Garuda - Garba Rujukan Digital

All

Eksponensial

exponensial
Website

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

1 2

Search results for , issue "Vol. 11 No. 2 (2020)" : 12 Documents clear

Pengelompokan Data Kategorik Dengan Algoritma Robust Clustering Using Links: Studi Kasus: PT. Prudential Life Jalan MT. Haryono Samarinda Dewi, Isma; Syaripuddin, Syaripuddin; Hayati, Memi Nor
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Cluster analysis is a technique of data mining that is used to group data based on the similarity of attributes of data objects. The problem that is often encountered in cluster analysis is the data on a categorical scale. Categorical scale data grouping can be done using the ROCK (RObust Clustering using linKs) algorithm. The ROCK algorithm is included in the of agglomerative hierarchical clustering algorithms in cluster analysis. This algorithm introduces a concept called neighbors and links in grouping data. Categorical data grouping with ROCK algorithm is done in three steps. The first step is counting similarities. The second step is determining the neighbors and the last is calculating the links between the observation objects. The value of the link is affected by θ. The optimum number of clusters in the ROCK algorithm is selected using a minimum ratio value of . The purpose of this study is to group 100 data of insurance customers of PT. Prudential Life Samarinda in 2018. Based on the analysis results, obtained that the optimum group is at θ = 0.1 with a ratio value of is 0.1371. The optimum number of groups formed is 2 clusters. The first group consisted of 42 customers and the second group consisted of 58 customers.

Implementasi Text Mining Pengelompokkan Dokumen Skripsi Menggunakan Metode K-Means Clustering Rachman, Dezty Adhe Chajannah; Goejantoro, Rito; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Text mining is the text analysis that automatically discover quality information from a series of texts that is summarized in a document. K-Means Clustering method is often used because of its ability to make a group of large amounts of data with relatively fast and efficient computing time. The purpose of this study is to determine the optimal number of the groups formed from the thesis documents and determine the results of the groups formed. This study is using Nazief and Adriani algorithms for the stemming step, Euclidean Similarity to calculate document distances, and Silhouette Coefficient to test the cluster validity. The sample in this study is 119 thesis documents of Statistics Study Program, Mathematics Department, Faculty of Mathematics and Natural Sciences, graduates of 2016-2018. Based on the results of the analysis, the optimal number of groups formed is two clusters with a silhouette coefficient of 0.12. The results of the grouping formed are two clusters with the total of the first cluster is 85 documents and the second cluster is 34 documents. The first cluster is dominated by studies with data mining especially classification, time series analysis, regression analysis, survival analysis, spatial analysis and operational research, and the second cluster is dominated by studies with multivariate analysis, quality control, and insurance mathematics.

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

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.

Penaksiran Kandungan Klorida di Sungai Mahakam Wilayah Samarinda Tahun 2017 dengan Metode Cokriging Putra, Eko Prasatyo; Goejantoro, Rito; Suyitno, Suyitno
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Cokriging is the interpolation method of value of an unsampled data by minimizing the variance of the estimation error by utilizing cross correlations between the main variable and the additional variable. This study aims to estimate the chloride content in the Mahakam River in Samarinda by using the cokriging method. The data of this study are spatial data that consists of the main variable data is chloride content and additional variable data is the pH of the water, as well as the coordinates of the observation location. Semivariogram (matrix covariance) is determined based on the best model, namely theoretical semivariogram. The best theoretical semivariogram model for cross variables is the exponential model, while the best theoretical semivariogram model for the main variable and additional variables are the spherical model. The selected theoretical semivariogram model was used to determine the semivariogram matrix in estimating chloride content in IPA Bantuas and Teluk Lerong. The results of estimation of chloride content in IPA Bantuas and Teluk Lerong are 1.91 mg/l and 1.64 mg/l. Based on the estimated chloride content in IPA Bantuas and in Teluk Lerong, it shows that the chloride content is still below the maximum threshold and meets the water chloride content standard for consumption by the Ministry of Health of the Republic of Indonesia, which is a maximum of 250 mg/l.

Penerapan Model Mixed Geographically Weighted Regression dengan Fungsi Pembobot Adaptive Tricube pada IPM 30 Kabupaten/Kota di Provinsi Kalimantan Timur, Kalimantan Tengah dan Kalimantan Selatan Tahun 2016 Safitri, Ranita Nur; Suyitno, Suyitno; Hayati, Memi Nor
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Mixed Geographically Weighted Regression (MGWR) model is a Geographically Weighted Regression (GWR) model which has global (equal value) and local (inequal value) parameters at every different observation location. The goal of this study is to obtain MGWR model of the Human Development Index (HDI) data and find out significant factors influencing the HDI in each district (city) East Kalimantan, Central Kalimantan and South Kalimantan province in 2016. Parameter estimation method is conducted in two stages namely local parameter estimation and global parameter estimation. Local parameter estimation method is Maximum Likelihood Estimation (MLE), with spatial weighting is calculated by adaptive tricube weighting function and optimum bandwidth determination uses the Akaike Information Criteria (AIC). Global parameter estimation method is Ordinary Least Square (OLS). Based on the result of MGWR parameter testing, it was concluded that the school enrollment rates (SMP) and poor people percentage affected the HDI of 30 districts (cities) in East Kalimantan, Central Kalimantan and South Kalimantan. Meanwhile the population density affected the HDI of two districts namely HDI of Samarinda and Bontang.

Perbandingan Metode Klasifikasi Naïve Bayes Dan Jaringan Saraf Tiruan: Studi Kasus: Pt Asuransi Jiwa Bersama Bumiputera Tahun 2018 Ardyanti, Hesti; Goejantoro, Rito; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Classification is a technique to form a model of data that is already known to its classification group. The model that was formed will be used to classify new object. Naïve Bayes is a classification technique for predicting future probability based on past experiences with a strong assumption of independence. Artificial neural network is one of the data mining analysis tools that can be used to create data on classification. Model selection in artifial neural networks requires various factors such as the selection of optimal number of hidden neuron. This research has a goal to compare the level of classification accuracy between the Naïve Bayes method and artificial neural network on payment status of the insurance premium. The data used is insurance costumer’s data of PT AJB Bumiputera Samarinda in 2018. The result of the comparison of accuracy calculation from the two analyzes indicate that artificial neural network has a higher level of accuracy than naïve Bayes method. Classification accuracy result of Naïve Bayes is 82,76% and artificial neural network is 86,21%.

Pemodelan Indeks Pembangunan Manusia (IPM) Menggunakan Analisis Regresi Probit: Studi Kasus: Indeks Pembangunan Manusia (IPM) di Pulau Kalimantan Tahun 2017 Christyadi, Santo; Satriya, Andi M Ade; Goejantoro, Rito
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Ordinal probit regression analysis is non-linear regression analysis that used to find affected independent variables for ordered categorical dependent variable and regression model in this analysis used Normal cumulative distribution function. Parameter estimation in this model used Maximum Likelihood Estimation (MLE) method. This model has been applied to Human Development Index (HDI) in Borneo Island in 2017 case study. HDI is the most important measurement in improving the human development quality in all cities/regencies in Indonesia. Some factors that affected to IPM, they are Life Expectancy (X1), School Expectancy (X2), Spending per Capita (X3), Average School Duration (X4), and Labour Force Participation Rate (X5). Based on research that was performed by researcher, resulted two factors affecting to HDI, those are Life Expetancy and Average School Duration. This model has classification accuracy of 89,29%, APER (Apparent Error Rate) value of 10,71%, and AIC (Akaike Information Criterion) value of 39,75; this model was very good because prediction value is almost approaching to observation value (actual value).

Analisis Cluster Pada Data Kategorik dan Numerik dengan Pendekatan Cluster Ensemble: Studi Kasus: Puskesmas di Provinsi Kalimantan Timur Kondisi Desember 2017 Lestari, Nur Aini Ayu; Hayati, Memi Nor; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Cluster analysis used to process categorical and numerical data at once is Cluster Ensemble algorithm Based on Mixed Data Clustering (algCEBMDC), which is a cluster algorithm with an ensemble cluster approach. The method used for numerical data is Agglomerative Nesting (AGNES) algorithm and for categorical data is the RObust Clustering using linK (ROCK) algorithm. The best clustering method and the optimum number of clusters in the AGNES algorithm is selected based on the maximum Pseudo-F value and the minimum icdrate value. The optimum number of clusters in the ROCK algorithm is selected using the minimum value of ratio . The purpose of this study was to make a group of 179 Puskesmas in East Kalimantan on December 2017. Based on the results of the analysis, obtained 5 optimum cluster for numerical clustering with the AGNES algorithm and 2 optimum cluster for categorical clustering data with the ROCK algorithm. Final cluster for mixed data clustering obtained 2 optimum cluster at a threshold of 0.2 and 0.3 with value of ratio is . The first cluster consists of 83 Puskesmas and cluster two of 96 Puskesmas.

Perbandingan Diagram Kontrol Demerit dan Fuzzy u: Studi Kasus : Kecacatan Produk Kayu Lapis (Plywood) di PT. Segara Timber Mangkujenang, Samarinda Provinsi Kalimantan Timur Tahun 2019 Septilasse, Rebeka Norcaline; Goejantoro, Rito; Wahyuningsih, Sri
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Control chart is a graph that provides a picture of a running process whether under controlled conditions or not. Demerit control chart and fuzzy u control chart are very suitable for production quality control. This study was applied to the data of defects of plywood products at pt. segara timber, samarinda, east Kalimantan in 2019. The purpose of this study is to get the results of a comparison of the decision of Demerit control chart and fuzzy u control chart. The results of this study shows the demerit control chart is more thorough than the fuzzy u control chart due to the demerit control chart found 12 out of control observations and the fuzzy u control chart only found 1 out of control observations.

Estimasi Parameter Model Regresi Linier dengan Pendekatan Bayes: Studi Kasus: Kemiskinan di Provinsi Kalimantan Timur pada Tahun 2017 Katianda, Kristin Rulin; Goejantoro, Rito; Satriya, Andi M Ade
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Two types of viewpoints in statistics are Frequentist and Bayesian Method. In Bayesian method sees a parameter as a random variable, so the value is not single. Frequentist method that are often used in linear regression are Ordinary Least Square (OLS) and Maximum Likelihood Estimation (MLE). But along with developments, several studies show the results of modeling that are better at using Bayesian method than the Frequentist method. The data used is Poverty data in 2017 from BPS East Kalimantan. The purpose of this study is to estimate the parameters of the regression model with the Bayesian method on data on the number of poor people and regional domestic products in East Kalimantan Province in 2017. To estimate the parameters of the Bayesian linear regression model it is used by the prior conjugate distribution. Then the markov chain is designed from the posterior distribution with Gibbs Sampler as many as 50.000 iterations and the estimated parameters that are the average of the Gibbs Sampler value are = 0.9149, = 5.462, and = 0.2827. From the Gibbs Sampler values that have been obtained, a density function for each parameter is generated so that the Bayesian confidence interval (credible interval) for estimation is (0.85; 0.9836), (4.484; 6.439) and (0.2694 ; 0,296) for parameters .

Page 1 of 2 | Total Record : 12

1 2

Filter by Year

2020 2020

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) Vol 10 No 2 (2019): Jurnal Eksponensial 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

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

Abstract

Abstract

Abstract

Abstract

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

Abstract

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