cover
Article Per Year (5 Year)
All
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
Eksponensial
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 205 Documents
 2   3   4   5   6 
Klasifikasi Rumah Tangga Miskin Di Kecamatan Kaubun Tahun 2020 Dengan Menggunakan Metode Improved Chi-Square Automatic Interaction Detection Yuliasari, Pratiwi Dwi; Goejantoro, Rito; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol. 12 No. 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (739.305 KB) | DOI: 10.30872/eksponensial.v12i1.765

Abstract

Classification was grouping samples based on similarities and differences by using categorical variables. The classification method used in this study was the Improved Chi-square Automatic Interaction Detection (I-CHAID) which was an improvement from the CHAID method. This research aims to provide an overview of poor households and classify poor households, and to compare the accuracy of the classification results for each data proportion. The data used is household data in Kaubun District in 2020 with poor and non-poor status. The results of this study indicate that there were 10 households with poor status, and households were classified as poor if the frequency of eating is less than 3 times a day, and the best classification accuracy results use the proportion of training data of 60% and testing data of 40%.
Model-Model Regresi Weibull Univariat pada Indikator Pencemaran Air Dissolved Oxygen di Daerah Aliran Sungai Lingkungan Hutan Hujan Tropis Kalimantan Timur Chairina, Puspa; Suyitno, Suyitno; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 11 No. 1 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (897.131 KB) | DOI: 10.30872/eksponensial.v11i1.641

Abstract

A Univariate Weibull Regression is a model of regression developed from univariate Weibull distribution with the parameter scale is stated in parameter regression. There are some of univariate Weibull regression model, namely Weibull survival regression, Weibull hazard regression and mean model. Univariate Weibull regression model in this research is applied to the water pollution indicator dissolved oxygen (DO data at Mahakam river in East Kalimantan. The purpose of this study is to find out the model of univariate Weibull regression based on the parameter estimation by using maximum likelihood estimation method (MLE) and to find out the factors which affect to univariate Weibull regression in Mahakam river. The result shows that the closed form of the maximum likelihood estimator can not be found analytically, and it can be approximed by using the Newton-Raphson iterative method. Based on the result of partial hypothesis test for all the parameter regression, it was found that detergent concentration and nitrate concentration had significant influence to the DO in the water of Mahakam river.
Aplikasi Classification and Regression Tree (CART) dan Regresi Logistik Ordinal dalam Bidang Pendididikan David Siahaan; Sri Wahyuningsih; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (233.875 KB)

Abstract

CART method is a nonparametric statistical methods which is for obtaining accurate data group in the classification analysis. CART main goal is to get an accurate data as a group identifier of a classification. CART can be applied in three main steps, namely the establishment of a classification tree, trimming the classification tree, and determination of optimal classification tree. Ordinal logistic regression is a statistical method for analysis response variables that have an ordinal scale consisting of three or more categories. Predictor variables that can be included in the model can be either continuous or categorical data consisting of two or more variables. This study wanted to know the classification results FMIPA UNMUL predicate graduation, the main factor that affect the predicate graduation FMIPA UNMUL who graduated in 2014, and a comparison of the accuracy of the classification results between CART and ordinal logistic regression. The results showed that gender (X1), region origin (X2), major (X3), the status of secondary school (X4), and duration of the study period (X5) is the primary identifier graduation predicate FMIPA UNMUL, whereas gender (X1 ) and duration of the study period (X5) is a factor that affects the predicate graduation. Ordinal logistic regression model was able to predict with 65% accuracy, while the CART method has a predictive accuracy of 54.9%
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (558.5 KB) | DOI: 10.30872/eksponensial.v13i1.887

Abstract

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 Faktor-Faktor yang Mempengaruhi Jumlah Kasus Tuberkulosis di Indonesia Menggunakan Model Geographically Weighted Poisson Regression Karima, Nabila Al; Suyitno, Suyitno; Hayati, Memi Nor
EKSPONENSIAL Vol. 12 No. 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (711.443 KB) | DOI: 10.30872/eksponensial.v12i1.754

Abstract

Tuberculosis is a contagious disease suffered by humans caused by mycobacterium tuberculosis bacteria. Tuberculosis in Indonesia must be eradicated both preventive and treatment. One effort that can be given to the community to reduce tuberculosis cases is by providing information on the factors that influence tuberculosis cases through Geographically Weighted Poisson Regression (GWPR) modeling. The number of tuberculosis cases in Indonesia is a count data with a small chance of occurrence so that it is suspected to have a Poisson distribution. Cases of tuberculosis are spatial data (spatial heterogeneity). The purpose of this study is to determine the GWPR model of the number of tuberculosis cases in Indonesia and determine the factors that influence tuberculosis cases in Indonesia. The research data are secondary data obtained from the Indonesian Ministry of Health. Parameter estimation method is Maximum Likelihood Estimation (MLE). Spatial weighting is calculated by using the Adaptive Gaussian weighting function and the optimum bandwidth is determined by using the Cross-Validation (CV) criteria. The research results showed that the exact Maximum Likelihood (ML) estimator could not be obtained analytically and the approximation of ML estimator was obtained by using the Newton-Raphson iterative method. Based on the results of the parameter testing of GWPR model, it was concluded that the factors affecting the number of tuberculosis cases were local and varied in 34 provinces. The factor affecting locally are the number of poor people, the percentage of houses unfit for habitation, the percentage of districts/cities that do not have a PHBS policy and the percentage of TPM not meeting health requirements, meanwhile factors influencing globally are the number of poor people.
Perbandingan Klasifikasi Metode Naive Bayes dan Metode Decision Tree Algoritma (J48) pada Pasien Penderita Penyakit Stroke di RSUD Abdul Wahab Sjahranie Samarinda Irene Lishania; Rito Goejantoro; Yuki Novia Nasution
EKSPONENSIAL Vol 10 No 2 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (554.727 KB)

Abstract

Classification is a technique to form a model of the data that has not been classified, then the model can be used to classify new data. Naive Bayes is a classification using probability method based on the Bayes theorem with a strong assumption of independence. The decision tree algorithm (J48) is an implementation of the algorithm (C4.5) that produces decision trees. In this research, will be compared the results of classification accuracy with the naive Bayes method and the decision tree algorithm (J48) in stroke patients. That is, a person who has stroke will be classified by using the data of patients in Abdul Wahab Sjahranie Samarinda Hospital with 7 factors, namely age, gender, blood pressure, diabetes mellitus, dyslipidemia, uric acid levels and heart disease. The results showed that the decision tree algorithm (J48) method has the higher level of accuracy than the method naive Bayes for stroke classification.
Multi-Attribute Decision Making dengan Metode Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) Oktri Mayasari; Yuki Novia Nasution; Rito Goejantoro
EKSPONENSIAL Vol 9 No 1 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (410.686 KB)

Abstract

Fuzzy TOPSIS is a method that is used for identifying solution from one limited alternative set. The basic principle is that the chosen alternative must have the shortest distance from the positive ideal solution and the furthest distance from the negative ideal solution to determine relative proximity from an alternative with optimal solution. Fuzzy numbers in this method give effectiveness to determine the value of decision matrix. The purpose of this research is to find out the recommendation of investment in ADHI, PTPP, WIKA, and WSKT stocks by using fuzzy TOPSIS method. The alternatives that is used in this research are four stocks in the building construction sector on LQ45, from February to July 2017 namely Adhi Karya (Persero) Tbk. (ADHI), PP (Persero) Tbk. (PTPP), Wijaya Karya (Persero) Tbk. (WIKA), and Waskita Karya (Persero) Tbk. (WSKT) with the attributes that consist of nine financial ratios, namely Earnings Per Share (EPS), Book Value Per Share (BV), Debt to Assets Ratio (DAR), Debt to Equity Ratio (DER), Return on Assets (ROA), Return to Equity (ROE), Gross Profit Margin (GPM), Operating Profit Margin (OPM) and Net Profit Margin (NPM) on June 2016. The result of the research with fuzzy TOPSIS analysis generates preference value from stocks of ADHI amount 0,1711, stocks of PTPP amount 0,6169, stocks of WIKA amount 0,6310, and stocks of WSKT amount 0,7488. The result of preference value shows that stocks of WSKT with the highest preference value become the best recommendation option to invest rather than the stocks of ADHI, PTPP, or WIKA.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (584.066 KB) | DOI: 10.30872/eksponensial.v13i1.874

Abstract

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%.
Optimasi Self-Organizing Map Menggunakan Particle Swarm Optimization untuk Mengelompokkan Desa/Kelurahan Tertinggal di Kabupaten Kutai Kartanegara Provinsi Kalimantan Timur: Studi Kasus : Data Potensi Desa Tahun 2018 Kusrahman, Nanda Yopan; Purnamasari, Ika; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (494.677 KB) | DOI: 10.30872/eksponensial.v11i2.656

Abstract

Self-Organizing Maps (SOM) is an efficient cluster analysis in handling high dimensional and large dataset. Particle Swarm Optimization (PSO) is an effective in nonlinear optimization problems and easy to implement. A clustering process occurs if all data can clustered into 1 cluster, however if one or two data did not join then the data have a deviant behavior called outliers or noise. PSO is used to evolve the weights for SOM to improve the clustering result and to cluster some social aspect in society, for example is poverty. Development strategies are prioritized to regions with largest population lived in poverty. Kutai Kartanegara regency (Kukar) are recorded as the biggest contributor on population lived in poverty at East Kalimantan in 2017. Development of underdeveloped villages is requires Village Potential data, which focus on visualizing the situation in the regions. This study aims to determine the number of clusters formed and to find the value of Davies Bouldin Index (DBI) from clustering underdeveloped villages in Kukar region using PODES 2018 data. This study uses 9 particle which are the final weight of the SOMs algorithm with different learning rate each particle. Based on the analysis, the optimal number of clusters is 2 clusters with DBI value of 0.7803, where cluster 1 consists of 82 underdeveloped villages and the cluster 2 consist of underdeveloped villages.
Pengendalian Kualitas Produk Menggunakan Diagram Kontrol Multivariat p Bayu Iswahyudi Noor; Ika Purnamasari; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 10 No 1 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (470.533 KB)

Abstract

Every company competes with other companies in similar industries. One way to win the competition or at least stay in the competition is to give full attention to the quality of the products, so it can outperform the products produced by a competitors company. Quality control is done at the stage of production process, in order to get the standard or quality as expected. Multivariate p chart is one of the methods used for quality control which is the development of the control chart p. This research is conducted in the newspaper company Kaltim Post, with the characteristics quality of color blur, not symmetrical and dirty. The research is conducted in two phases; phase I is conducted for the period of July 2017 and phase II is conducted for the period of August 2017. The purpose of this research is to know the result of the controlling production of Kaltim Post newspaper by using multivariate p chart , knowing the types of defects that often occur and the cause of the defects types. The result of controlling production of Kaltim Post newspaper using multivariate diagram p is controlled in phase I with upper control limit of 0.002736, center line of 0.0024224 and lower control limit of 0.0021087. So the limits in phase I are appropriate for use in phase II. The most common types of defects are colors blur the caused by machine, method, material, human, and environmental factors.

Page 4 of 21 | Total Record : 205

 2   3   4   5   6 

Filter by Year

2016 2025


Reset
Filter By Issues
All Issue Vol. 16 No. 2 (2025): Jurnal Eksponensial Vol. 16 No. 1 (2025): Jurnal Eksponensial Vol. 15 No. 2 (2024): Jurnal Eksponensial Vol. 15 No. 1 (2024): Jurnal Eksponensial Vol. 14 No. 2 (2023) Vol 14 No 1 (2023): Jurnal Eksponensial Vol. 14 No. 1 (2023) Vol. 13 No. 2 (2022) Vol. 13 No. 1 (2022) Vol. 12 No. 2 (2021) Vol. 12 No. 1 (2021) Vol. 11 No. 2 (2020) Vol. 11 No. 1 (2020) Vol 10 No 2 (2019): Jurnal Eksponensial Vol 10 No 2 (2019) Vol 10 No 1 (2019) Vol 9 No 2 (2018) Vol 9 No 1 (2018) Vol 8 No 2 (2017) Vol 8 No 1 (2017) Vol 7 No 2 (2016) Vol 7 No 1 (2016) More Issue