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INDONESIA
E-Jurnal Matematika
Published by Universitas Udayana
ISSN : 23031751     EISSN : -     DOI : -
Core Subject : Education,
Mathematics
E-Jurnal Matematika merupakan salah satu jurnal elektronik yang ada di Universitas Udayana, sebagai media komunikasi antar peminat di bidang ilmu matematika dan terapannya, seperti statistika, matematika finansial, pengajaran matematika dan terapan matematika dibidang ilmu lainnya. Jurnal ini lahir sebagai salah satu bentuk nyata peran serta jurusan Matematika FMIPA UNUD guna mendukung percepatan tercapainya target mutu UNUD, selain itu jurnal ini terbit didorong oleh surat edaran Dirjen DIKTI tentang syarat publikasi karya ilmiah bagi program Sarjana di Jurnal Ilmiah. E-jurnal Matematika juga menerima hasil-hasil penelitian yang tidak secara langsung berkaitan dengan tugas akhir mahasiswa meliputi penelitian atau artikel yang merupakan kajian keilmuan.
Arjuna Subject : -
Articles 408 Documents
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PERLUASAN REGRESI COX DENGAN PENAMBAHAN PEUBAH TERIKAT-WAKTU LUH PUTU ARI DEWIYANTI; NI LUH PUTU SUCIPTAWATI; I WAYAN SUMARJAYA
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.p069

Abstract

The aim of this study is to model job hunting period in Bali in 2012 using Extended Cox model. Previous study concluded that household status and age variables were not significantly influenced the job hunting period. However, previous study on factors that influence job waiting suggests that both variables should play important role in determining the waiting time for job hunters. Thus incorporating time-dependent covariates into model is necessary. After incorporating time-dependent covariates we found that age with time-dependent covariate is significant.  
ANALISIS SENTIMEN MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER DENGAN SELEKSI FITUR CHI SQUARE JUEN LING; I PUTU EKA N. KENCANA; TJOKORDA BAGUS OKA
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.p070

Abstract

Sentiment analysis is the computational study of opinions, sentiments, and emotions expressed in texts. The basic task of sentiment analysis is to classify the polarity of the existing texts in documents, sentences, or opinions. Polarity has meaning if there is text in the document, sentence, or the opinion has a positive or negative aspect. In this study, classification of the polarity in sentiment analysis using machine learning techniques, that is Naïve Bayes classifier. Criteria for text classification decisions, learned automatically from learning the data. The need for manual classification is still required because training the data derived from manually labeling, the label (feature) refers to the process of adding a description of each data according to its category. In the process of labeling, feature selection is used and performed by chi-square feature selection, to reduce the disturbance (noise) in the classification. The results showed that the frequency of occurrences of the expected features in the true category and in the false category have an important role in the chi-square feature selection. Then classification breaking news by Naïve Bayes classifier obtained an accuracy of 83% and a harmonic average of 90.713%.
PENERAPAN METODE BOOTSTRAP RESIDUAL DALAM MENGATASI BIAS PADA PENDUGA PARAMETER ANALISIS REGRESI NI MADE METTA ASTARI; NI LUH PUTU SUCIPTAWATI; I KOMANG GDE SUKARSA
E-Jurnal Matematika Vol 3 No 4 (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.i04.p075

Abstract

Statistical analysis which aims to analyze a linear relationship between the independent variable and the dependent variable is known as regression analysis. To estimate parameters in a regression analysis method commonly used is the Ordinary Least Square (OLS). But the assumption is often violated in the OLS, the assumption of normality due to one outlier. As a result of the presence of outliers is parameter estimators produced by the OLS will be biased. Bootstrap Residual is a bootstrap method that is applied to the residual resampling process. The results showed that the residual bootstrap method is only able to overcome the bias on the number of outliers 5% with 99% confidence intervals. The resulting parameters estimators approach the residual bootstrap values ??OLS initial allegations were also able to show that the bootstrap is an accurate prediction tool.
PERAMALAN KUNJUNGAN WISATAWAN MENGGUNAKAN MODEL ARMAX DENGAN NILAI KURS DAN EKSPOR-IMPOR SEBAGAI FAKTOR EKSOGEN PUTU IKA OKTIYARI LAKSMI; KOMANG DHARMAWAN; LUH PUTU IDA HARINI
E-Jurnal Matematika Vol 3 No 4 (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.i04.p076

Abstract

Forecasting is science to estimate occurrence of the future. This matter can be conducted by entangling intake of past data and place to the next period with a mathematical form. This research aims to estimate the number of foreign tourists visiting Bali models using autoregressive moving average exogenous (ARMAX). The data used in this study is the number of tourists in Australia and the number of tourists in the RRC as a variable Y, and foreign currency exchange rate AUD, Chinese Yuan, and Export Import as the X factor from the period July 2009 to July 2014. In the analysis can be obtained in the best ARMAX models of the number of tourists in Australia is ARMAX(1,2,2) and the best model of the number of tourists in the RRC does not exist because the data for the ARMAX model parameters tourists no significant RRC.
KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS HANY DEVITA; I KOMANG GDE SUKARSA; I PUTU EKA N. KENCANA
E-Jurnal Matematika Vol 3 No 4 (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.i04.p077

Abstract

Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached. Multicollinearity is a linear correlation between independent variabels in model. Jackknife Ridge Regression(JRR) as an extension of Generalized Ridge Regression (GRR) for solving multicollinearity.  Generalized Ridge Regression is used to overcome the bias of estimators caused of presents multicollinearity by adding different bias parameter for each independent variabel in least square equation after transforming the data into an orthoghonal form. Beside that, JRR can  reduce the bias of the ridge estimator. The result showed that JRR model out performs GRR model.
PENENTUAN HARGA KONTRAK OPSI TIPE EROPA MENGGUNAKAN METODE QUASI MONTE CARLO DENGAN BARISAN KUASI-ACAK HALTON I GUSTI PUTU NGURAH MAHAYOGA; KOMANG DHARMAWAN; LUH PUTU IDA HARINI
E-Jurnal Matematika Vol 3 No 4 (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.i04.p078

Abstract

Penelitian ini bertujuan untuk mengetahui keakuratan hasil simulasi harga saham untuk menentukan harga opsi call dari metode Monte Carlo dan metode Quasi Monte Carlo dengan menggunakan program Matlab. Harga standar yang digunakan untuk membandingkan kedua metode tersebut akan dihitung dengan metode Black-Scholes. Nilai error yang dihitung menggunakan metode MAPE (Mean Absolute Percentage Error) digunakan sebagai acuan dalam perbandingan. Selain keakuratan simulasi harga saham, kecepatan eksekusi program Matlab kedua metode juga dihitung untuk efisiensi waktu. Tahap pertama, menentukan variabel-variabel yang digunakan untuk menghitung lintasan harga saham pada waktu ke-t pada saat mensimulasikan harga saham. Tahap kedua, menghitung harga standar menggunakan metode Black-Scholes. Tahap ketiga, mensimulasikan harga saham dengan metode Monte Carlo dan Quasi Monte Carlo. Setelah mensimulasikan harga saham, catat waktu eksekusi program Matlab, lalu dihitung nilai pay-off dari opsi call, kemudian menaksir harga opsi call dengan merata-ratakan seluruh nilai pay-off dari masing-masing iterasi. Tahap terakhir, menghitung error dari kedua metode simulasi dengan metode MAPE lalu membandingkannya. Hasil penelitian ini menunjukkan bahwa metode Quasi Monte Carlo lebih akurat karena menghasilkan nilai error yang lebih kecil, artinya hasil simulasinya mendekati harga standar. Sedangkan untuk waktu eksekusi program, metode Monte Carlo lebih baik di semua iterasi.
APLIKASI ALGORITMA GENETIKA UNTUK MERAMALKAN KONSUMSI PREMIUM KOTA DENPASAR VICTOR MALLANG; KETUT JAYANEGARA; NI MADE ASIH; I PUTU EKA N. KENCANA
E-Jurnal Matematika Vol 3 No 4 (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.i04.p079

Abstract

This research aimed to forecast the gasoline demand at Denpasar using genetic algorithm method. This  algorithm was selected because of easy to implement and its ability to find acceptable solution quickly.  This algorithm works by searching the best individu according to fitness function defined. The series data used in the research were 60 observations of monthly gasoline demand at Denpasar for period January 2009 through December 2013.  By observing the Partial Autocorrelation Function (PACF) plot, we found the last lag before the series become stationer was sixth lag.  Based on this finding, we decided the best individu was represented by six genes. This individu, in addition, was used to make in-sample forecasting.  The forecasted data had mean absolute error (MAE) as much as 553,27 kiloliters.  For one semester out-of sample forecast, we found gasoline consumption fluctuated with lowest and highest consumption were for February 2014 and June 2014, respectively.
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.
PERBANDINGAN REGRESI BINOMIAL NEGATIF DAN REGRESI GENERALISASI POISSON DALAM MENGATASI OVERDISPERSI (Studi Kasus: Jumlah Tenaga Kerja Usaha Pencetak Genteng di Br. Dukuh, Desa Pejaten) NI MADE RARA KESWARI; I WAYAN SUMARJAYA; NI LUH PUTU SUCIPTAWATI
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.p072

Abstract

Poisson regression is a nonlinear regression that is often used to model count response variable and categorical, interval, or count regressor. This regression assumes equidispersion, i.e., the variance equals the mean. However, in practice, this assumption is often violated. One of this violation is overdispersion in which the variance is greater than the mean. There are several  methods to overcome overdispersion. Two of these methods are negative binomial regression and generalized Poisson regression. In this research, binomial negative regression and generalized Poisson regression statistically equally good in handling overdispersion.
KOMPARASI KINERJA FUZZY TIME SERIES DENGAN MODEL RANTAI MARKOV DALAM MERAMALKAN PRODUK DOMESTIK REGIONAL BRUTO BALI I MADE ARYA ANTARA; I PUTU EKA N. KENCANA; I KOMANG GDE SUKARSA
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.p073

Abstract

This paper aimed to elaborates and compares the performance of Fuzzy Time Series (FTS) model with Markov Chain (MC) model in forecasting the Gross Regional Domestic Product (GDRP) of Bali Province.  Both methods were considered as forecasting methods in soft modeling domain.  The data used was quarterly data of Bali’s GDRP for year 1992 through 2013 from Indonesian Bureau of Statistic at Denpasar Office.  Inspite of using the original data, rate of change from two consecutive quarters was used to model. From the in-sample forecasting conducted, we got the Average Forecas­ting Error Rate (AFER) for FTS dan MC models as much as 0,78 percent and 2,74 percent, respec­tively.  Based-on these findings, FTS outperformed MC in in-sample forecasting for GDRP of Bali’s data.

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