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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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PEMODELAN KREDIBILITAS BÜHLMANN-STRAUB UNTUK DATA FREKUENSI KLAIM BERDISTRIBUSI POISSON-SUJATHA Evania Putri; Aceng Komarudin Mutaqin
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.v4i1.14679

Abstract

Motor vehicle insurance provides compensation for damage or loss incurred by motor vehicles. In determining credibility values, claim frequency data is required. Sometimes, this claim frequency data contains overdispersion issues, necessitating alternative methods for modeling claim frequency using a mixture distribution. The mixture distribution used in this research is the Poisson-Sujatha mixture distribution. The credibility method employed is an advancement of the Bühlmann method, known as the Bühlmann-Straub credibility method. The Bühlmann-Straub credibility model has been successfully applied in various insurance contexts, previously used in modeling with the Negative Binomial-Lindley distribution in 2023, yielding significant results.Before applying the credibility model, the parameters of the Poisson-Sujatha distribution are estimated using the maximum likelihood estimation method. The goodness-of-fit test used in this research is the chi-squared goodness-of-fit test. The research data consists of secondary claim frequency data for motor vehicle insurance recorded by PT. X in Category 1 (passenger transport with coverage values between Rp 0 to Rp 125,000,000) in Region 2 (DKI Jakarta, West Java, and Banten) for 2018 and 2019. Based on the application of this claim frequency data, the Bühlmann-Straub credibility factor is close to 1, indicating that the processed data has a significant impact on estimating the average future claim frequency. The estimated average motor insurance claim frequency for Indonesia, Category 1, Region 2, in 2020 is 0.0041, meaning that if there are 10,000 insurance policyholders in 2020, approximately 41 partial loss claims are expected.
PEMODELAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) PADA KEJADIAN STUNTING DIiKABUPATEN BARITO KUALA TAHUN 2022 Azkia Azkia; 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.v3i1.12796

Abstract

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.
PERAMALAN ANGKA PERCERAIAN DI KABUPATEN JEMBER PADA TAHUN 2022 MENGGUNAKAN METODE ARIMA ana safitri; Nur Salam; Aprida Siska Lestia
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.v2i2.11333

Abstract

Jember Regime is a locale that has the largest number of separation cases in Indonesia. Subsequently, it is important to have a measurable estimating where information from the past is utilized to foresee future prospects. The reason for this study is to break down the attributes of the information, examine the best ARIMA (Autoregressive Coordinated Moving Normal) model and conjecture utilizing the best model in Jember Regime in June - December 2022. The kind of information is quantitative with relapse estimating techniques and Box-Jenkins. Assurance of the ARIMA model is done when the information is fixed and has met the background noise prerequisites and is typically conveyed. The best demonstrating is resolved in light of huge boundaries in view of the estimation aftereffects of the Sum squared resid (SSE), Adjusted R-squared, Akaike info criterion (AIC), dan Schwarz criterion (SBC). The outcomes acquired from this study are separate from case information in Jember Rule isn't great and lopsided so doing information stationarity is vital. After the information is fixed, the best ARIMA model that meets the necessities is the ARIMA model (2,1,1). The consequences of the ARIMA (Autoregressive Coordinated Moving Normal) model (2,1,1) with the situation Zt = 0,91 Zt-1 + 0,19 Zt-2 + 0,1 Zt-3 + ?t + 0,78 ?t-1.
PENERAPAN MODEL REGRESI PANEL KOMPONEN DUA ARAH PADA POLA CURAH HUJAN PROVINSI KALIMANTAN TENGAH Vichario Indra Pradana; Yuana Sukmawaty; Nur Salam
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.v2i1.8303

Abstract

Rainfall is the climate element that is most closely related to supporting the life processes of Indonesian people such as agricultural production, plantations, fisheries, and aviation. In 2014-2016, Indonesia experienced a drought due to a global climate anomaly called the El Nino phenomenon, where annual rainfall at that time tended to decrease from other years. While in 2020-2022, rainfall in Indonesia tends to increase from other years, this event is called the La Nina phenomenon. This study aims to describe the rainfall patterns that occur in each phenomenon and analyze the regression model of rainfall panels in Central Kalimantan province with a two-way component approach. Random Effects Model (REM) is the most appropriate model to be used in the phenomenon of La Nina. Fixed Effect Model (FEM) is the most appropriate model to be used in the El Nino phenomenon. Feasible Generalized Least Square is a parameter estimation method that is focused and used to estimate regression parameters in this study. Based on the results of regression analysis of panel data, for the phenomenon of La Nina obtained R2 value of 51.66% and found that the average air temperature variable tested significant. For the El Nino phenomenon, the value of R2 is 75.35% and it is found that there are no significant independent variables tested. Therefore, it can be expected that the increase in average air temperature can decrease the average rainfall value when the La Nina phenomenon occurs in Central Kalimantan province.
Penerapan Model Kredibilitas Bühlmann Pada Data Frekuensi Klaim Asuransi Kendaraan Bermotor Di Indonesia Yang Berdistribusi Poisson-Amarendra Aliya Maharani; Aceng Komarudin Mutaqin
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.v4i2.16591

Abstract

In motor vehicle insurance, policyholders are required to pay a premium to the insurance company. One method to assist insurance companies in determining premiums is credibility theory. One model from this approach is the Bühlmann credibility model. Generally, claim frequency data is overdispersed. There are various distributions suitable for addressing overdispersion, one of which is the Poisson-Amarendra distribution. The method used to estimate the parameters of the Poisson-Amarendra is the maximum likelihood method. The research material used is motor vehicle insurance data in Indonesia for the year 2019, recorded by PT. X, categorized into 8 categories and 3 regions. The results of the Chi-Square goodness-of-fit test show that the claim frequency data from the population distributed by the Poisson-Amarendra distribution includes category 2 in region 1 and category 6 in region 3. The results of applying the Bühlmann credibility model yield a credibility factor of 0.0029 for category 2 in region 1 and 0.0101 for category 6 in region 3. The estimated average claim frequency for motor vehicle insurance in the next period for category 2 in region 1 is 0.0029. This means that if the number of insurance policyholders in 2020 is the same as in 2019, which is 15,878, an estimated 46 partial loss claims will occur. The estimated average claim frequency for category 6 in region 3 is 0.0102, with an estimated 44 partial loss claims occurring in 2020, assuming the number of policyholders in 2020 remains the same as in 2019, which is 4,313.
PEMODELAN REGRESI SPASIAL PADA ANGKA PARTISIPASI MURNI JENJANG PENDIDIKAN SMA SEDERAJAT DI PROVINSI KALIMANTAN SELATAN Suci Anshari; Dewi Sri Susanti; 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.v1i1.7318

Abstract

This research is done for modeling of the Pure Enrolment Rate (PER) at the senior high school level in South Kalimantan Province that uses analysis of spatial regression. The purpose of this analysis is to construct the modeling of spatial regression of the Pure Enrolment Rate (PER) at the senior high school level in South Kalimantan Province and to identify the significant factors that influent the pure enrollment rate (PER). The result of this research shows that the modeling of spasial regression is suitable for use in the Pure Enrolment Rate (PER) at the senior high school level in South Kalimantan Province in 2017 – 2019 is the Spatial Autoregressive Model (SAR). The model form can be seen that in 2017 there is no significant influence factors to the PER, in 2018 the ratio of the student number to the school number (X5) and the ratio of the student number to the teacher number (X6) that are the influence factors significantly to the Pure Enrollment Rate (PER), while in 2019 only the factor of the ratio of the students number to the schools number (X5 ) that influents significantly to the PER.Keywords:   PER, Education, and Spatial Regression Analysis
PEMODELAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) MENGGUNAKAN PEMBOBOT KERNEL PADA KASUS TINGKAT PENGANGGURAN TERBUKA DI KALIMANTAN Viona Oktafiani; Dewi Sri Susanti; Yeni Rahkmawati
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.v3i1.12822

Abstract

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
PRAKIRAAN INDEKS KEKERINGAN MENGGUNAKAN METODE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) BERDASARKAN DATA STANDARDIZED PRECIPITATION INDEX (SPI) KOTA BANJARBARU Nabila Septiani; Nur Salam; Khairullah Khairullah
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.v2i2.11334

Abstract

Drought is a disaster that has a bad impact, especially in the city of Banjarbaru. There are  various ways to reduce the impact of drought in the future, one of which is by looking for information regarding the predicted drought index for the following year. The data used in this research to find the drought index value is Banjarbaru City rainfall data for 2007-2022. Seasonal Autoregressive Integrated Moving Average (SARIMA) method  is a calculated method for predicting rainfall data  and the data obtained is a forecast of rainfall in the city of Banjarbaru for the next 12 periods, namely SARIMA (0,2,3) (0,1,1)12. This model is a model that is suitable for use because it has fulfilled several tests, namely stationarity of variance and mean, significance test, white noise test and normality test with an AIC value of 1022,60 and an equation model obtained from SARIMA (0,2,3) (0,1,1)12 is (1-B)2 (1-B12) Zt=(1+1,77B-0,54B2+ 0,23B3 )(1-0,96B12 )εt. After obtaining forecast rainfall data for the next 12 periods. Rainfall data for 2007-2022 and forecast results for 2023 were used to find the drought index value using the Standardized Precipitation Index (SPI) method. It was found that the highest negative drought index value occurred in January, namely -1,774, including the dry category and the drought index had a positive value The highest occurred in June, namely 0,582, including the normal category.  The calculation results of this drought index forecast are used to provide additional information to anticipate drought disasters in the future. Keywords:   Drought Index, Rainfall, SPI Method, SARIMA Method, AIC
PENGARUH PERFORMA VIDEO TERHADAP JUMLAH VIEWS VIDEO REGULER DI YOUTUBE MENGGUNAKAN ANALISIS JALUR Muhammad Adam Ashar; Fuad Muhajirin Farid; Selvi Annisa
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.v2i1.10042

Abstract

YouTube is a video-sharing website which is one of the media for deployment information that is of great interest to the public in Indonesia. Being the most visited website in the world, YouTube has had a significant impact on modern society. Many people in Indonesia have utilized YouTube as a platform to share their thoughts and creativity through the video they create, as well as to make income. Creative content will usually get more responses from the audience. Creating a regression model for use in path analysis enables the investigation of causal links between various variables. The intention of this study is to specify the path's structure and analyze what variables effect YouTube video views. The exogenous variables used to observe the influence of views are impressions, CTR and watch time. This study uses path analysis to examine how video performance impacts views both directly and indirectly using path diagrams. Considering the outcomes of this study, the variables that affect views are impressions and CTR. Impressions have an indirect effect on Watch Time and Views but smaller than the direct effect, so the best path to increase views is the direct effect path of impressions. Impressions have a direct effect of 1,214 while CTR has a direct effect on views of 1,077. Keywords:     YouTube, Views, Path Analysis, Direct Effect
DETERMINAN KEJADIAN KISTA OVARIUM PADA WANITA USIA SUBUR DI KABUPATEN BALANGAN MENGGUNAKAN REGRESI LOGISTIK BINER Dhea Arinda; Dewi Anggraini; Meitria Syahadatina Noor
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.v1i1.7409

Abstract

Ovarian cysts are the most common gynecologic cases of many gynecologic cancers. Ovarian cyst is a disease that causes many deaths. This high mortality rate is due to the fact that the disease is initially asymptomatic and only causes complaints when metastases have occurred so that 60-70% of patients come at an advanced stage. Based on the results of the 2007 Basic Health Research survey, the number of patients with ovarian cysts in South Kalimantan was 1,2% of 56 respondents. This study took a case study in a district in South Kalimantan, namely Balangan Regency with the aim of explaining the characteristics of the distribution of ovarian cysts and the factors that influence the incidence of ovarian cysts in women of childbearing age in Balangan Regency using binary logistic regression method. Based on descriptive statistical analysis, it was found that the distribution characteristics of ovarian cyst sufferers were from 59 people who had checked for cyst symptoms at Balangan Hospital, 46 people were known to have cysts, while 13 people were not known to have cysts. Based on binary logistic regression analysis, the factors that influence the incidence of ovarian cysts for data on the incidence of ovarian cysts in Balangan Hospital are parity and employment status, while the age factor has no significant effect. Using the Odss Ratio (OR) parity value, patients with nulliparous status had a 0,033 higher risk of developing ovarian cysts than patients with multiparous status. using the OR value of the occupational status patients who had a job had a 0,014 higher risk of developing ovarian cysts than patients who did not have a job.  Keywords:   Ovarian cysts, Logistic binary, Odds Ratio.

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