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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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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 2 Documents
 1 
Search results for , issue "Vol 5, No 1 (2026): February 2026" : 2 Documents clear
Application of the Holt-Winters Multiplicative Method to Predict Cayenne Pepper Production in Riau Province Safitri, Elfira; Karimah, Isfi Zainul; -, Rahmadeni; Faizal, Ahmad
Indonesian Council of Premier Statistical Science Vol 5, No 1 (2026): February 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v5i1.38466

Abstract

Production forecasting in the agricultural sector is an important component in the planning and decision-making process, especially for commodities with high levels of demand such as cayenne pepper. This study aims to predict the amount of cayenne pepper production in Riau Province by applying the Holt-Winters Multiplicative method. The selection of this method is based on its ability to process time series data that has seasonal patterns and trends that change proportionally to the data level. The data used in this study is secondary data sourced from the Riau Province Food, Food Crops, and Horticulture Office for the period from 2022 to 2024. The analysis stage includes data exploration, determination of the initial smoothing value for levels, trends, and seasonality, adjustment of smoothing parameters (α, β, γ), and evaluation of model performance using MAPE, MAD, and MSE error measures. Based on the results of the analysis, it shows that the Holt-Winters Multiplicative model produces a MAPE value of 11%, which indicates that the model has a good level of accuracy. Therefore, this method can be used as a supporting tool in planning the production and distribution of cayenne pepper in Riau Province.
New Opportunity Model Using a Mix of 7 and 8 Gamma Chance Density Functions Yendra, Rado
Indonesian Council of Premier Statistical Science Vol 5, No 1 (2026): February 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v5i1.39109

Abstract

This research is based on the limitations of classic distributions such as the Normal, Exponential, and Weibull in modeling real-world data with skewness, heavy tails, multimodality, or hazard rate structures, and thus it is necessary to develop a new, more flexible opportunity model. The main objective of this study is to develop a new probability model with a single parameter using a mixed-method approach that specifically employs the 7- and 8 Gamma chance density functions. The research methodology includes the reduction of important characteristics such as the cumulative distribution function, survival function, and hazard function, as well as the use of the Maximum Likelihood method with Newton–Raphson numerical iteration for parameter estimation. The results of this study are expected to prove that the single-parameter 7 and 8 Gamma mixed models have superior performance compared to the previously existing single-parameter models. The contributions of this research include theoretical development in statistical science and the provision of efficient, parsimonious alternative models for application in fields such as biostatistics, finance, and reliability analysis

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