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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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Kota samarinda,
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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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Penentuan Besaran Premi Asuransi Jiwa dengan Model Apportionable Fractional Premiums Berdasarkan Tabel Mortalita dengan Metode Interpolasi Kostaki Muhammad Nor Abdul Rajak; Yuki Novia Nasution; Nanda Arista Rizki
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 (595.108 KB) | DOI: 10.30872/eksponensial.v9i1.272

Abstract

Insurance is an agreement between the customer and insurance company, at which the insurance company bears some loss in the future and the customer pays the premium according to the agreement. Insurance company determines the amount of premiums based on mortality tables. The purpose of the research is to determine the characteristics of Indonesia mortaliy table with Kostaki interpolation method, to determine whole life insurance premium with apportionable fractional premiums model, and to determine the amount of the premium return. The results of the research indicate that in the mortality table of Indonesia in 2014, the number of female deaths tend to be lower than male at 1-74 years, but the number of deaths increased over the age of 75 years. The premiums paid by a 30 year-old male with a semester payment is Rp 2.358.988, quarterly payment is Rp 1.186.823, and monthly payment is Rp 397.253. The premiums paid by a 30 year-old female with a semester payment is Rp 2.044.666, quarterly payment is Rp 1.028.669, and monthly payment is Rp 344.242. Premium return of 30 years-old male is Rp 84.204.338 and of 30 years-old female is Rp 72.968.560.
Pemodelan Penduduk Miskin di Provinsi Maluku Dengan Menggunakan Metode Backward Salmon N. Aulele; Henry W. M. Patty
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 (730.193 KB)

Abstract

Poverty is a complex issue, for it is not only related to the low level of income and consumption, but also to the low level of education, health and powerlessness to participate in development as well as various issues that are relevant to human development. According to the Badan Pusat Statistik (BPS), poverty is the inability to meet certain standards of basic needs, both food and non-food. The results of BPS survey on March 2017 showed that Maluku Province is ranked 4th in the poorest province in Indonesia with a poverty percentage of 18.45% of the total population in Maluku. This study aims to analyze the number of poor people in Maluku and its affecting variables by using backward method. The results show that Southwest Maluku Regency has the highest percentage of poor people in Maluku 31.01% and Ambon City has the lowest percentage of poor people with 4.64% of the poor. While the significant variables which affecting the percentage of poor people in Maluku are the percentage of households whose fuel for cooking is from wood; the percentage of population aged 7-24 years who is not / has never attended school; the percentage of population aged 7-24 years no longer schooling; the percentage of open unemployment rate; and the percentage of labor force participation rate per Regency/City in Maluku.
Peramalan Harga Minyak Mentah Menggunakan Model Autoregressive Integrated Moving Average Neural Network (ARIMA-NN) Laila Nur Qamara; Sri Wahyuningsih; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 10 No 2 (2019): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (647.856 KB)

Abstract

Crude oil prices can affect the production and consumption of a country. Crude oil prices are set every month and semester by Indonesian Crude Oil Price(ICP). Bontang Return Condensate (BRC) is one of crude oil type in Indonesia. Forecasting is an expectation of a request or thing that will come based on several forecasting variables.In this study, data was used in July 2010-December 2017. The purpose of this study was to determine the best model of Indonesian crude oil price data for the BRC type and the forecasting results. The model used in this study is the ARIMA-NN model which is a combination of ARIMA model and Neural Network (NN) model. The best ARIMA-NN model has ARIMA (2,1,0) and NN components with 2 inputs and 2 neurons in the hidden layer. The NN model is a Feed Forward Neural Network (FFNN) model with backpropagation algorithm. The results of the forecasting of the BRC Indonesia crude oil price for January-December 2018 are around the value of 60 USD / Barrel.
Regresi Nonparametrik Spline Birespon Untuk Memodelkan Persentase Penduduk Miskin dan Indeks Kedalaman Kemiskinan di Kalimantan Timur Tahun 2015 Ronald Tediwibawa; Desi Yuniarti; Memi Nor Hayati
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 (362.731 KB)

Abstract

State of Indonesia is a developing country which has a problem, namely poverty. Poverty is a condition that is often associated with the needs, difficulties and shortcomings in the various circumstances of life. Measuring poverty in a region that can be done by looking at two indicators, namely the percentage of the poor population and the poverty depth index. This study uses 5 factors thought to affect the percentage of poor people and the depth of poverty in East Kalimantan which includes the average of the old school, the open unemployment rate, the labor force participation rate, population growth rate and the expectancy of the old school. The Data used in this study is the data year 2015, which is obtained from the Central bureau of Statistics of East Kalimantan Province. The method used is a nonparametric regression spline-response and determine the value of the optimal knots point using the Generalized Cross Validation (GCV). The best Model resulting from this research is the model with the point of optimal knot with the value of GCV of 31.14057 and R-squared of 86.47.
Peramalan Menggunakan Metode Fuzzy Time Series Cheng Sumartini Sumartini; Memi Nor Hayati; Sri Wahyuningsih
EKSPONENSIAL Vol 8 No 1 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (513.357 KB)

Abstract

Forecasting process play an important role in time series data as required for decision-making process. Fuzzy Time Series (FTS) is a concept known as artificial intelligence which use to predict a problem where the actual data was formed in the values ​​of linguistic. This study discusses the FTS method developed by Cheng to forecast the Composite Stock Price Index (CSPI) in October 2016. Within FTS, long intervals determined in beginning process. Based on FTS Cheng method with interval determination using frequency distribution, forecasting stock index based on data from January 2011-September 2016 result forecast for the month of October 2016 was 5.367.98 points. Based on calculation of MAPE, CSPI data from January 2011-September 2016 had an error value as big as 2.56% and has an accuracy of forecasting results amounted to 97.44%. Forecasting use the FTS Cheng has a great performance because it has MAPE value below 10%.
Perbandingan Metode K-Means Dan Metode Fuzzy C-Means (FCM) Pada Analisis Kinerja Pegawai PT. Cemara Khatulistiwa Persada Bontang Rakhmawaty, Nurul; Nasution, Yuki Novia; Amijaya, Fidia Deny Tisna
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 (496.981 KB) | DOI: 10.30872/eksponensial.v13i1.886

Abstract

Discipline assessment of employee performance is one of the factors to improve the situation of the quality of human resources. Monitoring and assessment of employee discipline must be carried out continuously as some of the characteristics of management that have gone well as a benchmark for considering the targets that have been set. The K-Means method and the Fuzzy C-Means (FCM) method are non-hierarchical cluster methods. Both methods attempt to partition data into one or more clusters, so that data with the same characteristics are grouped into the same cluster or groups and data with different characteristics are grouped into other groups. This study discusses the comparison of the K-Means method and the Fuzzy C-Means (FCM) method in analyzing employee performance at PT. Cemara Khatulistiwa Persada Bontang, where groups of employees with high, medium, and low levels of employee performance will be determined based on the clustering results of the two methods and determine the best method. The grouping of data for the two methods was obtained from employee attendance data in 2020. Based on the results, it was found that clustering using the K-Means method in the first cluster (low performance level) had 23 employees, the second cluster (medium performance level) had 27 employees, and cluster the third (high performance level) there are 30 employees. Then based on the results of clustering using the FCM method in the first cluster (medium performance level) there are 26 employees, the second cluster (high performance level) there are 31 employees, and the third cluster (low performance level) there are 23 employees. Based on the results of the standard deviation ratio, it was obtained that the K-Means method with a value of 2.4944 was better than the FCM method with a value of 2.7323 in clustering employee performance at PT. Cemara Khatulistiwa Persada.
Aplikasi Metode Naive Bayes dalam Prediksi Risiko Penyakit Jantung M. Sabransyah; Yuki Novia Nasution; Fidia Deny Tisna Amijaya
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 (654.691 KB)

Abstract

Classification is an activity for assessing object data which include it the data into particular class among any number of classes available. Naive Bayes is classification with probability method. This research examines the use of naive Bayes method for a heart disease risk prediction application. In this research, it will be classified a person who have the risk of heart disease by using the data of patient in RSUD AWS during November and December 2016 the sample case is 47 years old male object, has cholesterol level of 198 mg/dL, has blood pressure of 131 mmHg, parents having heart disease medical record, suffering diabetes Mellitus, has obesity, has high dyslipidemia. It is concluded that the object falls into "potential category" of having heart disease. The classification result that has been done, the exact accuracy was obtained with 25 tested data and got accuracy level in an amount of 80% and 50 tested data sample and got accuracy level in an amount of 78%.
Estimasi Parameter Model Regresi Linier Berganda dengan Pendekatan Bayes Menggunakan Prior Pseudo: (Studi Kasus Indeks Pembangunan Manusia (IPM) di Kalimantan Timur) Isgiarahmah, Afryda; Goejantoro, Rito; Nasution, Yuki Novia
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 (431.932 KB) | DOI: 10.30872/eksponensial.v12i1.753

Abstract

The parameter estimation of a regression model can use the Ordinary Least Square (OLS) method which must fulfill the assumption of BLUE. Besides OLS, there is another method that can be used to estimate the regression parameters, namely the Bayes method. Parameter estimates using the OLS method and the Bayes method have been widely used in the field of development. One of them is on economic development, namely the Human Development Index (HDI). The purpose of this study is to know multiple linear regression models and interpretations that state the relationship between per capita expenditure, average length of school, life expectancy, and school length for the Human Development Index (HDI) with the Bayes approach using pseudo priors.
Analisis Survival pada Data Kejadian Bersama Menggunakan Metode Exact Partial Likelihood Rahmawati Isnaeni; Yuki Novia Nasution; Sri Wahyuningsih
EKSPONENSIAL Vol 9 No 2 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (555.604 KB)

Abstract

Survival data is the data of survival time until the appearance of certain events. In the survival analysis, ties are sometimes found, that is the situation where there are two or more individuals who experience the same event at the same time. There are several methods in estimating the parameters in the case of a ties, one of which is by applying the exact partial likelihood method. The exact method is the most accurate method, which can be applied in estimating Cox regression parameters from traffic accident data of Samarinda City in 2016. Traffic accidents are one of the most deadly events. The four main factors that cause traffic accidents are human factors, vehicles, roads, and weather or environmental factors. The variables used in this study are age, gender, role of victim, driver’s license, vehicle type, time of incident, line of the road, and weather. The results of analysis with the help of Rstudio software showed that the factors whose affect the fatality rate of traffic accident victims of Samarinda City are age and gender. For the age variables concluded that each addition of one year of age of the accident victim, the risk of dying from a traffic accident will also increase 1,0258 times. As for the gender variables concluded that the victim of male sex has a risk of 0.4180 times greater to die due to traffic accidents compared with female victims.
Perbandingan Hasil Klasifikasi Menggunakan Regresi logistik dan Analisis Diskriminan Kuadratik Pada Kasus Pengklasifikasian Jurusan Di SMA Negeri 8 Samarinda Tahun Ajaran 2014/2015 Cristine Uli Artha; Yuki Novia Nasution; Ika Purnamasari
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 (176.216 KB)

Abstract

Logistic Regression Analysis and Discriminant Analysis represent the statistical method for the classification of a number of object. In the case of classification especially if there's only two response categories, logistic regression is used more precisely if the assumption of multivariate normality in data cannot be fullfiled. The assumption of normality multivariate distribution and equality of variance covariance matrices represent the important matter in discriminant analysis for getting of high accuracy of classification. Discriminant analysis method that is used in inequality of variance covariance matrices is called quadratic discriminant analysis. The purpose of this study was to determine the classification results by using Logistic Regression and Quadratic Discriminant Analysis and compares the classification accuracy. The data that is used in the study is the average raport of the first and second semester of the class X at SMA Negeri 8 Samarinda academic year 2014/2015. Data consists of 190 students with two independent variables and four dependent variables. Based on research results, obtained results for the value of class accuracy is Logistic Regression 83.16% and Quadratic Discriminant Analysis 84.21%.

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