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ANALISIS DISKRIMINAN PADA KLASIFIKASI DESA DI KABUPATEN TABANAN MENGGUNAKAN METODE K-FOLD CROSS VALIDATION IDA AYU MADE SUPARTINI; I KOMANG GDE SUKARSA; I GUSTI AYU MADE SRINADI
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.p154

Abstract

Tabanan Regency is one of the eight regencies and one municipality in Bali Province. Administratively, it is divided into 10 districs and villages. There are rural areas and urban areas in the regions. Discriminant analysis is a technique related to the separation of objects into different groups that have been set previously. The purpose of this research is to classify villlages in Tabanan Regency into urban or rural groups with discriminant analysis. Linear discriminant analysis assumes that the covariance matrix of the two groups are equals, if the assumption of equality of covariance matrix is violated, quadratic discriminant analysis can be used for classification. This research uses k-fold crosss validation method for calculating the accuracy of quadratic discriminant function where . Quadratic discriminant function is obtained by with the smallest APER value (). All of classification results are stable and consistence.
PREDIKSI WAKTU KETAHANAN HIDUP DENGAN METODE PARTIAL LEAST SQUARE PANDE PUTU BUDI KUSUMA; I GUSTI AYU MADE SRINADI
E-Jurnal Matematika Vol 2 No 1 (2013): E-Jurnal Matematika
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.i01.p028

Abstract

Coronary heart disease is caused due to an accumulation of fat on the inside walls of blood vessels of the heart (coronary arteries). The factors that had led to the occurrence of coronary heart disease is dominated by unhealthy lifestyle of patients, and the survival times of different patients. This research objective is to predict the survival time of patients with coronary heart disease by taking into account the explanatory variables were analyzed by the method of Partial Least Square (PLS).  PLS method is used to resolve the multiple regression analysis when the specific problems of multicollinearity and microarray data. The purpose of the PLS method is to predict the explanatory variables with multiple response variables so as to produce a more accurate predictive value.  The results of this research showed that the prediction of survival for the three samples of patients with coronary heart disease had an average of 13 days, with a RMSEP value (error value) was 1.526 which means that the results of this study are not much different from the predicted results in the field of medicine. This is consistent with the fact that the medical field suggests that the average survival for patients with coronary heart disease by 13 days.
MODEL GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) FAKTOR-FAKTOR YANG MEMENGARUHI KECELAKAAN LALU LINTAS DI PROVINSI BALI NI KADEK ENDAH YANITA UTARI; I GUSTI AYU MADE SRINADI; MADE SUSILAWATI
E-Jurnal Matematika Vol 8 No 2 (2019)
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.2019.v08.i02.p245

Abstract

The number of traffic accidents in Bali kept increasing since 2015 until 2017. The factors that affected the traffic accidents in every region were suspected to be varied according to geographic position. This geographic effect was known as spatial heterogeneity. Spatial heterogeneity was analized by using Geographically Weighted Regression (GWR). This study aim to model the factors which affected the traffic accidents in every subdistrict in Bali by using fixed and adaptive gaussian kernel. The result showed that GWR with adaptive gaussian kernel was better at estimated the models because it had higher value of which was at . The factors which significantly affected the number of traffic accident in 57 subdistrict in Bali were the average rainfall and the number of population within age of 15 to 29 years old.
PERAMALAN JUMLAH PENDERITA DEMAM BERDARAH DENGUE DI KOTA DENPASAR MENGGUNAKAN MODEL FUNGSI TRANSFER MULTIVARIAT NOVIAN ENDI GUNAWAN; I WAYAN SUMARJAYA; I GUSTI AYU MADE SRINADI
E-Jurnal Matematika Vol 7 No 1 (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.i01.p186

Abstract

Forecasting is a way to predict future events. One model in forecasting is a transfer function. The transfer function is a forecasting model that combines characteristics of the ARIMA model with some characteristics of regression analysis. Dengue Hemorrhagic Fever is a major problem in Bali. Recorded Bali Province ranked fourth in the spread of dengue virus and Denpasar City ranked first in the number of death cases of Dengue Hemorrhagic Fever. The purpose of this research is to know the multivariate transfer function model and the prediction of people with Dengue Hemorrhagic Fever in Denpasar City based on the level of rain and humidity. Forecasting results in 2017 in January to June were 46, 51, 226, 625, 1064, 1001, and 580 peoples with a percentage error model transfer function of 17.2%.
PERBANDINGAN TRANSFORMASI BOX-COX DAN REGRESI KUANTIL MEDIAN DALAM MENGATASI HETEROSKEDASTISITAS NI WAYAN YUNI CAHYANI; I GUSTI AYU MADE SRINADI; MADE SUSILAWATI
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.p081

Abstract

Ordinary least square (OLS) is a method that can be used to estimate the parameter in linear regression analysis. There are some assumption which should be satisfied on OLS, one of this assumption is homoscedasticity, that is the variance of error is constant. If variance of the error is unequal that so-called heteroscedasticity. The presence heteroscedasticity can cause estimation with OLS becomes inefficient. Therefore, heteroscedasticity shall be overcome. There are some method that can used to overcome heteroscedasticity, two among those are Box-Cox power transformation and median quantile regression. This research compared Box-Cox power transformation and median quantile regression to overcome heteroscedasticity. Applied Box-Cox power transformation on OLS result ????2point are greater, smaller RMSE point and confidencen interval more narrow, therefore can be concluded that applied of Box-Cox power transformation on OLS better of median quantile regression to overcome heteroscedasticity.
ANALISIS KEMISKINAN DENGAN PENDEKATAN MODEL REGRESI SPASIAL DURBIN (Studi Kasus: Kabupaten Gianyar) LUH PUTU SAFITRI PRATIWI; I GUSTI AYU MADE SRINADI; MADE SUSILAWATI
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.p042

Abstract

Poverty still a complex problems for both at national and regional level, so it requires an appropriate and sustainable strategy mitigation. Every household in the different regions has different characteristics and influenced factors, so it requires the identification of the factors that influence poverty by paying attention to the influence of the area using the Spatial Durbin Model (SDM). The purpose of SDM modeling is to determine the spatial dependency relationship which occur not only in the dependent variables, but also on the independent variables. The result shows that the significant lagged dependent variable is indicated by the parameter Lag significant independent variables are independent variables with a significant weighting, but there are no independent variables that are significant with the weighting.
BOOTSTRAP AGGREGATING (BAGGING) REGRESI LOGISTIK ORDINAL UNTUK MENGKLASIFIKASIKAN STATUS GIZI BALITA DI KABUPATEN KLUNGKUNG PALUPI PURNAMA SARI; MADE SUSILAWATI; I GUSTI AYU MADE SRINADI
E-Jurnal Matematika Vol 5 No 3 (2016)
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.2016.v05.i03.p128

Abstract

This research was conducted to determine the variables that significantly influence nutritional status of children based on indicators that defined as height for age (H/A) and to classify children nutritional status into normal, short or very short categories. Height for age (H/A) is indicator used to describe the circumstances of malnutrition short. Short children (stunting) is  children who fail to reach optimal growth. The secondary data was list of 116 data of children aged 24-59 months at UPT. Puskesmas Klungkung I in 2015. The method was used was ordinal logistic regression and bagging ordinal logistic regression. Based on the research results, it was obtained variables children body length at birth, birth weight, and length of mid-upper arm circumference (MUAC) in pregnant woman were significantly affects the nutritional status of children by the classification accuracy level of ordinal logistic regression and misclassification . Classification accuracy of ordinal logistic regression can be improved by bagging ordinal logistic regression method. Bagging works well on classification method which has unstable procedures. One of classification method which has unstable procedures is ordinal logistic regression. Bagging ordinal logistic regression method by 501 times replication capable to improve classification accuracy of ordinal logistic regression model from to , increased .
PERBANDINGAN REGRESI ROBUST PENDUGA MM DENGAN METODE RANDOM SAMPLE CONSENSUS DALAM MENANGANI PENCILAN NI PUTU NIA IRFAGUTAMI; I GUSTI AYU MADE SRINADI; I WAYAN SUMARJAYA
E-Jurnal Matematika Vol 3 No 2 (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.i02.p065

Abstract

The presence of outliers in observation can result in biased in parameter estimation using ordinary least square (OLS). Robust regression MM-estimator is one of the estimations methods that able to obtain a robust estimator against outliers. Random sample consensus (ransac) is another method that can be used to construct a model for observations data and also estimating a robust estimator against outliers. Based on the study, ransac obtained model with less biased estimator than robust regression MM-estimator.
PEMODELAN JUMLAH KASUS PNEUMONIA BALITA DI JAWA TIMUR MENGGUNAKAN REGRESI SPATIAL AUTOREGRESSIVE MOVING AVERAGE MADE NARYMURTI WIDYASTUTI; I GUSTI AYU MADE SRINADI; MADE SUSILAWATI
E-Jurnal Matematika Vol 8 No 3 (2019)
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.2019.v08.i03.p259

Abstract

The purpose of this study is to model and determine the factors that significantly influence the number of toddler pneumonia cases in East Java Province. Modeling the number of toddler pneumonia cases was conducted using spatial autoregressive moving average (SARMA) regression analysis. The results showed that the best model to modeling was SARMA (1.1) with the AIC value is and the coefficient of determination ( is . The significant factors that affect the number of these cases are the number of toddler receiving complete basic immunization and the number of toddler receiving health services in each district/city.
PERHITUNGAN VALUE AT RISK KUNJUNGAN WISATAWAN ASING KE BALI AGUS PUTU SURYAWAN; KOMANG DHARMAWAN; I GUSTI AYU MADE SRINADI
E-Jurnal Matematika Vol 9 No 1 (2020)
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.2020.v09.i01.p281

Abstract

The development of the tourism industry in Bali is very fast compared to other regions in Indonesia. This is due to the fascination of Bali which fascinates tourists, such as culture, customs and natural beauty. The rapid development of tourism in Bali requires tourism risk management. The purpose of this study is to calculate the Value At Risk (VaR) of Chinese, British and American tourists visiting Bali. The study was conducted using the VaR method with the GARCH (1,1) and GJR (1,1) models. Chinese tourist visit data is homocedasticity so it cannot proceed to GARCH (1.1) and GJR (1.1) modeling. VaR value of British and American tourist visits using the GARCH (1.1) and GJR (1.1) models at 95% confidence levels respectively -69.2% and -43.6 with an average VaR value of -56, 4%, and -69.3% and -44.7% with an average VaR of -57%. This means that if the Bali Government targets the number of tourist visits to be 7,100,000 people with a tourism promotion cost of Rp.134.1 per person, then there will be at least 4,004,400 people visiting Bali. So the investment costs incurred by the Provincial Government of Bali for tourism promotion of Rp. 536,990,040.
Co-Authors AA Sudharmawan, AA ADE KUSUMA DEWI Agnes Juliet Boking Agnes Juliet Bokings Agung Dwi Cahya Megamahaputra AGUST WIRAS ARDI KUSUMA ANAK AGUNG ISTRI AGUNG CANDRA ISWARI ANNA FITRIANI AYU SANDRA TIARA DEWI Ayuk Dwi Cahyani Chairun Nisa Cokorda Istri Tirta Rusmala Dewi Deddi Prima Putra DERY MAULANA Desak Putu Eka Nilakusmawati DEWA AYU DWI ASTUTI Dewi, Cokorda Istri Tirta Rusmala DOMINGGAS TEO Dyan Ayu Wijayanti EKA ARISTA ANJASARI Eriska May Wulandari EVARISTUS VERIYOGI YALSCHEN LEMBUNAI G. K. GANDHIADI G. K. GANDHIADI GILANG BIMASAKTI ANDHIKA GUSTI AYU PUTU YULIANDARI GUSTI AYU RATIH ASTARI HIRZI FIRDAUSI I GEDE AGUS JIWADIANA I Gede Purna Adi Putra I GUSTI AYU MADE VALENTINA DEWI I Kadek Yudha Pramana Adi I KOMANG GDE SUKARSA I Komang Gede Sukarsa I Made Agus Gelgel Wirasuta I MADE BUDIANTARA PUTRA I MADE PRABA ESHA SUKSEMAWAN I Nyoman Widana I Putu Eka Nila Kencana I Putu Eka Suarsa I PUTU YUDANTA EKA PUTRA I Wayan Sumarjaya I.K.G. Sukarsa IDA AYU MADE SUPARTINI Isabel Divya Georgiana Walewangko K. Jayanegara KADEK NOVIA DWIJAYANTHI KASTIN DWILEN PONG SUMAE Ketut Jayanegara KHOSYI RUKITO Komang Dharmawan LUH KOMANG MARDIANI Luh Putu Ratna Sundari LUH PUTU SAFITRI PRATIWI LUIS RICARDO PANDIANGAN Luky Adrianto M ARRIE KUNILASARI ELYNA Made Asih MADE AYU DWI OCTAVANNY MADE NARYMURTI WIDYASTUTI Made Novita Dewi Made Susilawati MILATUS SHOLIKHA Mirza Rizaldi Sudrajat MULIA YASMAN NADIYA YUVITA RIZKI NATASYA WIDIA PUTRI Ni Kade Hindu Pertiwi NI KADEK ARISKA DEWI Ni Kadek Dhirayani NI KADEK DWI ARISYA AFRILIANTI NI KADEK ENDAH YANITA UTARI NI LUH GEDE SINTA ARYATI NI LUH NIKASARI Ni Luh Putu Suciptawati NI LUH WIWIN YUNIARTI Ni Made Asih Ni Made Audi Kirei Saraswati Ni Made Putri Ja Yanti NI MADE SEKARMINI NI MADE SRI KUSUMAWARDHANI NI NYOMAN UTAMI DEWI NI PUTU DIAN ASTUTIK Ni Putu Linda Laksmiani Ni Putu Manik Maharani NI PUTU MEILING UTAMI NI PUTU MIRAH SRI WAHYUNI Ni Putu Monikha Alvionitha NI PUTU NIA IRFAGUTAMI NI PUTU PREMA DEWANTI NI PUTU RINA ANGGRENI NI WAYAN ARI SUNDARI NI WAYAN ARIS APRILIA A.P NI WAYAN ASRI PRADNYANI Ni Wayan Dewi Anastasya Pratiwi Ni Wayan Merry Nirmala Yani NI WAYAN YULIANI NI WAYAN YUNI CAHYANI NOVIAN ENDI GUNAWAN NUR FAIZA NURMA ALIYUWANINGSIH NYOMAN GDE PRAJNAWIWEKA RATMASA TARAM NYOMAN KRISHNA PRATIWI DANGIN Nyoman Wendri PALUPI PURNAMA SARI PANDE PUTU BUDI KUSUMA Putu Edi Dimas Saputra PUTU SUSAN PRADAWATI Putu Wulan Cahayaningrat Ratna Sari Widiastuti SORAYA SARAH AFIFAH TJOK GDE SAHITYAHUTTI RANANGGA TRI ALIT TRESNA PUTRA Ulfatun Farika Novitasari ULYATIL AENI Wayan Evi Handayani Wijayakusuma, I Gusti Ngurah Lanang WILDAN FATTURAHMAN MUJTABA Yani Arthayanti ZANUAR SEPTYADI