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Sri Wahyuningsih
Dosen Program Studi Statistika FMIPA Universitas Mulawarman
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Analisis Cluster Non-Hirarki Dengan Menggunakan Metode K-Modes pada Mahasiswa Program Studi Statistika Angkatan 2015 FMIPA Universitas Mulawarman Nur Amah; Sri Wahyuningsih; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 8 No 1 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Cluster analysis is a technique that used to categorize or classify object into clusters or group which is relatively homogeneous. This research aims to know the number of the best cluster used in the selection of Statistics major using K-Modes Cluster, which variable as the best center of cluster & the most optimum, and also comparison of the cluster based on the Davies-Bouldin Index (DBI) which is derived in each cluster are 2 clusters, 3 clusters, and 4 clusters. Steps in this research is descriptive analysis, validity and reliability of questionnaire, determine the number of clusters, compute the dissimiliarity distance, calculate the cluster validation and interpretate the result of the best cluster. Selection of the best cluster use the smallest value comparison. The smallest of the two clusters are 0,599. The center (centroid) of clusters variables which is the best optimum using K-Modes with two clusters are for the first centroid is the first choice of major, SNMPTN, IPK satisfactory, study routines for 4 times a week, and the average length of study is between 60 minutes to 120 minutes per day.; for the second centroid is the first choice of study program, SNMPTN, IPK is very satisfied, study routines for 6 times a week, and the average length of study is less than or equal to 60 minutes per day. The final results showed that the best cluster produced is two clusters where cluster 1 consisted of 37 students and cluster 2 consisted of 8 students.
Analisis Survival Lama Masa Pengobatan Dan Tingkat Kesembuhan Pasien Narkoba Di Lembaga Terapi Dan Rehabilitasi Pondok Pesantren Ibadurrahman Tenggarong Seberang Fathur Rachman; Sri Wahyuningsih; Yuki Novia Nasution
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Survival analysis is used to analyze of the long life data, in general this method used to estimate and the time curve survival which is Life Table Method, Model of Cox Proportional Hazard or the Cox model and Product Limit Method (Kaplan Meier). This script well knowing about the model of Cox Proportional Hazard for the influencing factors in the recovery term of the Narcotics Patients in the Institution of Therapy and Rehabilitation Pondok Pesantren Ibadurrahman Tenggarong Seberang and knowing of the influencing factors in the recovery term of the Narcotics Patients in the Institution of Therapy and Rehabilitation Pondok Pesantren Ibadurrahman Tenggarong Seberang. The research data is done for 114 of Narcotics Patients. The Procedural in making Cox Proportional Hazard model including to several parts, they are deciding of variables which used to, assumption exam of Cox Proportional Hazard model, choosing the best model with backward exam, deciding variable which influenced of the cure rates duration. The usage data are forming by 5 variables, such as Gender, Education, The use of Smoking, Ages, and Parenting, based on the research was found the model of Cox Proportional Hazard for the influence factors in hi curing is: hi(t,x)=exp(-0.694 x4) h0(t). The influence factors in curing of the Narcotics Patients are the age of the patient since the therapy.
Model Dinamis: Autoregressive Dan Distribusi Lag Muhajir Choir Nurahman; Sri Wahyuningsih; Desi Yuniarti
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Regression model using time series data not only use the effect of changing the independent variables on the dependent variable in the same period and for the same period of observation, but also use the period of time before. The purpose of this study was to determine the dynamic model autoregressive and distribution lag by type of infinite lag, and to know the effect of US dollar exchange rate against GDP in 1993-2013. Based on the analysis of data has that GDP and US dollar exchange rate has a rising trend pattern, and obtained by a simple regression model. But this model can not be used because of two assumptions have not been met and that there are heteroscedasticity and autocorrelation. So this model should be transformed using log, and log transformation model is obtained from a simple regression. The transportation model can be used as desiredint his model is only one assumption are not met and that there are autocorrelation. Then sub sequently estimating models and obtained Koyckas well as all assumptions are met, namely residual normal distribution, no problem heteroscedasticity and autocorrelation. Thus, the obtained dynamic distribution models also lag within finite lag types.
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

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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 Hasil Analisis Cluster dengan Menggunakan Metode Single Linkage dan Metode C-Means Maria Goreti; Yuki Novia Nasution; Sri Wahyuningsih
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Cluster analysis is one of the multivariate analysis which is used to classify objects into groups based on similarity of observed variables, in order to obtain the similarity of objects in the same group compared between objects of different groups. Cluster analysis is divided into two methods, they are is hierarchy method that start grouping with two or more objects that have the closest similarity and non-hierarchical method that begin with the process of determining the number of clusters in advance. This study aims is to determine whether there are differences in the results of the cluster grouping formed by using the hierarchy method, that is single linkage method, and non-hierarchical method, that is C-means method. Data, which is taken from the Environment Agency West Kutai, is data Ambient Air Quality Levels in Plantation Company in West Kutai in 2014. The results showed that based on the type of pollutants from all aleven the eleventh plantation companies have different results clusters formed from both methods which were used. With the characteristics of each cluster or groups: single linkage method for the first cluster has good air quality and its members as much as 7 companies, second Cluster both have poor air quality and its members as much as two companies and for the third Cluster have fairly good air quality and its members as much as 2 companies. As for the method of C-means for the first cluster has good air quality and its members as many as four companies, second Cluster both have poor air quality and its members as many as four companies and third Cluster have fairly good air quality and its members as much as 3 companies. For the average value of the ratio of standard deviation in the group (Sw) and between groups (Sb) by using the method of single linkage has a smaller value that is equal to 0.022 while C-means method is equal to 0.063. Thus, in the case of the classification of the ambient air quality in plantation companies in West Kutai 2014, single linkage method better at classifying than C-means method.
Peramalan dengan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) di Bidang Ekonomi Verawaty Bettyani Sitorus; Sri Wahyuningsih; Memi Nor Hayati
EKSPONENSIAL Vol 8 No 1 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

A present event is probably a reiteration from a past event. The reiteration of an event every particular time period indicates seasonal pattern. Seasonal Autoregressive Integrated Moving Average (SARIMA) is one of the methods that is used for data forecasting which has seasonal pattern. The purposes of this research are finding out the best SARIMA model and forecasting the inflation in Indonesia for period January 2016 until December 2016 using the best SARIMA model. Sample of this research is 96 Indonesia inflation data (mtm) for period January 2008 until December 2015. The technique of this research is purposive sampling. There are five steps of SARIMA method, those are model identification, model estimating, diagnostic checking, selecting the best model, and forecasting. Based on the analysis, the best SARIMA model is SARIMA (1,0,0)(0,1,0)12. The forecasting of Indonesia inflation 2016 has similar pattern with the previous time. The inflation increases in January 2016 and decreases in February 2016 until April 2016. The inflation increases again in Mey 2016 until August 2016 and decreases in September 2016 until November 2016. At last, the inflation increases in December 2016.
Co-Authors Andi Widya Rhezky Awalul Aziz Andini Juita Sari Ayu Wulandari David Siahaan Desi Yuniarti Dewi Wulan Sari Eric Sapto Raharjo Fathur Rachman Fidia Deny Tisna Amijaya Fitriani Fitriani Hasniah Hasniah Maria Goreti Memi Nor Hayati Muhajir Choir Nurahman Nissa Irabawati Nur Amah Nurmalia Purwita Yuriantari Rito Goejantoro Rudy Ramadani Syoer Sumartini Sumartini Verawaty Bettyani Sitorus Yuki Novia Nasution