a Moderating Variable in mining companies listed on the Indonesia Stock Exchange 2015-2019. The population of this research is all mining companies listed on the Indonesia Stock Exchange (BEI) in 2015-2019. The sampling method uses purposive sampling with a total sample of 3 companies. The data analysis method used is SEM-based Patrial Least Square (PLS) software. This research concludes that from the results of data analysis, based on predetermined decision-making, the results of this research are that Good Corporate Governance (GCG) has a positive and significant effect on company value. Then, profitability does not moderate the influence of GCG on company value.]]>
<![CDATA[PERAMALAN JUMLAH PENUMPANG BUS RAPID TRANSIT (BRT) BANJARBAKULA DENGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE WITH EXOGENOUS VARIABLE (ARIMAX) DENGAN EFEK VARIASI KALENDER ]]>
<![CDATA[Eka Ayu Frasetyowati]]>;
<![CDATA[Nur Salam]]>;
<![CDATA[Yeni Rahkmawati]]>
<![CDATA[ RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application ]]>
Publisher : <![CDATA[Universitas Lambung Mangkurat ]]>
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DOI: 10.20527/ragam.v3i1.12789
<![CDATA[Banjarbakula Bus Rapid Transit (BRT) is an inner-city bus-based mass transit system that provides a sense of comfort, safety, speed in mobility, and low cost in serving the citizens of Banjarmasin City and Banjarbaru City. Based on data on the number of passengers on the Banjarbakula BRT for the period April 2020 - February 2023, public interest in using the Banjarbakula BRT as a mode of transportation is quite high. However, the limited units and operational schedules make the Banjarbakula BRT unable to fully meet the needs of the public. Forecasting the number of passengers of BRT Banjarbakula for the next 12 periods is one of the measures to prepare the infrastructure, quality and units of BRT Banjarbakula in order to facilitate the public and create a better transportation system. In the Banjarbakula BRT passenger data, there is an increase in the number of passengers at certain times such as during religious holidays and school holidays, so this increase in passenger numbers is thought to be due to the influence of the calendar variation effect. This research intends to forecast the number of passengers of BRT Banjarbakula using the best ARIMAX model with the effect of calendar variation. The results indicate that the ARIMAX (0, 1, 1) model is the best ARIMAX model to forecast the number of passengers of BRT Banjarbakula for the next 12 periods. The forecast results indicate an increase in the month where the Christmas celebration and also the memorial haul guru sekumpul, so that the variable Christmas celebration and memorial haul guru sekumpul significantly affect the number of passengers of BRT Banjarbakula.Keywords: Forecasting, BRT Banjarbakula, ARIMAX with calendar variation effects]]>
