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Published by Universitas Mulawarman

ISSN : 20857829 EISSN : 27983455 DOI : https://doi.org/10.30872/

Core Subject : Economy, Science, Education,

Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

Jurnal Eksponensial is a scientific journal that publishes articles of statistics and its application. This journal This journal is intended for researchers and readers who are interested of statistics and its applications.

Arjuna Subject : Matematika - Statistik dan Probabilitas

Articles 12 Documents

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Search results for , issue "Vol. 13 No. 1 (2022)" : 12 Documents clear

Model Regresi Hazard Rate Weibull Kesembuhan Pasien Rawat Inap Demam Berdarah Dengue (DBD) Di RSUD Panglima Sebaya Tanah Grogot Fajriati, Nur Ainun; Suyitno, Suyitno; Wasono, Wasono
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Univariate Weibull Regression (RWU) is a regression model development of the Univariate Weibull distribution, where the scale parameters is expressed in terms of the regression parameters. Univariate Weibull Regression Models discussed in this study are Weibull survival regression and the Weibull hazard regression model. Weibull regression models in this study was applied to lifetime data containing the right censored data for Dengue Hemorrhagic Fever (DHF) inpatients at the Regional General Hospital (RSUD) Panglima Sebaya Tanah Grogot, Paser Regency, Kalimatan Tinur. The purpose of this study was to obtain a Weibull regression model and to determine the factors that affect the patients is survive (have not recovery) and the recovery rate of DHF patients. The parameter estimation is the Maximum Likelihood Estimation (MLE) which is solved by using the Newton-Raphson iterative method. The study conclude that the factors influencing the patients is survive (have not recovery) and the recovery rate of DHF patients at RSUD Panglima Sebaya Tanah Grogot were platelets and leucocytes.

Klasifikasi Status Pembayaran Kredit Barang Elektronik dan Furniture Menggunakan Support Vector Machine Casuarina, Indah Putri; Hayati, Memi Nor; Prangga, Surya
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Classification is the process of finding a model or function that can describe and differentiate data into classes. One application of classification is Support Vector Machine (SVM). SVM is a learning system that uses a hypothetical space in the form of linear functions in a high-dimensional feature space, trained with a learning algorithm based on optimization theory by implementing machine learning derived from statistical learning theory. The concept of classification with SVM is to find the best hyperplane to separate the two data classes and use a support vector approach. This study uses the proportion of the distribution of training data and testing data, namely 50%:50%, 70%:30%, 90%:10% and uses the SVM algorithm Polynomial kernel function with parameters =0.01, r=0.5, d =2, and C=1. This study aims to determine the results of the classification of the credit payment status of electronic goods and furniture and the level of classification accuracy in the SVM method. The data used is the debtor data of PT. KB Finansia Multi Finance Bontang in 2020 as many as 133 data with current and non-current credit payment status and using 7 independent variables, namely age, number of dependents, length of stay, income, years of service, large credit payments, and length of credit borrowing. The results of the SVM classification show an average accuracy value of 72.25% and the best accuracy chosen is the proportion of training data distribution and testing data 90%:10%, which is 84.62%.

Analisis Regresi Logistik Multinomial Bayes untuk Pemodelan Minat Peserta Didik MAN 2 Samarinda Tahun Ajaran 2018/2019 Cahyani, Era Tri; Goejantoro, Rito; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Currently, Senior High School and Madrasah Aliyah have implemented student specialization. The specialization includes Natural Science, Social Science and Language. There are several criteria for determining interest in Senior High School and Madrasah Aliyah which include academic scores, student interests and IQ. The multinomial logistic regression model is used to examine these factors because the dependent variable has more than 2 categories. Bayes method is used to estimate the parameters of the multinomial logistic regression. The Bayesian method is a parameter estimation technique that combines the likelihood and prior distribution function. The estimation with the Bayesian method was solved using Markov Chain Monte Carlo simulation (MCMC) with the Gibbs Sampler algorithm. The data used were new students at MAN 2 Samarinda on 2018/2019 with the results of interest namely Natural Science, Social Science and Language. Independent variables were used, namely the score of the Junior High School in subjects Natural Science, Social Science, Language and the rate of National Test. The results of modeling and analysis showed that the factors that significantly influenced were the score of the junior high school in the subject of Natural Science and the rate of National Test. The classification accuracy of the model was 63,10%.

Pemodelan Harga Saham PT. Telekomunikasi Indonesia Tbk Menggunakan Model TSR Linier Ramadani, Kartika; Wahyuningsih, Sri; Hayati, Memi Nor
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

The movement of the stock price of PT. Telekomunikasi Indonesia Tbk from time to time is relatively erratic, but in 2020 the movement shows an decreasing trend pattern in January-October and an increasing trend pattern in November-December. There needs a stock price modeling for PT. Telekomunikasi Indonesia Tbk which is useful for investors as a consideration in making decisions to invest. In this study, modeling the stock price of PT. Telekomunikasi Indonesia Tbk uses a Time Series Regression (TSR) Linear model. The results of this study obtained a model for the proportion of data in sample 90, a model for the proportion of data in sample 80, and a model for the proportion of data in sample 70. It was found that the residual value of the TSR linear model the white noise assumption and normally distributed is not valid, so it can be concluded that TSR Linear model has not been able to understand all information on stock price data of PT. Telekomunikasi Indonesia Tbk.

Regresi Binomial Negatif untuk Memodelkan Kematian Bayi di Kalimantan Timur Fathurahman, M
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Negative Binomial Regression (NBR) is an alternative regression model to model the relationship between the dependent variable in overdispersion count data and one or more independent variables. Overdispersion is a problem in Poisson regression modeling. Namely, the variance of the dependent variable is more than the mean. If there is overdispersion, then the parameter estimator of the Poisson regression model has a standard error value that is not under-estimated. The NBR model was applied to modeling infant mortality in East Kalimantan in 2019. Data on infant mortality in East Kalimantan in 2019 indicated overdispersion. Infant mortality is an indicator that can measure the progress of development outcomes in the health sector in a region. In the last three years, from 2017 to 2019, infant mortality data in East Kalimantan has increased. Therefore, it is necessary to do modeling to get the factors that cause it. The modeling results with NBR show that the percentage of the complete neonatal visit of KN3, the percentage of infant health services, and the percentage of visits by pregnant women K4 significantly affect infant mortality in East Kalimantan in 2019.

Perbandingan Hasil Analisis Cluster Dengan Menggunakan Metode Average Linkage Dan Metode Ward: Studi Kasus : Kemiskinan Di Provinsi Kalimantan Timur Tahun 2018 Imasdiani, Imasdiani; Purnamasari, Ika; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Hierarchical cluster analysis is an analysis used to classify data based on its characteristics. The average linkage method and the Ward method are methods of hierarchical cluster analysis. Grouping data from various aspects, one of which is poverty. This study uses poverty indicator data in East Kalimantan in 2018. The average linkage method is based on the average distance size, while the Ward method is based on the size of the distance between clusters by minimizing the number of squares. The purpose of this study was to determine the best method based on the average value of the standard deviation ratio. The results of the study using the average linkage method obtained two clusters, both the average linkage method and the Ward method both obtained two clusters. Where in the average linkage method, the first cluster consists of 7 districts / cities and the second cluster consists of 3 districts / cities. Whereas in the Ward method, the first cluster consists of 6 districts / cities and the second cluster consists of 4 districts / cities. For the best method based on the average standard deviation ratio in groups (Sw) and the standard deviation between groups (Sb), it is found that the ratio in the Ward method is smaller than the average linkage method, which is 2,681 which indicates that the average linkage method is the best method.

Optimasi Parameter Pemulusan Pada Metode Peramalan Double Exponential Smoothing Holt Menggunakan Golden Section: Studi Kasus : NTPT Provinsi Kalimantan Timur Tahun 2014-2019 Yani, Tika Anggre Ria; Wahyuningsih, Sri; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Double Exponential Smoothing Holt (DES Holt) is a method that can be used when the data pattern shows a trend pattern. Determination of smoothing parameters usually uses trial and error, but this method still has inefficient results to get the best accuracy. One method that can be used to determine the smoothing parameters value is the golden section method. The application of the DES Holt and golden section methods will be carried out to predict the Exchange Rate of Farmers Subsector Livestock (ERFSL) of East Kalimantan Province. The purpose of this study was to obtain forecasting results and the level of accuracy of the ERFSL of East Kalimantan Province for the period January, February, and March 2020 using the DES Holt methods with the golden section smoothing parameter optimization method. The Forecasting results of DES Holt method have increased in the next three periods with an accuracy rate of 0.8856663%. The level of accuracy of forecasting results using the DES Holt methods has a MAPE value of less than 10%, which means the methods very good for predicting the ERFSL of East Kalimantan Province.

Aplikasi K-Nearest Neighbor Dengan Fungsi Jarak Gower Dalam Klasifikasi Kelulusan Mahasiswa: Studi Kasus : Mahasiswa Program Studi Statistika, Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Mulawarman Fadil, Irfan; Goejantoro, Rito; Prangga, Surya
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

The results of the reaccreditation of the Statistics Study Program, Mulawarman University in 2019 remain accredited B. One of the assessment indicators used in reaccreditation is the student's timely graduation status. Therefore, it is necessary to predict the graduation status of Statistics students, Mulawarman University.. The prediction method used in this research is K-Nearest Neighbor (K-NN). K-NN is a classification method based on studying previously classified data. This method is very easy to understand, easy to applied and also non-parametric method, so that no certain assumptions are needed in the process. The independent variables used in this study were student profiles, including gender, regional origin, cumulative Grade Point Average (GPA) and single tuition fee. The dependent variable in this study is the graduation status of students, namely graduating on time and not graduating on time. The data used were students of the Mulawarman University, Statistics Study Program in 2014, 2015, and 2016. The results showed at k = 7 and the distribution of training and testing data with the proportion of 80:20 obtained optimal accuracy of 0,909 with a TPrate of 0.500, a TNrate. in the amount of 1,000 and AUC value of 0,75 that means fair classification.

Penerapan Metode Klasifikasi Chi-Square Automatic Interaction Detection dan Exhaustive Chi-Square Automatic Interaction Detection: Studi Kasus: Data Masa Studi Mahasiswa Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Mulawarman Nurhasanah, Nurhasanah; Goejantoro, Rito; Suyitno, Suyitno
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

The Chi-Square Automatic Interaction Detection (CHAID) and Exhaustive CHAID methods are nonparametric statistical methods that can be used to classify. CHAID and Exhaustive CHAID were used to determine the significant relationship between the dependent variable and the independent variables based on the chi-square independence test. This study was applied to data on the study period of students of FMIPA UNMUL batch 2014. Based on the CHAID and Exhaustive CHIAD methods, it can be seen that the dependent variable of the study period has a significant relationship with the independent variable, namely the study program and GPA predicate. Where students who graduated on time for the Statistics, Biology and Chemistry study program with a satisfactory GPA predicate of 82 students and with a very satisfactory GPA predicate and cum laude with 46 students. Meanwhile, students who did not graduate on time for the Statistics, Biology and Chemistry study program with an adequate GPA predicate of 5 students, a satisfactory GPA predicate of 41 students, very satisfactory and cum laude with 3 students. Students who graduated on time for the Physics study program were 13 students and those who did not graduate on time were 34 students. The chi-square independence test performed on the CHAID method uses fewer possible categorical pairs than the Exhaustive CHAID method which uses all possible categorical pairs so that it requires a long computational and calculation time.

Perbandingan Metode K-Means Dan Metode Fuzzy C-Means (FCM) Pada Analisis Kinerja Pegawai PT. Cemara Khatulistiwa Persada Bontang Rakhmawaty, Nurul; Nasution, Yuki Novia; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Discipline assessment of employee performance is one of the factors to improve the situation of the quality of human resources. Monitoring and assessment of employee discipline must be carried out continuously as some of the characteristics of management that have gone well as a benchmark for considering the targets that have been set. The K-Means method and the Fuzzy C-Means (FCM) method are non-hierarchical cluster methods. Both methods attempt to partition data into one or more clusters, so that data with the same characteristics are grouped into the same cluster or groups and data with different characteristics are grouped into other groups. This study discusses the comparison of the K-Means method and the Fuzzy C-Means (FCM) method in analyzing employee performance at PT. Cemara Khatulistiwa Persada Bontang, where groups of employees with high, medium, and low levels of employee performance will be determined based on the clustering results of the two methods and determine the best method. The grouping of data for the two methods was obtained from employee attendance data in 2020. Based on the results, it was found that clustering using the K-Means method in the first cluster (low performance level) had 23 employees, the second cluster (medium performance level) had 27 employees, and cluster the third (high performance level) there are 30 employees. Then based on the results of clustering using the FCM method in the first cluster (medium performance level) there are 26 employees, the second cluster (high performance level) there are 31 employees, and the third cluster (low performance level) there are 23 employees. Based on the results of the standard deviation ratio, it was obtained that the K-Means method with a value of 2.4944 was better than the FCM method with a value of 2.7323 in clustering employee performance at PT. Cemara Khatulistiwa Persada.

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Meiliyani Siringoringo

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Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Mulawarman Jl. Barong Tongkok, Kampus Gunung Kelua Kota Samarinda, Provinsi Kalimantan Timur 75123

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Kota samarinda,

Kalimantan timur

INDONESIA

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Meiliyani Siringoringo

Contact Email meiliyanisiringoringo@fmipa.unmul.ac.id

Phone +6285250326564

Journal Mail Official eksponensial@fmipa.unmul.ac.id

Editorial Address Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Mulawarman Jl. Barong Tongkok, Kampus Gunung Kelua Kota Samarinda, Provinsi Kalimantan Timur 75123

Location Kota samarinda, Kalimantan timur INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

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Abstract

Abstract

Abstract

Contact Name
Meiliyani Siringoringo

Contact Email
meiliyanisiringoringo@fmipa.unmul.ac.id

Phone
+6285250326564

Journal Mail Official
eksponensial@fmipa.unmul.ac.id

Editorial Address
Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Mulawarman Jl. Barong Tongkok, Kampus Gunung Kelua Kota Samarinda, Provinsi Kalimantan Timur 75123

Location
Kota samarinda,

Kalimantan timur

INDONESIA