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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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PENAKSIR RASIO REGRESI LINEAR YANG EFISIEN UNTUK RATA-RATA POPULASI PADA SAMPLING ACAK SEDERHANA Dakhyu, Ratni; Efendi, Rustam; Sirait, Haposan
Jurnal Online Mahasiswa (JOM) Bidang Matematika dan Ilmu Pengetahuan Alam Vol 2, No 1 (2015): Wisuda Februari 2015
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In this article we study three linear regression ratio estimators for the population mean on simple random. This study is a review of the article of Singh, et. al [Statistics in Transition-new series, 10: 85-100]. Each estimator is a biased estimator and their mean square errors are determined. Estimator with the smallest mean square error is the most efficient one. An example is given at the end of the discussion.
Analisis Komponen Utama dan Biplot untuk Mereduksi Faktor Inflasi Berdasarkan Indeks Harga Konsumen Anne Mudya Yolanda; Arisman Adnan; Rustam Efendi; Haposan Sirait; Irfansyah Irfansyah; Okta Bella Syuhada; Rahmad Ramadhan Laska; Riko Febrian
AL-Muqayyad Vol. 5 No. 2 (2022): Al-Muqayyad
Publisher : STAI Auliaurrasyidin Tembilahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46963/jam.v5i2.766

Abstract

Inflation of a region can be measured from the Consumer Price Index (CPI) by spending group. The aim is to look at the factors that influence monthly inflation based on the CPI for 2021. Principal Component Analysis is used to reduce the expenditure group variables in the CPI, followed by biplot analysis to display the visualization of the first two main components of the PCA in a two-dimensional graph. The results of the main component analysis, (1) the primary expenditure component consists of housing, water, electricity and household fuel variables; equipment, tools and household routine maintenance; transportation; information, communication and financial services; recreation, sports and culture, (2) secondary expenditure components include food, drink and tobacco variables; health; education; general, and (3) complementary expenditure components, namely clothing and footwear variables; personal equipment and other services. These three components simultaneously can represent 88.1% of the diversity of the data. Biplot analysis succeeded in describing the similarity and position of the variables with a total variance of 75%
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.
Premium Sufficiency Reserve of Last Survivor Endowment Life Insurance Using Exponentiated Gumbel Distribution Putri, Viona Sephia; Sirait, Haposan
International Journal of Global Operations Research Vol. 6 No. 1 (2025): International Journal of Global Operations Research (IJGOR)
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i1.365

Abstract

Life insurance is a protection effort provided by the insurer against risks to the insured’s life that will arise from an unpredictable event. Insurance companies are required to prepare reserves to fulfill the sum insured when a claim occurs. Premium sufficiency reserves are modified reserves whose calculations use gross premiums that contain administrative maintenance costs. The purpose of this study is to determine the amount of premium sufficiency reserves of endowment life insurance for two insurance participants aged x years and y years using the exponentiated Gumbel distribution. The parameters of the exponentiated Gumbel distribution are estimated using the maximum likelihood method and then determined by a Newton-Raphson iteration method. The solution of the problem is obtained by determining the initial life annuity term, single premium, and annual premium so as to obtain the reserve formula of the premium sufficiency of the last survivor status endowment life insurance using the exponentiated Gumbel distribution. The results of the calculation of reserves premium sufficiency of endowment life insurance last survivor status using the exponentiated Gumbel distribution is slightly smaller than premium sufficiency reserve for endowment life insurance using the Indonesian Mortality Table 2019.
Analysis Of Unemployment Clusters In Indonesia Using The Self Organizing MAP Method: Analysis Of Unemployment Clusters In Indonesia Using The Self Organizing MAP Method Gunawan, Chairani; Sirait, Haposan
International Journal of Mathematics, Statistics, and Computing Vol. 1 No. 3 (2023): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v1i3.4

Abstract

Unemployment is a situation where someone is not working or is trying to find a job but unable to find work. The spread of unemployment in Indonesia has different characteristics in each region, so it is necessary to classify the unemployed so that each government policy program can be carried out in a more focused and directed manner. This study discusses cluster analysis of unemployment using the Self Organizing Map (SOM) method in classifying the unemployed in Indonesia in 2020. The SOM method is able to show dominant patterns and variables in clusters. The variables used in this study consisted of school enrollment rates, average length of schooling, labor force participation rates, and the percentage of the population using computers. The results of this study formed 3 unemployment rate clusters with cluster 1 being a low unemployment group consisting of 144 districts/cities, cluster 2 with a medium level consisting of 287 districts/cities, and cluster 3 with a high level consisting of 83 districts/cities. The grouping using the SOM method on district/city unemployment data in Indonesia is good because it has a minimum standard deviation ratio of 0.529.
Modeling Life Expectation of Population in Sumatra Island Using Durbin Spatial Model Analysis: Modeling Life Expectation of Population in Sumatra Island Using Durbin Spatial Model Analysis Aulia, Sartika Mega; Sirait, Haposan
International Journal of Mathematics, Statistics, and Computing Vol. 1 No. 3 (2023): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v1i3.7

Abstract

Life expectancy is an indicator in measuring government performance in improving the level of health and well-being of the population of a region. While life expectancy on Sumatra Island has variable values, the quality of public health is less uniform. These variations can be caused by several factors such as education, health services, and economic conditions. Therefore, the government needs to provide further treatment on Sumatra Island by identifying factors that affect life expectancy. The study used a Spatial Durbin Model with Queen Contiguity and Rook Contiguity 154 districts/cities on the island of Sumatra. The variables used in the study were data on life expectancy, average school age, the percentage of many infants who received complete immunization, the percent of households having access to decent drinking water, the proportion of the poor population, the regional gross domestic product in 2021. The results of the study showed that the best model was the model that used the Queen Contiguity weighing matrix because it had a smaller AIC value of 365.22. Factors influencing the life expectancy of the population of Sumatra Island in 2021 using the durbin spatial model with the Queen weigher are the average age of school, the percentage of the poor population, and the regional gross domestic product.
Determining Customer Preferences in Choosing a Marketplace Using the Conjoint Analysis Method Agustina, Sri; Sirait, Haposan; Salih, Yasir
International Journal of Mathematics, Statistics, and Computing Vol. 1 No. 4 (2023): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v1i4.39

Abstract

The number of online shopping transactions in Indonesia has grown over the last ten years by 17% and the total number of e-commerce businesses has reached 26.2 million units. This creates competition that requires companies to maintain their existence by understanding consumer psychology. This study aims to determine the combination of attribute levels that are most preferred by consumers in choosing a marketplace as a place to shop by using conjoint analysis. The data used is the result of a survey of Riau University students in the form of a questionnaire. The results of the conjoint analysis in this study show that the level of each attribute that respondents prefer is the display of full color applications and images, free shipping promotions on a certain amount of purchase, the method of paying Cash On Delivery (COD), using J&T/JNE/Sicepat/Tiki/Pos delivery services, and product reviews are available in the form of photos and videos.
Bayes Estimator for Dagum Distribution Parameters Using Non-Informative Prior Rules with K-Loss Function and Entropy Loss Function Asti Ralita Sari; Sirait, Haposan
International Journal of Mathematics, Statistics, and Computing Vol. 1 No. 4 (2023): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v1i4.59

Abstract

The parameter estimator discussed is the p parameter estimator of the Dagum distribution with the K-loss function and the entropy loss function using the Bayes method. To get the Bayes estimator from the scale parameter of the Dagum distribution, the Jeffrey non-informative prior distribution is used based on the maximum likelihood function and the loss function for the K-loss function and the entropy loss function to obtain an efficient estimator. Determination of the best estimator is done by comparing the variance values generated from each estimator. An estimator that uses the entropy loss function is the best method for estimating the parameters of the Dagum distribution of the data population with efficient conditions met.
Factors Affecting Cases of Dengue Hemorrhagic Fever in Riau Province fitriani, selvi; sirait, haposan; nurnisaa
International Journal of Mathematics, Statistics, and Computing Vol. 2 No. 1 (2024): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v2i1.66

Abstract

One infectious disease that has a high morbidity and mortality rate is Dengue Hemorrhagic Fever. Dengue Hemorrhagic Fever (DHF) is a disease caused by the dengue virus transmitted through the bite of the Aedes aegypti mosquito. In general, the habitat of Aedes mosquitoes is in areas with tropical climates, high rainfall, and hot and humid temperatures. The number of patients and the area of distribution are increasing along with increasing mobility and population density. Improper sanitation can also be a cause of DHF. In Riau province, dengue cases in 2022 continue to increase compared to 2021, with the most cases in Pekanbaru City. This study was conducted to see the factors that influence dengue cases in Riau Province. Using multiple linear regression can measure what factors affect the number of dengue cases. From the results, it was found that population density and sanitation had a significant effect on dengue cases in Riau Province. And judging from the coefficient of determination, it can be interpreted that the variables of population density (X1) and sanitation (X2) simultaneously affect the variable of dengue cases (Y) by 77.8%. While the remaining 22.2% was influenced by other variables that were not studied in this study
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