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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 205 Documents

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Model Geographically Weighted Weibull Regression pada Indikator Pencemaran Air Biochemical Oxygen Demand di Daerah Aliran Sungai Mahakam Rahmah, Siti Mahmudatur; Suyitno, Suyitno; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 12 No. 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Geographically Weighted Weibull Regression (GWWR) Model is a Weibull regression model applied to spatial data. Estimation of the GWWR model is performed at every observation location using spatial weighting. The purpose of this study was to determine the GWWR model of water pollution indicator Biochemical Oxygen Demand (BOD) data and the factors that influence BOD in the Mahakam River. The estimating parameters method of the GWWR model was the Maximum Likelihood Estimation (MLE) and it’s estimator was obtained by Newton-Raphson Iterative method. Spatial weighting in parameter estimation was determined using the Adaptive Bisquare weighting function and bandwidth optimum was determined by using Generalized Cross-Validation (GCV) criteria. Based on the GWWR model parameters testing, the factors that influence BOD locally was nitrate concentrations, while the factors influence globally were temperature and nitrate concentration.

Estimasi Parameter Model ARIMA untuk Peramalan Debit Air Sungai Menggunakan Least Square dan Goal Programming Dewi Wulan Sari; Rito Goejantoro; Sri Wahyuningsih
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (184.755 KB)

Forecasting is a technique to make a desicion in the future considered by data from the past and present. This forecasting is in hydrology sector which is river flow forecasting. River flow forecasting is one way to anticipate the instability of the river flow. The aim of this research was to determine the best ARIMA model based on analysis of the river flow of Karang Mumus, Samarinda. This research will explain the procedure of ARIMA model building using the Least Square and Goal Programming to predict the river flow of Karang Mumus, Samarinda. The data used montly from January until December. The model of ARIMA (2,1,2)to predict the river flow of Karang Mumus using Goal Programming is : Zt=μ-0,0492Zt-1-0,0523Zt-2-0,9969Zt-3+0,9247at-1+0,9339at-2+at ARIMA (2,1,2) for river flow forecasting using Goal Programming is : Zt=1,17Zt-1-0,17Zt-2+at+0,31at-1 The best ARIMA model for river flow forecasting of Karang Mumus is ARIMA (2,1,2) using Least Square method. Result for river flow forecasting of Karang Mumus river in Samarinda from January until Desember 2015 are 1.733 m3, 1.729 m3, 1.730 m3, 1.730 m3, 1.729 m3, 1.730 m3, 1.732 m3, 1.729 m3, 1.730 m3, 1.732 m3, 1.729 m3, dan 1.730 m3.

Peramalan Jumlah Kunjungan Wisatawan Mancanegara Ke Indonesia Menggunakan Autoregressive Integrated Moving Average (ARIMA) Ramadhani, Adelia; Wahyuningsih, Sri; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 13 No. 2 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Autoregressive Moving Average (ARIMA) is a general model that is often used in time series modeling. One application of ARIMA can be used on the data foreign tourist visits to Indonesia. The tourism sector is one of the priority sectors in Indonesia's economic development. One of the determining factors in the tourism sector is the number of foreign tourist visits. Therefore, forecasting the number of foreign tourist visits is very necessary. The purpose of this study was to obtain a model and forecast results for the number of foreign tourist visits from March 2020 to October 2021 using the ARIMA model. The results of the analysis showed that the ARIMA model (0,1,1) was the best model with a MAPE of 6.23%. The forecasting results with the best model showed that the highest number of foreign tourist visits is in Agustus 2021 and the lowest is in December 2020.

Pengelompokan Kabupaten/Kota Di Pulau Kalimantan Dengan Fuzzy C-Means Berdasarkan Indikator Kemiskinan Ningtyas, Retno Ayu; Nasution, Yuki Novia; Syaripuddin, Syaripuddin
EKSPONENSIAL Vol. 13 No. 2 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Cluster analysis is a branch of statistical science that is used to grouping data that have similar characteristics between each other. The grouping method used in this research is Fuzzy C-Means. Fuzzy C-Means method is one of the grouping methods developed from the C-Means method by applying the properties of fuzzy sets. With the existence of each data is determined by the degree of membership. This method is applied to data from 56 districts/cities on Borneo based on poverty indicators with variables namely the percentage of average length of schooling, life expectancy, percentage of the poor, percentage of open unemployment rate, percentage of households with proper sanitation, and percentage of households with proper drinking water. This study aims to obtain the results of grouping districts/cities on Borneo based on poverty indicators and to obtain optimal cluster results based on three validity indices, namely Connectivity, Dunn, and Silhoutte values. Based on the results of the study, it was found that there were 2 optimal clusters, namely the first cluster consisted of 36 regencies/cities while the second cluster consisted of 20 regencies/cities.

Metode Hierarchical Density-Based Spatial Clustering of Application with Noise (HDBSCAN) Pada Wilayah Desa/Kelurahan Tertinggal di Kabupaten Kutai Kartanegara: (Studi Kasus : Data Hasil Pendataan Potensi Desa (PODES) Tahun 2018) Wahyuni, Nanda Anggun; Hayati, Memi Nor; Rizki, Nanda Arista
EKSPONENSIAL Vol. 12 No. 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

The underdeveloped areas are generally the districts which are relatively underdeveloped compared to other regions on a national scale. Determination of underdeveloped villages is often done in order to determine the distribution of government assistance so that assistance can be distributed appropriately. The identification is based on facilities, infrastructure, access, social, population and economy provided in the Village Potential data (PODES). The concept of grouping based on regional or spatial is done to find out certain characteristics in an area. HDBSCAN is a grouping concept with a parameter called Mpts. The purpose of this study is to know the number of clusters formed in the grouping of underdeveloped villages / urban areas in Kutai Kartanegara Regency using the HDBSCAN method. The Mpts parameters that is used in this study is from 2 to 6. Based on the results of the analysis, the clusters formed in the grouping of underdeveloped villages / urban areas in Kutai Kartanegara Regency using the HDBSCAN method, were 3 clusters. Cluster 0 consists of 19 villages / urban areas , cluster 1 consists of 4 villages / urban areas and cluster 2 consists of 61 villages / urban areas. Based on the analysis, villages / urban areas included in cluster 1 could be the main target of the government in providing assistance and development of regional facilities / infrastructure.

Optimasi Pendistribusian Barang Dengan Menggunakan Vogel’s Approximation Method dan Stepping Stone Method Yuli Ratnasari; Desi Yuniarti; Ika Purnamasari
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 (958.795 KB)

The development of era and technology are getting shopisticated which impacts the increasing of company in service area. Distribution and transportation are important aspects that can affect the success of the company’s performance.Vogel’s Approximation Method the first solution to solve the transportation problem and also Stepping Stone Method for the optimum solution to get the minimum operational cost. The aim of this research is to see the difference distribution operational cost of LPG gas 3 Kg in PT. Tri Pribumi Sejati before and after applying Vogel’s Approximation Method (VAM) and Stepping Stone Method. The result shows that Vogel’s Approximation Method (VAM) spent transportation cost Rp 24.353.568,- so it saved the transportation cost for 45,9% and made difference Rp 20.646.432,-. Next, applying Stepping Stone Method optimum solution spent transportation cost Rp 24.031.104,- so it also saved the transportation cost for 46,6% and made difference Rp 20.968.896,- of total cost of PT. Tri Pribumi Sejati Rp 45.000.000,-. To sum up that using Vogel’s Approximation Method the first solution and Stepping Stone Method optimum solution are exact method to minimize the distribution operational cost of 3kg gas tube in PT. Tri Pribumi Sejati.

Analisis Survival Data Kejadian Bersama dengan Pendekatan Efron Partial Likelihood Santi Prabawati; Yuki Novia Nasution; Sri Wahyuningsih
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 (751.87 KB)

Survival analysis is a statistical procedure used to analyze data related to survival time, from the defined time origin until the occurrence of certain events. In the survival analysis, sometimes ties are found, in which two or more individuals experience the same event at the same time. There are three widely used methods to treat ties in survival analysis, that is Exact method, Breslow approach, and Efron approach. Efron's approach has a simple, fast, and accurate calculation especially when the data contains many ties. The purpose of this study is to find out the Cox proportional hazard data ties model using Efron partial likelihood approach and to know the variables that affect the graduation time of student of Faculty of Mathematics and Natural Sciences of Mulawarman University class of 2011 that graduated until February 28, 2017. The variables are Gender, home area, funding sources, and GPA. Based on the results of the analysis that has been done with the help of software R, it is obtained that the variables that have significant effect are gender and GPA. For the gender variables it was concluded that female students had a chance of 1,362 times to graduate faster than male students. While for the GPA variable it is concluded that each addition of GPA of 0.1, then the student's chance to graduate faster will increase by 1,225 times.

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.

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.

Klasifikasi Data Nasabah Asuransi Dengan Menggunakan Metode Naive Bayes Dyah Arumatica Novilla; Rito Goejantoro; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 10 No 2 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Classification is the logical grouping of objects according to the characteristics of their similarities. Naive Bayes is a method for predicting future opportunities based on past experiences. This study discusses the classification of insurance customer data of PT. Prudential Life Branch of Samarinda in 2017. With the aim to know whether the method of Naive Bayes can classify data of insurance customers of PT. Prudential Life in 2017 using the R program and to determine the accuracy of the results of data testing I and data testing II. As a result, Naive Bayes method can classify data of insurance customers of PT. Prudential Life with 80% accuracy for 25 data testing I and 74.67% for 75 data testing II.

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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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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

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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