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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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MENGATASI HETEROSKEDASTISITAS PADA REGRESI DENGAN MENGGUNAKAN WEIGHTED LEAST SQUARE PUTU AYU MAZIYYA; I KOMANG GDE SUKARSA; NI MADE ASIH
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.p083

Abstract

In the regression analysis we need a method to estimate parameters to fulfill the BLUE characteristic. There are assumptions that must be fulfilled homoscedasticity one of which is a condition in which the assumption of error variance is constant (same), infraction from the assumptions homoskedasticity called heteroscedasticity. The Consequence of going heteroscedasticity can impact OLS estimators still fulfill the requirements of not biased, but the variant obtained becomes inefficient. So we need a method to solve these problems either by Weighted Least Square (WLS). The purpose of this study is to find out how to overcome heteroscedasticity in regression with WLS. Step of this research was do with the OLS analysis, then testing to see whether there heteroscedasticity problem with BPG method, the next step is to repair the beginning model by way of weighting the data an exact multiplier factor, then re-using the OLS procedure to the data that have been weighted, the last stage is back with a heteroscedasticity test BPG method, so we obtained the model fulfill the assumptions of homoskedasicity. Estimates indicate that the WLS method can resolve the heteroscedasticity, with exact weighting factors based on the distribution pattern of data seen.
APLIKASI MULTIVARIATE MULTIPLE REGRESSION UNTUK MENDUGA FAKTOR-FAKTOR YANG MEMENGARUHI KESEJAHTERAAN MASYARAKAT PUTU EKA SWASTINI; I KOMANG GDE SUKARSA; I PUTU EKA N. KENCANA
E-Jurnal Matematika Vol 3 No 3 (2014)
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.2014.v03.i03.p071

Abstract

This essay aimed to apply the Multivariate Multiple Regression (MMR) methodfor the welfare issue. The predictor variables in the model are 18 indicators of welfare according to Indonesian Central Bureau of Statistic (BPS) and  the response variables are Human Development Index (IPM), Gross Regional Domestic Product (PDRB), and Regional Crime Index (IKD). In modeling the relationship between q responses and a single set of predictor variables , MMR assumed each pairs of two response variables were correlated and its distribution follows normal multivariate. Based on the result of MMR, we obtained six out of 18 predictor variables simultaneously affect IPM and  PDRB. The final model showed the association between those variables very closed to 100 percent.
PENERAPAN REGRESI ZERO INFLATED POISSON UNTUK MENGATASI OVERDISPERSI PADA REGRESI POISSON (Studi Kasus: Ketidaklulusan Siswa SMA/MA dalam Ujian Nasional di Buleleng) LUH KOMANG MARDIANI; KOMANG GDE SUKARSA; I GUSTI AYU MADE SRINADI
E-Jurnal Matematika Vol 2 No 3 (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.i03.p044

Abstract

The Poisson regression analysis is one of the regression methods used for count data and has the assumption of equidispersion. However, it is the overdispersion and then underestimate standard errors will be obtained. If the data are overdispersed and more data is zero then ZIP (Zero Inflated Regression) regression is used. ZIP regression model is more appropriate to be used to analyze the amount of Senior High School/Madrasah Aliyah who do not pass the exam with five independent variables, because a lot of data failure is zero. In this paper, data are overdispersed on Poisson regression, so ZIP regression are used. ZIP regression models obtained are only influenced by the proportion of Senior High School/Madrasah Aliyah classroom were damaged (X3), is and .
PERBANDINGAN INTERPOLASI SPASIAL DENGAN METODE ORDINARY DAN ROBUST KRIGING PADA DATA SPASIAL BERPENCILAN (Studi Kasus: Curah Hujan di Kabupaten Karangasem) NI MADE SUMA FRIDAYANI; 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.p012

Abstract

Kriging as optimal spatial interpolation can produce less precise predictive value if there are outliers among the data. Outliers defined as extreme observation value of the other observation values that may be caused by faulty record keeping, improper calibration equipment or other posibbilities. Development of Ordinary Kriging method is Robust Kriging which transforms weight of clasic variogram thus become variogram that robust to outlier. The spatial data that used in this research is the spatial data that contains outliers and meet the assumptions of Ordinary Kriging. The analysis showed that the estimation value of Ordinary Kriging and Robust Kriging method is not much different in terms of Mean Absolute Deviation values which generated by both methods. An increase value of Mean Absolute Deviation on Robust Kriging estimation does not indicate that the Ordinary Kriging method is more precise than Robust Kriging method in the rainfall estimates of Amlapura control point remind that Robust Kriging does not eliminate the data of observation such as the Ordinary Kriging method. In general, Ordinary Kriging and Robust Kriging method can estimate the rainfall value of Amlapura control point quite well although it is not able to cover the changes in rainfall value that occurs due to the behavior geographic data.
PENGGOLONGAN UANG KULIAH TUNGGAL MENGGUNAKAN SUPPORT VECTOR MACHINE I GEDE SEKA SUYOGA; I PUTU EKA NILA KENCANA; I KOMANG GDE SUKARSA
E-Jurnal Matematika Vol 6 No 4 (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.i04.p169

Abstract

Tuition fee is the payment of tuition fees each semester borne by each student based on their economic capabilities. Tuition fee is divided into five groups from tuition fee group 1 to tuition fee group 5. This research aims to find the accuracy of the classification of tuition fee using Support Vector Machine (SVM). SVM is a method used for classification of the concept to find hyperplane (separator function) that can separate the data into a predetermined class. In this research, SVM is used to determine the accuracy of tuition fee classification. The variables used are income parents, father’s occupation, mother’s occupation, home ownership status, building, land area, electricity cost, water cost, phone cost, saving accounts, jewelry ownership, and a premium ownership. The results obtained are five hyperplanes to separate tuition fee with accuracy of the classification of tuition fee was 59,69%.
PENERAPAN REGRESI PROBIT BIVARIAT UNTUK MENDUGA FAKTOR-FAKTOR YANG MEMENGARUHI KELULUSAN MAHASISWA (Studi Kasus: Mahasiswa Fakultas MIPA Unversitas Udayana) NI GUSTI KETUT TRISNA PRADNYANTARI; I KOMANG GDE SUKARSA; NI LUH PUTU SUCIPTAWATI
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.p088

Abstract

The aim of this research to estimate the factors that affect students graduation using bivariate probit regression. Bivariate probit regression is a statistical method that involves two response variables which are qualitative and the independent variables are qualitative, quantitative, or a combination of both. In bivariate probit regression model, the result obtained is the probability of the response variable. The result of this research are the factors that affect significantly for students graduation based on study period are majors, sex, and duration of the thesis, while the factors that significantly for students graduation based on GPA are the entry system, duration of the thesis and the number of parents’ dependents.
ESTIMASI SINTASAN PENDERITA DIABETES MELITUS: KOMPARASI KINERJA REGRESI PLS DAN LASSO GEDE ARY PRABHA YOGESSWARA; EKA N. KENCANA; 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.p223

Abstract

Partial least squares (PLS) regression and least absolute shrinkage and selection operator (LASSO) are the regression analysis techniques used to overcome the problems that can not be solved by ordinary least squares (OLS). The purpose of this research is to model and compare the performance of both PLS regression and LASSO to the diabetes mellitus study data which is divided into 30 groups of data redundancy as an example of microarray data. The survival time of diabetes mellitus patients as dependent variable while age, sex, body mass index, blood pressure, and six blood serum measurements as independent variables. By using paired sample t-test of adj R2 value, the result of this research concluded that the mean of adj R2 value of PLS regression is smaller than the mean of adj R2 value of LASSO. In other words, the performance of LASSO is better than PLS regression.
PENGELOMPOKKAN KABUPATEN DI PROVINSI BALI BERDASARKAN PERKEMBANGAN FASILITAS PARIWISATA NOVA SARI BARUS; I PUTU EKA NILA KENCANA; KOMANG GDE SUKARSA
E-Jurnal Matematika Vol 2 No 3 (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.i03.p049

Abstract

The development of tourism facilities in each district/city between districts with each district is different. The purpose of this study was to determine the grouping of several districts in the province of Bali is based on the number of tourism facilities as well as what the variable identifier of each group. The analysis used in this study is the analysis of clusters and Biplot. The data used is secondary data obtained from the Bali Tourism Office in 2011. Variables used are classified hotel, non-classified hotels, home stays, restaurants, bars, tourist destination, the area of ??tourism, water tourism and business. The results of this study formed three groups: group 1 Bangli, Buleleng, Jembrana District, Klungkung, Karangasem district and Denpasar City. Group 2 and group 3 Badung district of Gianyar district. Variable identifier is a restaurant for all variables.
POTRET KESEJAHTERAAN RAKYAT DI PROVINSI BALI MENGGUNAKAN METODE CHERNOFF FACES I WAYAN WIDHI DIRGANTARA; KOMANG GDE SUKARSA; KOMANG DHARMAWAN
E-Jurnal Matematika Vol 2 No 3 (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.i03.p040

Abstract

Chernoff Faces method is a graphical method of visualization techniques to present data with many variables in the form of a cartoon face which can be determined by 20 parameters or less. In this research it was shown how the Chernoff Faces method was used to see welfare of the people in the province of Bali and Bali's nine regencies. To pair the variables and Chernoff’s facial features, then we used  Principal Component Analysis and survey to make the faces look more human. The result from 18 indicators of welfare of the people in the province of Bali, only 8 indicators were not really well. It was obtained too that Tabanan was the most prosperous regency and Karangasem was the lest prosperous regency.
MEMODELKAN TINGKAT PENGANGGURAN DI KOTA DENPASAR DENGAN PENDUGAAN AREA KECIL EMPIRICAL BAYES REYNALDO PANJI WICAKSONO; I KOMANG GDE SUKARSA; I PUTU EKA NILA KENCANA
E-Jurnal Matematika Vol 10 No 2 (2021)
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.2021.v10.i02.p325

Abstract

Economic development are described by the unemployment rate. The higher unemployment rate, the weaker economic conditions. Nowadays more policies require information on small areas. The direct estimation does not provide accurate results in smaller areas. Thus the small area estimation becomes an alternative to estimate the parameters. The accuracy depends on the selection of the predictors. In 2019, the unemployment rate in Denpasar is 2,22%. The result shows that the unemployment rate in each district in Denpasar varies from 0,1% to 0,3%
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