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All Journal Jurnal Matematika E-Jurnal Matematika Prosiding Seminar Nasional MIPA BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Ilmiah Ilmu Sosial Dinasti International Journal of Education Management and Social Science Jurnal Cahaya Mandalika JOURNAL SCIENTIFIC OF MANDALIKA (JSM)
I KOMANG GDE SUKARSA
Mathematics Department, Faculty Of Mathematics And Natural Sciences, Udayana University
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Transportation Other

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ANALISIS PENGARUH CITRA DESTINASI DAN MOTIF BERWISATA TERHADAP TINGKAT KEPUASAN WISATAWAN LANJUT USIA I MADE DANNY DANANJAYA; I PUTU EKA N. KENCANA; I KOMANG GDE SUKARSA
E-Jurnal Matematika Vol 6 No 2 (2017)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2017.v06.i02.p157

Abstract

The purpose of this research is to know the influence of the image of the destination towards internal motivation and external motivation, internal motivation and external motivation against travelers, the influence of taourist motivation towards the stratification of tourists holidaying in the village of Sanur. The population in this research is the elderly tourists on vacation to the village of Sanur with sample as much 100 sample and sample “technique used is Accidentalsampling techniques. This research method using Partial Least Square with 3 variables second order in the variable image of tourist destination, motivation, satification of tourists and 6 first orde variable the explain the motivation variable satisfaction of tourists and travelers.The results of the research show that the image of destinations likely to affect external motivation path coefficient with a value amounting to 0.939 on the tourist motivation more dominant external motivation that affect the value of the coefficient of 0.836. While the motivations of tourist proved to influence that satisfaction of travelers with coefficients of 0.402.
PENERAPAN REGRESI BINOMIAL NEGATIF UNTUK MENGATASI OVERDISPERSI PADA REGRESI POISSON PUTU SUSAN PRADAWATI; KOMANG GDE SUKARSA; I GUSTI AYU MADE SRINADI
E-Jurnal Matematika Vol 2 No 2 (2013)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2013.v02.i02.p031

Abstract

Poisson regression was used to analyze the count data which Poisson distributed. Poisson regression analysis requires state equidispersion, in which the mean value of the response variable is equal to the value of the variance. However, there are deviations in which the value of the response variable variance is greater than the mean. This is called overdispersion. If overdispersion happens and Poisson Regression analysis is being used, then underestimated standard errors will be obtained. Negative Binomial Regression can handle overdispersion because it contains a dispersion parameter. From the simulation data which experienced overdispersion in the Poisson Regression model it was found that the Negative Binomial Regression was better than the Poisson Regression model.
INTERPOLASI SPASIAL DENGAN METODE ORDINARY KRIGING MENGGUNAKAN SEMIVARIOGRAM ISOTROPIK PADA DATA SPASIAL (Studi Kasus: Curah Hujan di Kabupaten Karangasem) PUTU MIRAH PURNAMA D.; KOMANG GDE SUKARSA; KOMANG DHARMAWAN
E-Jurnal Matematika Vol 4 No 1 (2015)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2015.v04.i01.p084

Abstract

Spatial data is data that is presented in the geographic of an object, related to the location, shape and relationship of the earth in space. One of example of spatial data is rainfall. To determine the value of rainfall in an area, built to predict rain post information regarding rainfall. Spatial interpolation is used to estimate rainfall by collecting rainfall values held rain heading around. Assessment methods used in the estimate the rainfall in the Karangasem district is ordinary kriging using isotropic semivariogram that takes into account height on spatial data. Isotropic semivariogram which only takes into account the distance alone. Ordinary kriging method using isotropic semivariogram that takes into account height  value estimated rainfall is much different to the values at the control points Amlapura and Besakih. Interpolation on 3D data are not suitable for use on ordinary kriging method, grouping should be done at the data into a few weeks to application of ordinary kriging interpolation method using anisotropic semivariogram on 3D data.
PERBANDINGAN ANALISIS LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR DAN PARTIAL LEAST SQUARES (Studi Kasus: Data Microarray) KADEK DWI FARMANI; I PUTU EKA NILA KENCANA; KOMANG GDE SUKARSA
E-Jurnal Matematika Volume 1, No 1, Tahun 2012
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2012.v01.i01.p013

Abstract

Linear regression analysis is one of the parametric statistical methods which utilize the relationship between two or more quantitative variables. In linear regression analysis, there are several assumptions that must be met that is normal distribution of errors, there is no correlation between the error and error variance is constant and homogent. There are some constraints that caused the assumption can not be met, for example, the correlation between independent variables (multicollinearity), constraints on the number of data and independent variables are obtained. When the number of samples obtained less than the number of independent variables, then the data is called the microarray data. Least Absolute shrinkage and Selection Operator (LASSO) and Partial Least Squares (PLS) is a statistical method that can be used to overcome the microarray, overfitting, and multicollinearity. From the above description, it is necessary to study with the intention of comparing LASSO and PLS method. This study uses coronary heart and stroke patients data which is a microarray data and contain multicollinearity. With these two characteristics of the data that most have a weak correlation between independent variables, LASSO method produces a better model than PLS seen from the large RMSEP.
MODEL PERSAMAAN STRUKTURAL TINGKAT KEPUASAN MASYARAKAT TERHADAP KUALITAS PELAYANAN JALAN TOL BALI MANDARA I PUTU AGUS WIDHIANTARA; I KOMANG GDE SUKARSA; I PUTU EKA N. KENCANA
E-Jurnal Matematika Vol 4 No 4 (2015)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2015.v04.i04.p107

Abstract

The aim of this research is to determine public satisfaction level for the quality of Bali Mandara Highway service and to determine the dominant variable influencing public satisfaction level. This research implemented by using Structural Equation Modeling Partial Least Square (SEM-PLS) and Servqual model. This research was conducted in Badung Regency in the period of March to June 2015. Data were collected by using questionnaires that were distributed directly to 150 users of respondents. The result shows that the public haven’t been satisfied with service quality provided by Jasamarga. Meanwhile the empathy, responsiveness and tangible are significantly influencing public satisfaction level to Bali Mandara Highway service quality. We also showed that empathy provide a dominant influence to public satisfaction level.
PENERAPAN REGRESI QUASI-LIKELIHOOD PADA DATA CACAH (COUNT DATA) YANG MENGALAMI OVERDISPERSI DALAM REGRESI POISSON DESAK PUTU PRAMI MEITRIANI; KOMANG GDE SUKARSA; I PUTU EKA NILA KENCANA
E-Jurnal Matematika Vol 2 No 2 (2013)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2013.v02.i02.p036

Abstract

Poisson regression can be used to analyze count data, with assuming equidispersion. However, in the case of overdispersion often occur in the count data. The implementation of Poisson Regression can not be applied on this data because the data having overdispersion, that will lead to underestimate the standard error. Thus, use Quasi-Likelihood regression on this data. Quasi-Likelihood regression was also could not handle the overdispersion, but Quasi-Likelihood regression can improve the value of the standard error becomes greater than the value of the standard error on Poisson regression. Thus, by using the Quasi-Likelihood regression obtained three independent variables that affect the number of divorce cases in each urban city of Denpasar in 2011.
IMPLEMENTASI METODE BOOTSTRAP DALAM INFERENSI TITIK-TITIK BIPLOT AMMI MODEL AMMI CAMPURAN (MIXED AMMI) (Studi Kasus: Menduga Stabilitas Genotipe Padi) NI PUTU AYU DINITA TRISNAYANTI; I KOMANG GDE SUKARSA; NI LUH PUTU SUCIPTAWATI
E-Jurnal Matematika Vol 4 No 3 (2015)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2015.v04.i03.p098

Abstract

In this research bootstrap methods are used to determine the points inference of biplot figures on the analysis of AMMI. If the environmental factors are assumed to be random factors, then Mixed AMMI is used as a model of analysis.  In the analysis of the stabilit, the main components score interaction used are KUI1 and KUI2. The purpose of this study is to determine the Biplot figures based on two scores these are KUI with the greatest diversity of Mixed AMMI models and the points inference by using the bootstrap method. The stable genotypes obtained from biplot AMMI2 are G1, G5, and G6. Based on points inference of each genotype, G1 and G5 can be regarded as the most stable genotype. This is because the distribution of G1 and G5 are the closest to the center point (0,0) and both of them have  a small radius.
PENERAPAN REGRESI LOGISTIK ORDINAL UNTUK MENGANALISIS TINGKAT KEPARAHAN KORBAN KECELAKAAN LALU LINTAS KABUPATEN BULELENG DEWA AYU MADE DWI YANTI PURNAMI; I KOMANG GDE SUKARSA; G. K. GANDHIADI
E-Jurnal Matematika Vol 4 No 2 (2015)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2015.v04.i02.p089

Abstract

Ordinal logistic regression is a statistical method for analyzing the respone variables that have an ordinal scale consisting of three or more categories. This method is an extension of logistic regression with a binary respone variable. In this study the cases studies was the severity of traffic accident victims in Buleleng. The severity of the victims were divided into three categories: minor injuries, serious injuries and died. This research also used six predictor variables, namely age, hours of accident, education, gender, the status of location, and the venicles involved. Result of study shows that the variables age, hours of accident, education and the status of location have a significan effect on the severity of traffic accident victims.
PEMODELAN JUMLAH TINDAK KRIMINALITAS DI PROVINSI JAWA TIMUR DENGAN ANALISIS REGRESI SPATIAL AUTOREGRESSIVE AND MOVING AVERAGE NI MADE LASTI LISPANI; I WAYAN SUMARJAYA; I KOMANG GDE SUKARSA
E-Jurnal Matematika Vol 7 No 4 (2018)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2018.v07.i04.p224

Abstract

One of spatial regression model is spatial autoregressive and moving average (SARMA) which assumes that there is a spatial effect on dependent variable and error. SARMA can analyze the spatial effect on the higher order. The purpose of this research is to estimate the model of the total crime in East Java along with factors that affect it. The results show that the model can describe total crime in East Java is SARMA(0,1). The factors that influence the total crime are population density (), poverty total (), average length of education at every regency/city and error from the neigbors.
PERBANDINGAN REGRESI KOMPONEN UTAMA DAN ROBPCA DALAM MENGATASI MULTIKOLINEARITAS DAN PENCILAN PADA REGRESI LINEAR BERGANDA NI WAYAN YULIANI; I KOMANG GDE SUKARSA; I GUSTI AYU MADE SRINADI
E-Jurnal Matematika Vol 2 No 4 (2013)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2013.v02.i04.p050

Abstract

Multiple linear regression analysis with a lot of independent variable always makes many problems because there is a relationship between two or more independent variables. The independent variables which correlated each other are called multicollinearity. Principal component  analysis which based on variance covariance matrix is very sensitive toward the existence of outlier in the observing data. Therefore in order to overcome the problem of outlier it is needed a method of robust estimator toward outlier. ROBPCA is a robust method for PCA toward the existence of outlier in the data. In order to obtain the robust principal component is needed a combination of Projection Pursuit (PP) with Minimum Covariant Determinant (MCD). The results showed that the ROBPCA method has a bias parameter and Mean Square Error (MSE) parameter lower than Principal Component Regression method. This case shows that the ROBPCA method better cope with the multicollinearity observational data influenced by outlier.
Co-Authors ADI PUTRAYASA ALEXANDER JOSEPH RIADI ANAK AGUNG ISTRI AYU PRATAMI Damayanthi, Ni Wayan Rita Desak Putu Eka Nilakusmawati DESAK PUTU PRAMI MEITRIANI DEWA AYU MADE DWI YANTI PURNAMI DIAN PRAMESTI DEWI DIAN RAHMAN Diana Diana DINI AMALIA PUTRI DOMINGGAS TEO DWI HERAYANTHI W. Eka N Kencana EKA N. KENCANA EKA N. KENCANA Eka N. Kencana EVI NOVIYANTARI FATIMAH G. K. GANDHIADI G.K. GANDHIADI Gandhiadi, GK Gandhiadi, I. G. K GEDE ARY PRABHA YOGESSWARA GUSTI AYU MADE ARNA PUTRI HANY DEVITA I GEDE AGUS JIWADIANA I GEDE SEKA SUYOGA I Gusti Ayu Made Srinadi I KETUT PUTRA ADNYANA I MADE ARYA ANTARA I MADE CANDRA SATRIA I MADE DANNY DANANJAYA I PUTU AGUS WIDHIANTARA I PUTU EKA IRAWAN I PUTU EKA N. KENCANA, I PUTU EKA N. I Putu Eka Nila Kencana I Wayan Sumarjaya I WAYAN WIDHI DIRGANTARA IA KOMANG MERIANI IDA AYU MADE SUPARTINI IDA AYU PRASETYA UTHAMI Isabel Divya Georgiana Walewangko KADEK DWI FARMANI Kencana, Eka N Kencana, Eka N. Ketut Jayanegara KOMANG AYU YULIANINGSIH Komang Dharmawan LUH KOMANG MARDIANI Luh Putu Trisna Darmayanti Made Susilawati MOCH. ANJAS A MODANA LOLITA Mohamad Dwi Agus Arianto NADIYA YUVITA RIZKI NGURAH GDE PRABA MARTHA NI GUSTI KETUT TRISNA PRADNYANTARI NI KADEK ARISKA DEWI NI KADEK SETIAWATI NI KETUT TRI UTAMI NI KOMANG AYU SRI CAHYANI NI LUH ARDILA KUSUMAYANTI NI LUH GEDE SINTA ARYATI Ni Luh Putu Suciptawati NI LUH SUKERNI Ni Made Asih NI MADE LASTI LISPANI NI MADE METTA ASTARI Ni Made Santiningsih NI MADE SEKARMINI NI MADE SUKMA PERTIWI NI MADE SUMA FRIDAYANI NI PUTU AYU DINITA TRISNAYANTI NI PUTU JULIANINGSIH NI PUTU NADYA AGUSVIANI NI WAYAN AMANDA DEWI SULISTYANINGSIH NI WAYAN NINING ISMIRANTI NI WAYAN YULIANI NISA HIDAYATI NOVA SARI BARUS NYOMAN GDE PRAJNAWIWEKA RATMASA TARAM PUTU AYU MAZIYYA PUTU EKA SWASTINI PUTU GDE BUDHA WIRYADANA PUTU MIRAH PURNAMA D. PUTU SUSAN PRADAWATI Ratna Sari Widiastuti REYNALDO PANJI WICAKSONO Riadi, Alexander Joseph Safitri, Asa Vira Tjokorda Bagus Oka TRI ALIT TRESNA PUTRA VANIA RISKASARI YR Wijayakusuma, I Gusti Ngurah Lanang Yasmin Roni Mz