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Jl. H.R. Soebrantas Km. 15.5 No. 155 Gedung Fakultas Sains dan Teknologi UIN Sultan Syarif Kasim Riau Kel. Tuahmadani Kec. Tampan Pekanbaru - Riau 28293
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INDONESIA
Indonesian Council of Premier Statistical Science
Published by Universitas Islam Negeri Sultan Syarif Kasim Riau
ISSN : -     EISSN : 30309956     DOI : http://dx.doi.org/10.24014/icopss.v2i1.25322
Core Subject : Economy, Health, Science, Education, Social,
Computer Science & IT Economics, Econometrics & Finance Mathematics Public Health Social Sciences
Indonesian Council of Premier Statistical Science (ICoPSS) established in 2022, publishes scientific papers in the area of statistical science and its applications with E-ISSN 3030-9956. The published papers should be research papers with, but not limited to, the following topics: experimental design and analysis, survey methods and analysis, operation research, data mining, statistical modeling, computational statistics, time series and econometrics, and statistics education. All papers were reviewed by peer reviewers consisting of experts and academicians across universities and agencies. Indonesian Council of Premier Statistical Science (ICoPSS) is a double-blind peer-reviewed international journal published by the Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau. Scope: Indonesian Council of Premier Statistical Science is a refereed journal committed to Statistics and its applications.
Arjuna Subject : Ilmu Pengambilan Keputusan (semua kategori) - Statistik, Kemungkinan dan Ketidak Pastian
Articles 5 Documents
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Search results for , issue "Vol 4, No 1 (2025): February 2025" : 5 Documents clear
Prediction of the Amount of Areca Nut Plantation Production in Riau Province Using the Single Exponential Smoothing Zukrianto, Zukrianto; Melka Pratama, Melka Pratama
Indonesian Council of Premier Statistical Science Vol 4, No 1 (2025): February 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v4i1.35598

Abstract

Data in this study was obtained from the Riau Provincial Plantation Office. This study aims to forecast areca nut plantation production in Riau Province in 2023. The data used is data obtained from the Riau Provincial Plantation Office in 2011-2022. Betel seeds are generally grown to be used for their seeds, areca nuts that are traded, especially those that have been dried, in a whole (round), split, and there is also by boiling to get the quality of areca nuts to be exported. Betel seeds have been used since hundreds of years ago as one of the mixtures of people eating betel nut in addition to gambier and lime. In addition, areca nuts are also used as industrial raw materials such as fabric dyes and medicines. The separation of areca nuts is carried out by splitting them using a knife or machete, drying them in the sun, then prying and drying them before selling them to collectors. The purpose of this study is to find out the results of predicting the number of areca nut production in Riau Province and find out the results of forecasting the number of areca nut production in Riau Province in 2023. The method used in this study is Single Exponential Smoothing. Based on the results of data processing from 2011 to 2022, the results of this forecast have a MAPE value with α = 0.7 in 2023 of 7.095371%. The results show that the Single Exponential Smoothing method has a good level of accuracy to predict the amount of areca nut production in Riau Province
Management the Potential Data on Web Site For Communicating Research In Social and environment Issues Yendra, Rado
Indonesian Council of Premier Statistical Science Vol 4, No 1 (2025): February 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v4i1.35853

Abstract

This paper focus on Management the Potential Data On Web Site For Communicating Research In Social and Environment Field with a special focus to produce some research on education and climate issue. It discusses the potentials and challenges of Internet data for social and environmet and presents a selection of the relevant literature to establish the wide spectrum of topics, which can be reached. Such data represent a large and increasing part of everyday life, which cannot be measured otherwise. They are timely, perhaps even daily following the factual process, they typically involve large numbers of observations, and they allow for flexible conceptual forms and experimental settings. In this paper, the data from website be managed to produce some academic article. Internet data can successfully be applied to a very wide range of climate issues including forecasting (e.g. of rainfall, wind speed, and the like)  and detecting education issues  (e.g. spatial analysis for relation a number of male and female students and test score mathematic and foreign lenguages subjects) ,Our article reviews the current attempts in the literature to incorporate Internet data into the mainstream of scholarly empirical research and guides the reader through this Special Issue. We provide some insights and a brief overview of the current state of research.
Modeling of Educated Unemployment Rates in Indonesia Using Geographically Weighted Regression Mufarida, Abdilla; Marizal, Muhammad
Indonesian Council of Premier Statistical Science Vol 4, No 1 (2025): February 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v4i1.35855

Abstract

Modeling the Educated Unemployment Rate (EUR) in Indonesia is an effort to reduce the number of unemployed, especially the unemployed who are able to complete their education at least high school. The SATKERNAS survey stated that more than half of the unemployed in Indonesia in 2022 were educated unemployed. Modeling was generally carried out to see the relationship between independent variables and dependent variables using linear regression analysis and specifically using Geographically Weighted Regression (GWR) analysis to understand significant differences in independent variables between provinces in Indonesia. In accordance with the model goodness criteria, GWR produces a model that has a smaller AIC value than the general model generated by linear regression. From linear regression analysis, EUR in Indonesia in 2022 is influenced by TPT, JPUP, and Investment. In addition to these variables, the PG variable also affects EUR in Aceh, North Sumatra, West Sumatra, and Riau Provinces according to GWR analysis with a fixed gaussian weighting function
Rainfall Forecasting in the City of Pekanbaru Using the Exponential Smoothing Method Rahmadeni, Rahmadeni; Desasri, Rifa; Safitri, Elfira
Indonesian Council of Premier Statistical Science Vol 4, No 1 (2025): February 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v4i1.37475

Abstract

 Rainfall is one of the main components in the climate system that has a major influence on various sectors, such as agriculture, transportation, and disaster management. The city of Pekanbaru in Riau Province has tropical climate characteristics with varying levels of rainfall throughout the year. Therefore, a forecasting method is needed that is able to accurately predict rainfall. This study aims to identify the most suitable forecasting model for rainfall data in the region. The two methods used in this study are Single Exponential Smoothing (SES) and Holt's Double Exponential Smoothing (DES). The results of the analysis showed that the SES method provided a higher level of accuracy compared to the DES Holt method, with a MAPE value of 33.37%. Thus, the SES model is considered the best method in predicting rainfall in the city of Pekanbaru.
Estimation of Hazard Cumulative Function Using the Nelson-Aalen Method on Covid-19 Patient Data in Jember Regency Ramadhani, Hilvania; Pauziah, Rini
Indonesian Council of Premier Statistical Science Vol 4, No 1 (2025): February 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v4i1.37519

Abstract

The Covid-19 pandemic presents a major challenge in the health sector, especially related to understanding patient recovery patterns. This study aims to estimate the cumulative hazard function using the Nelson-Aalen method on the length of treatment data of Covid-19 patients who have recovered in Jember Regency. The Nelson-Aalen method is a non-parametric approach that does not require certain distribution assumptions and is suitable for survival data, especially those subjected to right censorship. In this study, all patient data was complete without sensors. The analysis was performed with R software, resulting in a cumulative hazard curve that showed an increased risk of recovery as the treatment time increased. The results of this study provide an empirical picture of patient recovery patterns and serve as a basis for evaluating health service efficiency and hospital capacity planning during the pandemic. In addition, the application of the Nelson-Aalen method reinforces the contribution of non-parametric statistical methods in epidemiological studies

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