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Muhammad Marizal
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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 6 Documents
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Search results for , issue "Vol 4, No 2 (2025): August 2025" : 6 Documents clear
Application of the Cox Proportional Hazard Model with the Breslow Method in Inpatients with Type 2 Diabetes Mellitus at XYZ Hospital Nuraisyah, Puteri; Zakiyah, Zahra
Indonesian Council of Premier Statistical Science Vol 4, No 2 (2025): August 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v4i2.37878

Abstract

Type 2 Diabetes Mellitus (DMT2) is a chronic disease that is often accompanied by complications such as hypertension, anemia, and infection, which can affect the length of hospitalization and the time of clinical occurrence. The purpose of this study is to analyze the clinical factors that affect the time of occurrence in DMT2 inpatients at XYZ Hospital using the Cox Proportional Hazard regression model using the Breslow method. Secondary data from 33 patients were analyzed by including variables of age, sex, comorbidities, blood pressure, blood sugar levels, diabetes mellitus diet program, diabetic feet, and pain. Results showed that age (p = 0.076), comorbidities (p = 0.058), and pain complaints (p = 0.101) had a significant effect on the time of occurrence, while other variables were insignificant. Thus, the Cox model of the Breslow method has been shown to be effective in identifying risk factors in the clinical data of survival of DMT2 patients
Survival Analysis of Pneumonia Patients Using Kaplan-Meier and Cox Proportional Hazard Regression Putri, Aulia Azira; Ananda, Dian Desti
Indonesian Council of Premier Statistical Science Vol 4, No 2 (2025): August 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v4i2.37879

Abstract

Pneumonia is one of the leading infectious diseases in the world, especially in the elderly and patients with complications. This study aims to analyze the survival of pneumonia patients treated in the ICU room of Hospital X by 2024 using the Kaplan-Meier method and Cox Proportional Hazard regression. The data used consisted of 37 patients, with independent variables in the form of age and type of pneumonia (mildness, sepsis, and other complications), and dependent variables in the form of patient survival time. The Kaplan-Meier analysis showed that patients with >60 years of age and complicated types of pneumonia tended to have lower survival rates. However, the results of the Log-Rank test showed that there was no statistically significant difference between age groups and types of pneumonia on survival (p > 0.05). The results of the Cox regression also showed that no variables had a significant effect on the patient's risk of death, although types of pneumonia with other complications showed a significant tendency at the level of 10% (p = 0.0699). This study demonstrates the importance of considering disease severity in the evaluation of the risk of death of pneumonia patients, as well as the need for further research with larger sample counts and additional clinical variables.
Application of the Cox Proportional Hazard Model on Survival Data of Multiple Myeloma Patients Using the R Application Riyan, Achmad; Nengsih, Surya
Indonesian Council of Premier Statistical Science Vol 4, No 2 (2025): August 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v4i2.37880

Abstract

Multiple Myeloma is a type of blood cancer characterized by the proliferation of malignant plasma cells in the bone marrow and can affect the patient's survival. This study aims to analyze the influence of age, sex, and protein levels on patient survival time. Multiple Myeloma uses the Cox Proportional Hazards model. The data used came from 47 patients with variables of survival time, patient status (dead or alive), age, gender, and protein content. The analysis was carried out using R software. The model match test with the likelihood ratio test also showed insignificant results, but testing of the assumption of proportional hazards through residual Schoenfeld showed that all variables met the model's assumptions. Thus, the Cox PH model in this study is technically valid, but its predictive power is still limited, so further model development is recommended by increasing the amount of data or considering other more relevant variables.
Analysis of the 2022 Shallot Price Grouping in the Areas of Arengka, Cikpuan, Incense, and Suka Ramai Using the Hierarchical Clustering Method Akbar, Muhammad Rizki; Hasibuan, Muhammad Angga Piansyah; Prasetyo, Restu
Indonesian Council of Premier Statistical Science Vol 4, No 2 (2025): August 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v4i2.37870

Abstract

This study aims to analyze the price grouping of shallots in four regions, namely Arengka, Cikpuan, Incense, and Suka Ramai in 2022 using the Hierarchical Clustering method. The data used is secondary data on the price of shallots from each region. The analysis was carried out with a multivariate statistical approach to identify price patterns and form clusters based on price similarities between regions. The results of the study show that the price of shallots in the four regions can be grouped into two main clusters. The first cluster consists of areas with lower shallot prices (<66.67 rupiah), while the second cluster consists of areas with higher prices (>66.67 rupiah). The distance between the clusters of 5.22715 shows a significant difference in price characteristics. These findings provide strategic benefits for industry players and policy makers in optimizing distribution and developing marketing strategies that are more targeted in each region. Overall, the Hierarchical Clustering method has proven to be effective in identifying shallot price patterns and can be used as a basis for data-driven decision-making in the agricultural sector
Survival Analysis of Kidney Failure Patients Using Nelson-Aalen Cumulative Hazard Function Estimation and Log-Rank Test Safitri, Novita Dwi; Jannah, Salwa Zahrotul
Indonesian Council of Premier Statistical Science Vol 4, No 2 (2025): August 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v4i2.37876

Abstract

Kidney failure is a critical condition that requires intensive monitoring of the patient's survival. This study aims to analyze the survival of kidney failure patients using a survival analysis approach with Nelson-Aalen cumulative hazard function estimation and the Log-Rank Test. The data used consisted of 106 patients with kidney failure who underwent hospitalization for 35 days, with characteristic variables including disease severity (chronic or acute), sex, and age group (≤ 50 years or > 50 years). The results of Nelson-Aalen's estimation showed that patients with acute conditions, male and > age 50 years had a visually higher risk of death. However, the results of the Log-Rank test showed that there was no significant difference in survival function between the three categories (p-value > 0.05). These findings suggest that although there are visual indications of a difference in risk, statistically, these clinical characteristics have not had a significant effect on patient survival in the observation period. This study suggests the need for further studies with data and a broader duration of observations to gain a deeper understanding.
Forecasting of Average Air Temperature in the City of Pekanbaru Using the Holt-Winters Method Yendra, Rado; Marizal, Muhammad; Ramadhani, Hilvania
Indonesian Council of Premier Statistical Science Vol 4, No 2 (2025): August 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v4i2.37868

Abstract

Global climate change causes significant fluctuations in air temperature, including in the city of Pekanbaru, therefore, a predictive system is needed that can help the government and the community in dealing with climate impacts, one of which is through air temperature forecasting. This study aims to forecast the average air temperature in Pekanbaru City using the Holt-Winters Exponential Smoothing method, which is known to be effective in capturing seasonal patterns and trends. The data used is monthly average air temperature data from 2017 to 2024 obtained from BMKG. The analysis was carried out using an addictive approach and model evaluation was carried out based on the Mean Absolute Percentage Error (MAPE) value. The results show that the best model is obtained on a parameter with a MAPE value of 2.684. This model is then used to forecast the air temperature in 2025, which is predicted to decrease gradually. The results of this forecast are expected to be a reference in planning and decision-making related to climate change mitigation in the Pekanbaru area

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