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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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Perbandingan Metode C-Means dan Fuzzy C-Means Pada Pengelompokan Kabupaten/Kota Di Kalimantan Berdasarkan Indikator IPM Tahun 2019 Mahmudi, Mahmudi; Goejantoro, Rito; Amijaya, Fidia Deny Tisna
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 (879.164 KB) | DOI: 10.30872/eksponensial.v12i2.814

Abstract

The Human Development Index is an indicator used to measure one important aspect related to the quality of the results of economic development, namely the degree of human development. Data Mining is a technique or process for obtained information from large database warehouses. Based on its function, one of the data mining tasks was to group data, where the method used in this study was the C-Means and Fuzzy C-Means grouping methods. The two classification methods were applied to the human development index indicator data. The purpose of this study was to determined the best method based on the ratio of the standard deviation in clusters to the standard deviation between clusters. Based on the results of the analysis, it was concluded that the best method is the C-Means method with the value of the standard deviation value in the cluster against the standard deviation between clusters of 0.434 which results in 5 clusters, namely cluster 1 consisting of 9 districts / cities, cluster 2 consisting of 7 districts / cities, cluster 3 consists of 10 districts / cities, cluster 4 consists of 15 districts / cities and cluster 5 consists of 15 districts / cities.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (687.398 KB) | DOI: 10.30872/eksponensial.v11i2.655

Abstract

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.
Aplikasi Critical Path Method (CPM) dengan Crashing Program untuk Mengoptimalkan Waktu dan Biaya Proyek Try Hardini Rahayu Mukti; Ika Purnamasari; Wasono Wasono
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 (488.402 KB)

Abstract

The management of small and large-scale projects requires good planning, scheduling and coordination. Critical Path Method (CPM) is one of the method that has been developed to overcome the problem of managing a project. Time and cost greatly affect the success and failure of a project. A desired project is completed within a predetermined time, it can accelerate the duration of the activity with the consequence of an increase in cost. Acceleration of project duration at the lowest possible cost is called crashing program. The purpose of this study is to determine the optimal time and cost in completing the project. The data used is the type of work and time of completion of project work and wage costs of workers wages in the project development of SMP Negeri 24 Jalan Pangeran Suryanata Samarinda. Based on the analysis using the time efficient CPM method for completion of the project is 185 days. Acceleration of project completion time by using crashing program, project can be done for 157 days with increase of worker wage equal to Rp 473,802,785.32.
Analisis Faktor Konfirmatori untuk Mengetahui Faktor-Faktor yang Mempengaruhi Prestasi Mahasiswa Program Studi Statistika FMIPA Universitas Mulawarman Andini Juita Sari; Desi Yuniarti; 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 (263.804 KB)

Abstract

Confirmatory factor analysis is one part of the multivariate analysis. In this study conducted a confirmatory factor analysis of statistics student of Mulawarman University in 2013, 2014, and 2015 of 159 with research aims to determine the factors affecting the achievement of students. The analysis showed that, is influenced by four latent variables are latent variables background (ξ1) with three indicator variables of the relation with family (X1), parental (X2), and the motivation of the family (X3). Latent variables learning environment outside the campus (ξ2) with two indicator variables are the concentrations studied (X6) and the completion of tasks (X7). Latent variables campus facilities (ξ3) with indicator variables study room (X8), reading room of statistics (X9), wifi (X10), and computer facilities laboratory (X11). Latent variable students perceptions of lecturers (ξ4) with two indicator variables the learning system of lecturers (X14) and system administration duties of lecturers (X15). Indicator variables give large contribute affect to student achievement is the completion of the task (X7) rated loading factor of 0.89.
Perbandingan Kinerja Metode Klasifikasi Chi-square Automatic Interaction Detection (CHAID) dengan Metode Klasifikasi Algoritma C4.5 pada Studi Kasus : Penderita Diabetes Melitus Tipe 2 Di Samarinda Tahun 2015 Muhammad Faisal; 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 (712.426 KB)

Abstract

C4.5 algorithm is tree classification where tree branches can be more than two. In C4.5 algorithm, the decision tree is based on entropy and gain criterias. Chi-Squared Automatic Interaction Detection (CHAID) classification method is a methods which is used to divide data to become a smaller groups based on categorical dependent and independent variables. The purpose of this research is to determine the classification process by C4.5 algorithm and CHAID method for DM type 2 patients. Risk factors for diabetes type 2 are Decline, Age, Gender, Status of Obesity, Diet, and Sports Activity based on the availability of source data from the Hospital of Abdul Wahab Sjahranie Samarinda. The results show that factors which significantly affect the DM type 2 patients are Obesity and Sport Activity. While by using CHAID, the factors which significantly affect the patients are Decline, Obesity, Diet and Sports Activity. The Classification result accuracy of the C4.5 algorithm is 90% and 94% for CHAID method.
Model Spatial Autoregressive Moving Average (SARMA) pada Data Jumlah Kejadian Demam Berdarah Dengue (DBD) di Provinsi Kalimantan Timur dan Tengah Tahun 2016 Sari, Devi Nur Endah; Hayati, Memi Nor; Wahyuningsih, Sri
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 (963.921 KB) | DOI: 10.30872/eksponensial.v11i1.645

Abstract

Spatial Autoregressive Moving Average (SARMA) is a spatial regression model that uses the regional approach. The weighting matrix used is an adjacency matrix which is based on the intersection between observed locations. This study was conducted to determine the SARMA model and the factors that influence the number of cases of dengue hemorrhagic fever (DHF) in the provinces of East Kalimantan and Central Kalimantan in 2016. Based on the results of the Moran's Index test, there is a spatial autocorrelation on the number of dengue events in East Kalimantan Province and Central Kalimantan in 2016. The Lagrange Multiplier (LM) test has a spatial lag on the dependent variable and the error variable, which is a parameter and that is significant to the significance level . Based on the results of SARMA modeling that the factors that influence the number of dengue events in the provinces of East Kalimantan and Central Kalimantan in 2016 are the percentage of population density, the percentage of healthy houses, and the percentage of puskesmas.
Pemodelan Generalized Space Time Autoregressive (GSTAR) Pada Data Inflasi di Kota Samarinda dan Kota Balikpapan Riska Handayani; Sri Wahyuningsih; Desi Yuniarti
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 (784.445 KB)

Abstract

One of the macroeconomic indicators used in the preparation of government’s economicpolicy is inflation. Inflation is a data time series monthly that also is influenced by location effects. Generalized Space Time Autoregressive (GSTAR) is a time series methode that combines time and location effects. The case study is applied of GSTAR for forecasting inflation in two cities in East Kalimantan namely Samarinda and Balikpapan. This research aims to implement GSTAR model to gain forecasting model for inflation data in Samarinda city and Balikpapan city by using method of cross-correlation normalization. The resulting model is GSTAR model GSTAR (2,1) and GSTAR (3,1). The model obtained is not feasible to be used for forecasting, because it does not meet the white noise assumption.
Penerapan Metode Fuzzy Time Series Using Percentage Change Nurul Hidayah; Ika Purnamasari; Memi Nor Hayati
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 (107.311 KB)

Abstract

In 1993, Song and Chissom introduce fuzzy times series is capable of handling the problem of data forecasting if historical data are the values ​​of linguistic. The study uses the modeling outline by way of fuzzy relation equations and approximate reasoning to predict the number of students. In this study, the approach to the theory of fuzzy time series used is fuzzy time series using percentage change developed by Stevenson and Porter in 2009. The case studies used in this study is the population of East Kalimantan Province. This study aims to determine how the application of fuzzy time series method using percentage change in the population of East Kalimantan from 1980 until 2013. Forecasting is done menggukan linguistic value of the fuzzy set which is formed of the differences and converted into a percentage of the universe of discourse as a value data. Based on the results of the application of the method using fuzzy time series of the percentage change obtained 12 fuzzy set which is linguistics of the data, the accuracy of forecasting value from 1981 to 2013 using MAPE (Avarage Forcasting Error Rate) that is equal to 0.557%.
Pemantauan Peramalan Akseptor KB Baru Provinsi Kalimantan Timur Menggunakan Simple Moving Average dan Weighted Moving Average dengan Metode Tracking Signal Eric Sapto Raharjo; Memi Nor Hayati; Sri Wahyuningsih
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 (218.042 KB)

Abstract

Simple moving average (SMA) is the basic method used to measure seasonal variations. This method is done by moving the average value counted along the time series. Weighted moving average (WMA) includes selecting weights may be different for each data value and then calculating the weighted average time period of k, the value obtained as the smoothed value.The purpose of this study was to determine the method and the best forecasting model with the results of forecasting on new data on the number of new acceptors KB using tracking signal. Results of this study is to model 3 SMA method is the best monthly tracking signal with a value of -0.0349 to -0.0178 β = 0.1 and β = 0.2 for the forecasting results for the period of January, February, and March 2015 amounted to 8.151, 8.131, and 7.485. For model 3 monthly WMA method is best with a variety of weights W1 = 0.25; W2 = 0.35; W3 = 0,40 tracking signal has a value of -0.0451 to -0.0439 β = 0.1 and β = 0.2 for the forecasting results for the period of January, February, and March 2015 for 8.044, 7.893, and 7.517 , In this case the method of 3-month SMA model is the most appropriate method to forecast the number of new acceptors KB East Kalimantan province.
Peramalan Inflasi Kota Balikpapan Menggunakan Metode Singular Spectrum Analysis Sergio, Andrean; Wahyuningsih, Sri; Siringoringo, Meiliyani
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 (1290.292 KB) | DOI: 10.30872/eksponensial.v14i1.1098

Abstract

Singular Spectrum Analysis (SSA) is a nonparametric forecasting method capable of separating time series data into interpretable trend, seasonal, cycle, and noise. Methods with component separation are suitable for characterizing economic and business data trends that tend to contain stationary, trend, cycle, and seasonal factors. One of the economic data that can be used in research is inflation. The purpose of this study is to obtain the results of inflation forecast in Balikpapan City from November 2022 to October 2023. Based on the forecasting results of the SSA method on inflation in Balikpapan City, the MAAPE value was 23.53% which showed that the forecasting results were quite accurate. Based on the results of inflation forecast from November 2022 to October 2023, there was a decrease in inflation in November 2022 by -0.64% or it could be said that there would be deflation by 0.64%. Over the next period, inflation tends to increase where the highest inflation will occur in June 2023, which is 1.96%.

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