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All Journal Jurnal Online Mahasiswa (JOM) Bidang Matematika dan Ilmu Pengetahuan Alam BAREKENG: Jurnal Ilmu Matematika dan Terapan Al-Muqayyad Operations Research: International Conference Series International Journal of Global Operations Research International Journal of Quantitative Research and Modeling International Journal of Mathematics, Statistics, and Computing
Haposan Sirait
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Transportation

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Journal : Operations Research: International Conference Series

 1 
Comparison of K-Medoids and Clara Algorithm in Poverty Clustering Analysis in Indonesia Ardini, Ananda Rizki Dwi; Sirait, Haposan
Operations Research: International Conference Series Vol. 4 No. 4 (2023): Operations Research International Conference Series (ORICS), December 2023
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v4i4.279

Abstract

The Covid-19 pandemic entered Indonesia in March 2020, so the government imposed restrictions on people's movement in various regencies. The imposition of restrictions on people's movement will have an impact on the economy to the point of poverty. Poverty is influenced by several factors such as population, health, education, employment and economic factors. The poverty of a district/city in Indonesia is grouped to assist the government in alleviating poverty more efficiently. The process of grouping data in data mining is to group districts/cities in Indonesia based on factors that affect poverty with the K-Medoids and CLARA algorithms, then compare the two methods based on the average value of the ratio of the standard deviations. The variables used in this study consist of 4 variables, namely human development index (HDI), gross regional domestic product (GRDP), unemployment rate, and population density. The results of this study indicate that using the K-Medoids obtained 2 clusters, while using the CLARA algorithm obtained 3 clusters. Based on the results of grouping the two algorithms, the best algorithm was obtained using cluster validation, namely the CLARA algorithm because it has the average value of the ratio of the smallest standard deviation of 0.106. 
Analysis of Risk Factors for Dengue Hemorrhagic Fever in Riau Province using Negative Binomial Regression Rangkuti, Aisyah Azhari; Sirait, Haposan
Operations Research: International Conference Series Vol. 4 No. 4 (2023): Operations Research International Conference Series (ORICS), December 2023
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v4i4.280

Abstract

Dengue Hemorrhagic Fever (DHF) is a serious threat in Riau province, Indonesia. To better understand and control the spread of dengue fever, this research aims to analyze the factors that cause dengue fever. This study aims to identify significant risk factors that influence the spread of dengue fever in Riau Province. The Negative Binomial Regression Method was used to identify factors associated with the increase in dengue fever cases in Riau. The variables evaluated include population density of the Aedes aegypti vector , level of environmental cleanliness, prevention practices, and socio-economic factors. In addition, the best model was selected to overcome overdispersion in the data. The results of the analysis show that factors such as population density of the Aedes aegypti vector , environmental cleanliness, and the level of public understanding about dengue prevention practices have a significant influence on the spread of dengue fever in Riau. The best model used to overcome overdispersion in the 2021 dengue fever case data in Riau is Negative Binomial Regression. This research provides a deeper understanding of the factors causing dengue fever in Riau and selects an appropriate statistical model for analyzing data that experiences overdispersion. Negative Binomial Regression proved to be more appropriate in overcoming the problem of overdispersion in the data. These results can be used as a basis for designing more effective dengue prevention and control strategies and provide guidance for more targeted interventions in fighting dengue fever in this region.
Estimation of the Three-Parameter Inverse Rayleigh Distribution Parameters for Guinea Pig Survival Data Faradila, Eky; Utari, Farah Asyifa; Zahra, Lathifah; Novitasari, Ratna; Astuti, Syaftiani Dwi; Sirait, Haposan
Operations Research: International Conference Series Vol. 6 No. 2 (2025): Operations Research International Conference Series (ORICS), June 2025
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v6i2.384

Abstract

The Generalized Transmuted Inverse Rayleigh Function (GTIR) distribution is an extension of the inverse Rayleigh distribution, which is commonly used to model reliability and survival data. By incorporating an additional shape parameter (α) and a transmutation parameter (λ) alongside the scale parameter (σ), this distribution offers greater flexibility in handling skewed data or data with a non-monotonic hazard function. The parameters of the GTIR distribution are estimated using the Maximum Likelihood Estimation (MLE) method; however, they must be solved implicitly through numerical procedures. In this study, the GTIR distribution was employed to analyze the survival data of guinea pigs infected with tuberculosis. The primary objective of this analysis was to estimate the distribution parameters and to provide an overview of the survival pattern. The application of the GTIR distribution to the survival and hazard functions demonstrated that guinea pigs experience a sharp decline in survival probability at the onset of tuberculosis infection, followed by a gradual decrease in the risk of mortality over time. The hazard rate pattern, which initially increases and then decreases, indicates that the most critical period occurs immediately after infection. Parameter estimation of the GTIR distribution using the MLE approach yielded estimates of λ = 0.781, α = 10.135, and σ = 12.319, confirming that this model effectively captures the complex survival pattern with high accuracy.
Co-Authors Anne Mudya Yolanda Ardini, Ananda Rizki Dwi Arisman Adnan Asti Ralita Sari Astuti, Syaftiani Dwi Aulia, Sartika Mega Endang Lily Faradila, Eky fitriani, selvi Gunawan, Chairani Hasriati Hasriati, Hasriati Irfansyah Irfansyah Nayunda, Voundri Nindia nurnisaa Okta Bella Syuhada Pradana, Visca Nadia Putri, Viona Sephia Rahmad Ramadhan Laska Rangkuti, Aisyah Azhari Ratna Novitasari Ratni Dakhyu Riko Febrian Rimisti, Pragista Rustam Efendi Rustam Efendi Salih, Yasir Sanurtillah, Farissa Sri Agustina, Sri Triyuni, Meisy Utari, Farah Asyifa Zahra, Lathifah