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

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Penggunaan Metode Kaizen Pada Tahap Improve Dalam Six Sigma Yuliana Yuliana; Yuki Novia Nasution; Wasono Wasono
EKSPONENSIAL Vol 8 No 1 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Six sigma is a holistic approach to solve the cause of disabilityproductsproblems and improve processes through the DMAIC (Define, Measure, Analyze, Improve, Control). Analyze the causes of product defect using the proposed improvement of Kaizen that is Five-M Checklist, 5W+1H (What, Why, Where, When, When, Who, How), and Five Step Plans. Obtained a better quality thereby creating customer satisfaction. The purpose of this study were to determine the value of Defect Per Million Opportunities (DPMO), Critical To Quality (CTQ) products, and know the process of production of bottled water brand RAMA volume 220ml. The result showed DPMO value 45.808. The level of the company be at 3,186 sigma with Critical To Quality (CTQ) is lid at 41,3%, water volume at 27,1%, and glass at 25%. The p-chart is used before and after improvement in this study to analyze the number of defective product. The result showed that before the repair using analysis of Kaizen, there is a lot of data out of the control limits, whereas after repair using analysis of Kaizen there is no data out of the control limits and some data products were near the centerline of the control p-chart.
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.
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

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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%.
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

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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%.
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.
Klasifikasi Lama Masa Studi Mahasiswa Menggunakan Perbandingan Metode Algoritma C.45 dan Algoritma Classification and Regression Tree Hadi Dwi Darmawan; Desi Yuniarti; Yuki Novia Nasution
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Classification is the grouping samples based on the characteristics of the similarities and differences using target variable category. In this study, the decision tree is formed using C4.5 algorithm and Classification and regression tree (CART) algorithm to classify a student’s study period class of 2016 FMIPA UNMUL. C4.5 algorithm is a non binary classification tree where the branches of trees can be more than two on C4.5 algorithm, decision tree is established based on Entropy value. The purpose of CART algorithm is to get an accurate data as group identifier of a classification. CART can be applied in three main steps, namely the establishment of a classification tree, trimming of the classification tree, and determination of optimal classification tree. The main goal of this research is to determine factors which may effect on all predicate graduation who was graduated on 2016 using C4.5 algorithm and CART algorithm and also to know comparison accuracy of classification result by C4.5 algorithm and CART algorithm. The result showed that factors which affected the duration of all graduation using C4.5 algorithm are major (X4), region school (X5) and region origin (X3) and factors affected to the duration of all graduation using CART algorithm are major (X4) and Cumulative Achievement Index (X1). Precision classification in CART algorithm is better than C4.5 algorithm. C4.5 algorithm was able to predict with 40% accuracy while the CART algorithm has a predictive accuracy of 60%.
Co-Authors Akbar Maulana Andi Widya Rhezky Awalul Aziz Chairunnisa Chairunnisa Cristine Uli Artha Desi Yuniarti Dessy Ariani Fathur Rachman Fidia Deny Tisna Amijaya Fitriani Fitriani Hadi Dwi Darmawan Ika Purnamasari M. Sabransyah Maria Goreti Marisa Nanda Rahmaniah Muhammad Faisal Nadya Rahmawati Nanda Arista Rizki Ricca Noviani Sri Wahyuningsih Wasono Wasono Yuliana Yuliana Yurin Febria Suci