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Meiliyani Siringoringo
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eksponensial@fmipa.unmul.ac.id
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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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Pemodelan Status Kesehatan Pasien Medical Check Up Klinik Handil Muara Jawa Dengan Regresi Logistik Biner Rakhmanto Anugrah Darmawan; Darnah Andi Nohe; Desi Yuniarti
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 (572.204 KB)

Abstract

Health is a major human needs are also priorities in human life. many types of health providers available to the community as an example of health care clinics were organized promotive, preventive, curative and rehabilitative. Handil clinics serve patients Muara Jawa Medical Check-Up for the workers of the company or the public. To analyze the factors that affect the health of a patient Medical Check Up can use logistic regression analysis. Logistic regression analysis is an analysis that describes the relationship between the response variable is binary with explanatory variables that can be either qualitative or quantitative variables variables. Based on the research results, we concluded that of the testing parameters, only gender and companies that significantly affect the patient's health status.
Model Geographically Weighted Weibull Regression Pada Indikator Pencemaran Air COD di Daerah Aliran Sungai Mahakam Kalimantan Timur Primadigna, Ullimaz Sam; Suyitno, Suyitno; Siringoringo, Meiliyani
EKSPONENSIAL Vol. 13 No. 2 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (860.595 KB) | DOI: 10.30872/eksponensial.v13i2.1050

Abstract

The Geographically Weighted Weibull Regression (GWWR) model is a Weibull regression model applied to spatial data. Parameter estimation is carried out at each observation location using spatial weighting. This study aimed to determine the GWWR model on the Chemical Oxygen Demand (COD) water pollution indicator data and to obtain the factors that influence COD in the Mahakam watershed. The parameter estimation method was Maximum Likelihood Estimation (MLE). Spatial weighting in parameter estimation has been determined using the adaptive tricube weighting function and the criteria for determining the optimum bandwidth was Generalized Cross-Validation (GCV). The research sample was 20 location points of the Mahakam river determined by the Environmental Department of East Kalimantan Province. The results showed that the factors that influence COD locally was temperature, while the factors that influence globally were temperature, Total Suspended Solids (TSS), and Fecal Coli.
Optimasi Algoritma Naïve Bayes Menggunakan Algoritma Genetika Untuk Memprediksi Kelulusan: Studi Kasus: Mahasiswa Jurusan Matematika FMIPA Universitas Mulawarman Feronica, Elisa; Nasution, Yuki Novia; Purnamasari, Ika
EKSPONENSIAL Vol. 13 No. 2 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1106.672 KB) | DOI: 10.30872/eksponensial.v13i2.1057

Abstract

The Naïve Bayes algorithm is classification method that uses the principle of probability to create predictive models. Naïve Bayes is based on the assumption that all its attributes are independent which can be optimized by genetic algorithms. Genetic algorithm is an optimization technique which works by imitating the process of evaluating and changing the genetic structure of living creatures. In this study, the Naive Bayes algorithm was optimized using by genetic algorithm to predict student graduation with attributes, namely gender, regional origin, admission path and employment status. The data used is the students of the Mathematics Department, Faculty of Mathematics and Natural Sciences, Mulawarman University who graduated in March 2018 to December 2020. The results of this study indicate the accuracy value generated by Naïve Bayes of 50% increased by 16,67% after the attributes were optimized by using the genetic algorithm to 66,67% with 3 selected attributes, namely regional origin, admission path and employment status
Analisis Cluster Pada Produk Mie Instan Berdasarkan Komposisi Yang Terkandung Dengan Menggunakan Metode Ward Sam, Faza Syahrudin; Syaripuddin, Syaripuddin; Wasono, Wasono
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 (611.762 KB) | DOI: 10.30872/eksponensial.v12i1.759

Abstract

Cluster analysis is a grouping of data (objects) based on only the information found in the data that describes the object and the relationships between data. The variance method commonly used is the Ward method where the average for each cluster is calculated. At each stage, the two clusters that have the smallest increase in sum of squares in the cluster are combined.. Some compositions of ingredients in noodles, for example, fat, protein, carbohydrates, food fiber, sugar and sodium. The composition of the noodles that are dangerous one of which is Monosodium Glutamate (MSG). The purpose of this research is to find out how many clusters are formed based on the composition of the content of instant noodle products. Based on the results of cluster research formed based on the composition of the contents of 43 instant noodle samples are 9 clusters where the first cluster consists of 2 members, the second cluster consists of 7 members, the third cluster consists of 5 members, the fourth cluster consists of 7 members, the fifth cluster consists of 6 members, the sixth cluster consists of 4 members, the seventh cluster consists of 4 members, the cluster the eighth consists of 1 member and the ninth cluster consists of 7 members.
Peramalan Menggunakan Time Invariant Fuzzy Time Series Siti Rahmah Binaiya; Memi Nor Hayati; 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 (632.787 KB)

Abstract

Forecasting is a technique for estimating a value in the future by looking at past and current data. Fuzzy Time Series is a forecasting method that uses fuzzy principles as the basis, where the forecasting process uses the concept of fuzzy set. This study discusses the Time Invariant Fuzzy Time Series method developed by Sah and Degtiarev to forecast the East Kalimantan Province Consumer Price Index (CPI) in May 2018. In the Time Invariant Fuzzy Time Series method using a frequency distribution to determine the length of the interval, 13 fuzzy sets are used in the forecasting process. Based on this study, using CPI data of East Kalimantan Province from September 2016 to April 2018, the forecasting results for May 2018 were obtained 135.977 and obtained the results of forecasting error values using Mean Absolute Percentage Error (MAPE) is under 10% of 0.0949%.
Analisis Data Kejadian Berulang Tidak Identik Dengan Cox Gap TimeModel Andi Widya Rhezky Awalul Aziz; Yuki Novia Nasution; Sri Wahyuningsih
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (987.342 KB)

Abstract

The gap time method is a method that can be used in recurrent event Time-based modelling. Gap analysis is often useful when events are relatively uncommon, when the object of the study is the prediction of time for the next event, or on the phenomenon of circulation.The analysis of model for non-identical recurrent events using survival time in the form of gap time is called Cox Gap Time Model. The purpose of this research is to know Cox Gap Time model for recurrent occurrence in DM type II disease and to know the factors that influence repetitive incident in DM type II disease in RSUD A. W. Sjahranie Samarinda. The variables in this research are age, treatment, status and relapse time (gap time). The study was conducted by using 263 medical records data of DM type II patients admitted to the hospital during observation period in January 2015 until December 2016. The results shows that age factor affects the first gap time and there are age, gap 1 covariate and gap 2 covariate that have significant effect aga inst to the third gap time variable, meanwhile there is no variable affects the second gap time.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (560.404 KB) | DOI: 10.30872/eksponensial.v13i1.879

Abstract

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.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (252.858 KB) | DOI: 10.30872/eksponensial.v11i2.661

Abstract

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.
Optimasi Klasifikasi Batubara Berdasarkan Jenis Kalori dengan menggunakan Genetic Modified K-Nearest Neighbor (GMK-NN) Nanang Wahyudi; Sri Wahyuningsih; Fidia Deny Tisna Amijaya
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 (482.276 KB)

Abstract

The K-Nearest Neighbor (K-NN) method is one of the oldest and most popular Nearest Neighbor-based methods. The researchers developed several methods to improve the performance of the K-NN algorithm by using the Genetic Modified K-Nearest Neighbor (GMK-NN) algorithm. This method combines the genetic algorithm and the K-NN algorithm in determining the optimal K value used in the classification prediction. The GMK-NN algorithm will greatly facilitate the examination of coal classification in the laboratory without having to do a lot of chemical and physics testing that takes a long time only with the data already available. In this research, K value optimization is done to predict the classification of coal based on calories owned by PT Jasa Mutu Mineral Indonesia in 2017. Based on the research, using the proportion of training and testing data 90:10, 80:20 and 70:30 obtained the value of K the most optimal is at K = 1. The highest prediction accuracy was obtained by using 90:10 proportion data which is 100%, then with the proportion of 80:20 data obtained prediction accuracy of 91.67% and with the proportion of 70:30 data obtained prediction accuracy of 94.44%.
Pemodelan Geographically Weighted Regression (GWR) Dengan Fungsi Pembobot Tricube Terhadap Angka Kematian Ibu (AKI) Di Kabupaten Kutai Kartanegara Tahun 2015 Muhammad Rahmad Fadli; Rito Goejantoro; Wasono Wasono
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 (702.1 KB)

Abstract

Maternal Mortality in Kutai Kartanegara is a geographical problem that suspected affected by geographical factor which the global regression cannot model the relation well between the main problem and its independent variable. Therefore, Geographically Weighted Regression (GWR) is used to solve it. Spatial statistics is a method for analyzing data that has spatial correlation. GWR Model is the locally of global regression which considering the geographical or location as the weighted function for estimating the parameters of models. The tricube weighted function is used for the weighting. From this study, the models are different from location to others with also has the independent variables. For Samboja, Muara Jawa, Sanga-Sanga, Anggana, Muara Badak, Marang Kayu, and Tabang which are not affected by the indicators. Loa Janan, Loa Kulu, Muara Muntai, Kota Bangun, Tenggarong, Sebulu, Tenggarong Seberang, Muara Kaman, and Kenohan have the Maternal Mortality affected by Hospital Ratio per 1.000 Pregnant Mothers (x1). Muara Wis, Kenohan, dan Kembang Janggut have the Maternal Mortality affected by Childbirth with Medical Help (x2). Muara Muntai, Muara Wis, Kota Bangun, Sebulu, Tenggarong, Muara Kaman, Kenohan, and Kembang Janggut have the Maternal Mortality affected by Health Care of Childbed (x4).

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