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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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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
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Analisis Pengendalian Kualitas Produksi Menggunakan Peta Kendali U dan Diagram Kontrol Decision On Belief (DOB). Nurul Rahmahani; Rito Goejantoro; Desi Yuniarti
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 (407.197 KB)

Abstract

Statistical quality control is a problem solving technique used to monitor, control, analyze, manage, and improve products. There are two kinds of control charts, namely the attribute control chart and the variable control chart. The Decision On Belief (DOB) control chart is an attribute control chart based on Bayes's Theorem. In this study, to determine the comparison of control chart U and the DOB control chart the degree of control sensitivity in identifying out of control data on the production quality control data banner of Lineza digital printing in Samarinda. Based on the result of the research, it is found that quality control using U control chart and DOB control diagram has not been statistically controlled because there is still data out of control and in better sensitivity level in detecting out of control data is a DOB control chart because this diagram detects 65% while the U control chart is only 15%.
Analisis Autokorelasi Spasialtitik Panas Di Kalimantan Timur Menggunakan Indeks Moran dan Local Indicator Of Spatial Autocorrelation (LISA) Nurmalia Purwita Yuriantari; Memi Nor Hayati; Sri Wahyuningsih
EKSPONENSIAL Vol 8 No 1 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

In the last few decades has developed statistical methods relating to spatial science, is the spatial statistics. Spatial Statistics aims to analyze spatial data. The case studies in this study was the amount of hotspots in East Kalimantan by Regency/City in years 2014-2016. This study aimed to analyze the existence of spatial autocorrelation in the data the amount of hotspots as well as determine the level of vulnerability to potential areas of forest and land fires in East Kalimantan by Regency/City in 2014-2016. The method used to analyze the global spatial autocorrelation is the Moran Index method and Local Indicators of Spatial Autocorrelation (LISA) for analyze spatialautocorrelation locally. The results of the analysis of global spatial autocorrelation using the Moran index with α = 20% showed there spatial autocorrelation amount of hotspots in East Kalimantan in 2014, 2015, and 2016. Meanwhile, the analysis results locally using LISA showed that there spatial autocorrelation in several Regency/City in East Kalimantan in 2014, 2015 and 2016. The analysis results Regency/City that belong to the vulnerable category of forest and land fires is Bontang City, Kutai Barat Regency, Kutai Kartanegara Regency, Mahakam Ulu Regency, dan Penajam Paser Utara Regency and Samarinda City.
Penerapan Metode Analisis Regresi Logistik Biner Dan Classification And Regression Tree (CART) Pada Faktor yang Mempengaruhi Lama Masa Studi Mahasiswa Chairunnisa Chairunnisa; Yuki Novia Nasution; Ika Purnamasari
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Binary Logistic Regression is one of the logistic regression analysis which is used to analyze the relationship between a dichotomous dependent variable with several independent variables. Classification and Regression Tree (CART) is one of the methods that developed to perform classification analysis on dependent variables either on nominal, ordinal, or continuous scale. In this research, Binary Logistic Regression method and Classification and Regression Tree (CART) applied to the data of the students at Faculty of Math and Natural Science Mulawarman University graduated in year 2016 to determine the characteristic of student which is classified according to two categories that is the study period less than or equal to 5 years and study period more than 5 Years, with five independent variables namely GPA Graduates (X1), Gender (X2), Type of Junior School (X3), Domicile (X4), and Major (X5). Factors that influence the study period of the students based on Binary Logistic Regression method are GPA, Gender, Secondary School Type and Major. The result of classification by using CART method is the student who have the study period less than or equal to 5 Years is a student from Chemistry major or have GPA between 3,51 and 4,00, while the study period more than 5 Year is the student who have GPA between 2,00 and 2,75; 2,76 and 3,50. In terms of classification accuracy, Binary Logistic Regression method was able to accurately predict the observation as much as 75.0%, while the CART method was able to accurately predict the observation as much as 77.27%.
Analisis Distribusi Frekuensi dan Periode Ulang Hujan: Studi Kasus: Curah Hujan Kecamatan Long Iram Kabupaten Kutai Barat Tahun 2013-2017 Widyawati, Widyawati; Yuniarti, Desi; Goejantoro, Rito
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 (597.349 KB) | DOI: 10.30872/eksponensial.v11i1.646

Abstract

Increasing water demand for various needs can be a complex problem so it is necessary to manage water resources. Analysis of hydrological data is very necessary to get information about water resources where the information can be used as a benchmark for planning a water resources builder. One of hydrological analysis is the analysis of rainfall data where this analysis uses frequency distribution analysis and rain return periods. There are four types of distribution used, namely normal distribution, normal log distribution, Gumbel distribution and type III log Pearson distribution. The goodness of fit test uses the Kolmogorov-Smirnov method, Chi-Square and Anderson-Darling. Rainfall return calculation is calculated when it is known the type of distribution of the data studied. This research uses rainfall data of Long Iram Sub-Distric, West Kutai Distric in 2013 to 2017 obtained from the Meteorology, Climatology and Geophysics Agency (BMKG) of Samarinda City. The results from research showed that the Gumbel distribution was the right distribution or distribution that was the best with the results of the return period of rain for the return period of 2 years obtained by rainfall of 519 mm, 5-year return period of 796 mm, 10-year return period of 980 mm, return period 20 years of 1.154 mm, a 50 year return period of 1.348 mm and in a 100 year return period of 1.752 mm.
Penerapan Diagram Kontrol Multivariate Exponentially Weighted Moving Variance (MEWMV) Agustina Feni Baransano; Sri Wahyuningsih; Yuki Novia Nasution
EKSPONENSIAL Vol 9 No 2 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Statistical Process Control based on the quality characteristics can be divided into two kinds, namely univariate control chart and multivariate control charts.This study usedMultivariate Exponentially Weighted MovingVariance Control Chart (MEWMV).PDAM Tirta Mahakam in the districts of Kutai Kartanegara is one of the regional companies engaged in the production of drinking water, which is located in Tenggarong, East Kalimantan.In the production process, PDAM Tirta Mahakam always refers to the standard which is set by the government in producing drinking water.The purpose of this study was to determine whetherwater quality characteristics of PDAM Tirta Mahakam in a controlledstate or not by using control charts MEWMV,and to know the the water process capability. From the result of research it can be concluded that by using MEWMV control chart with weight , , and , show that the condition has been statistically in control. Process capability index MCpin multivariateexplains that the process has not been capable in precision with a value of 0,896 or not meet the specifications of the company.
Penggunaan Metode Nonparametrik Untuk Membandingkan Fungsi Survival Pada Uji Gehan, Cox Mantel, Logrank, Dan Cox F Fitriani Fitriani; Sri Wahyuningsih; Yuki Novia Nasution
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Survival analysis is a statistical method that aims to study and model the relationship between risk factors and the study time students to reach graduation. In this study conducted a survival analysis using a nonparametric method. They are Gehan Test, Cox Mantel Test, Logrank Test, and Cox F Test on data of students of Mulawarman University Faculty of Mathematical and Natural Science majoring in Statistics and majoring in Computer Science 2010. The purpose of this research was to compare the of period of study survival function students majoring in Statistics and majoring in Computer Science . This study was conducted using data of 167 students majoring in Statistics and majoring in Computer Science. The results showed that students of majoring in Computer Science longer in studying compared with students majoring in Statistics. For students majoring in Statistics who participated in the selection to go to college through the SBMPTN and SMMPTN study longer than SNMPT. While those who while majoring in Computer Sciences who participated in the selection to go to college through three pathways had the same study time.
Peramalan Jumlah Penduduk Kota Samarinda Dengan Menggunakan Metode Pemulusan Eksponensial Ganda dan Tripel Dari Brown Reyham Nopriadi Gurianto; Ika Purnamasari; Desi Yuniarti
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 (183.926 KB)

Abstract

Forecasting is a process or method to predict an event that will occur in the future. Exponential smoothing is a method of moving average forecasting that conduct weighting decreases exponentially toward the value of the older observations. In this study discusses the Brown’s double exponential smoothing and Brown’s triple exponential smoothing method in predicting the population of the city of Samarinda in 2014, 2015 and 2016 are very necessary for the government to determine the population of the city of Samarinda. Double exponential smoothing method and triple from Brown is a method of extrapolation or by using a time series of past history in making the forecast for the future which used as a guide in decision-making processes. Results obtained using the method of Brown's double exponential smoothing using the parameter alpha of 0,52 was obtained that the forecast of total population in 2014 was 843.653 residents, in 2015 was 898.647 residents, and in 2016 was 944.716 residents with an average value deviation absolute (MAD) is 12.937 and the average -rata absolute percentage error (MAPE) is 2,4548. In the triple exponential smoothing method Brown’s using parameters alpha 0,4 obtained results forecast the total population in 2014 was 854.766 residents, in 2015 was 898.647 residents, and in 2016 was 944.716 residents with an average value deviation absolute (MAD) is 14.709 and the average percentage The absolute error (MAPE) is 2,7589.
Optimasi Fuzzy C-Means Menggunakan Particle Swarm Optimization Untuk Pengelompokan Kabupaten/Kota Di Pulau Kalimantan (Studi Kasus : Data Indikator Kesejahteraan Rakyat Tahun 2020) Febriyanti, Nur Afifah; Goejantoro, Rito; Prangga, Surya
EKSPONENSIAL Vol. 14 No. 1 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1148.931 KB) | DOI: 10.30872/eksponensial.v14i1.1095

Abstract

Fuzzy C-Means (FCM) is a method of grouping data based on the degree of membership whose observation object is based on the information found in the data describing the object. The FCM method has weaknesses in the initial cluster center determination, so it can be overcome by the Particle Swarm Optimization (PSO) method that can be applied to find the optimal solution of the optimal cluster center determination. The purpose of this research is to determine the optimal number of clusters based on the validity indexes of Partition Coefficient (PC) and Modified Partition Coefficient (MPC), and obtain the results of grouping regencies/cities using the FCMPSO method. Based on the FCMPSO method with a validity index of PC and MPC, it produces an optimal cluster of two clusters, the first cluster consisting of 33 regencies/cities on Kalimantan Island and the second cluster consisting of 23 regencies/cities on Kalimantan Island.
Optimalisasi K-Means Cluster dengan Principal Component Analysis pada Pengelompokan Kabupaten/Kota di Pulau Kalimantan Berdasarkan Indikator Tingkat Pengangguran Terbuka Rais, Muhammad; Goejantoro, Rito; Prangga, Surya
EKSPONENSIAL Vol. 12 No. 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (553.224 KB) | DOI: 10.30872/eksponensial.v12i2.805

Abstract

Data mining or often also called knowledge discovery in databases is an activity that includes collecting, using historical data to find regularity, patterns, or relationships in large data sets resulting in useful new information. Cluster analysis is an analysis that aims to group data based on its likeness. This research uses the K-Means method combined with PCA. The K-Means method groups data in the form of one or more clusters that share the same characteristics. While the PCA method was used to reduce research variables. This grouping method was applied to the data indicator of the unemployment rate of districts/cities in Kalimantan Island in 2018. The cluster validation used in this study was the Davies-Bouldin Index (DBI). Based on the results of the analysis, it was concluded that the number of principal components formed was as many as 2 principal components. The most optimal grouping of districts/cities in Kalimantan island in 2018 was to use 2 clusters with a DBI value of 0,507. The grouping of districts/cities in Kalimantan Island in 2018 produced 2 clusters, cluster 1 consisting of 51 districts/cities and clusters of 2 consisting of 5 districts/cities. Cluster 1 was a cluster that has the highest percentage of the poor population and the highest labor force participation rate when compared to cluster 2. While cluster 2 was a cluster that has an index value of human development, population, number of the labor force, number of unemployed, population density, and the minimum wage of district/city was high compared to cluster 1.
Analisis Positioning dengan Menggunakan Multidimensional Scaling Nonmetrik Devy Sintya Putri; Sri Wahyuningsih; Rito Goejantoro
EKSPONENSIAL Vol 9 No 1 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

As the era progresses, more and more smartphone brands are present in the market in which it is difficult for consumers to decide which smart phone brands are good among others. The aims of this research are to know the position of five brands of the smart phone based on the consumer perception by using multidimensional scaling analysis (MDS) and also to know the superiority for each of these smartphone brands based also on the consumer perception focused on the product attribute and consumer perception about smartphone brands which they mostly prefered. So the result indicates that the coordinate points got based on the consumer perception by using MDS analysis are as follows; Asus is (10,494,2525), Oppo is (-4,154; 3,591), Samsung is (-4,216; (- 3,979)), Sony is (4,188 ; (- 3,985)), and Xiaomi is (-6,312; 1,848). Among the five smart phone brands above, Xiaomi has an advantage on the most affordable price attribute. Samsung has an advantage on the attributes of good screen display results, better known brands, more beautiful designs, complete features, ease for use, and large memory capacity. The smart phone brands of Asus, Oppo and Sony have the advantage on the results of a good camera and good processor performance. It is the fact that the most superior smart phone brands based on the consumer’s perception data are the brand of Oppo and Samsung smart phones.

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