Garuda - Garba Rujukan Digital

p-Index From 2019 - 2024

0.961

P-Index

This Author published in this journals

All Journal Media Statistika MATEMATIKA SAINS DAN MATEMATIKA Jurnal Gaussian Efficient: Indonesian Journal of Development Economics

Diah Safitri

Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

Author-ID : 368037

Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

Published : 31 Documents Claim Missing Document

Claim Missing Document

Articles

Title

1 2

PENGGUNAAN REGRESI LOGISTIK BINER DAN ITERATIVE DICHOTOMISER 3 (ID3) DALAM PEMBUATAN KLASIFIKASI STATUS KERJA (Studi Kasus Penduduk Kota Surakarta Tahun 2015) Winastiti, Lugas Putranti; Rusgiyono, Agus; Safitri, Diah
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Discussing about the macro economy usually discuss about unemployment. Unemployment basically can not be fully eliminated. Unemployment usually symbolized with an employment status of person. In this research, two methods were used in making the classification of employment status in the population of the city of Surakarta in February 2015, the methods are binary logistic regression and Iterative Dichotomiser 3 (ID3) Algorithm. Predictor variables used in determining employment status were age, gender, status in the household, marital status, education and work training. Comparison of the training data and testing data is 60:40. Based on calculations obtained binary logistic regression variables that significantly affect the employment status are age, gender and marital status and the accuracy using testing data is 75%, while the calculations of a decision tree using iterative dichotomiser 3 algorithm the accuracy using testing data is 75%. Keywords: Classification, Iterative Dichotomiser 3 Algorithm, Binary Logistic Regression

ANALISIS FAKTOR KONFIRMATORI STRATEGI POSITIONING PASAR MODERN INDOMARET (Studi Kasus Wilayah Tembalang Kota Semarang) Sholihin, Imam Nur; Mustafid, Mustafid; Safitri, Diah
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Indomaret marketing strategy became one of modern market that has significant development in the last five years. Market positioning is one form of marketing strategy that functions to adjust as desired market position of market actors. Positioning has some major elements of the constituent factors of the product, price, place and promotion. Measurement of the magnitude of the influence of each factor were developed with confirmatory factor analysis. This study aims to examine the factors that influence the positioning strategy and the characteristics of the modern consumer market. The method used in the study using confirmatory factor analysis as used multivariate analysis to confirm the hypothesized model. The study was based on a case study on consumer Indomaret modern market in Tembalang, Semarang City. Results of the analysis showed that all the variables are valid and reliable indicators to measure the factors. Can be known as well as some consumer characteristics of a modern market. Among the interested consumer spending in the modern market with regard to the quality of the stuff is good, the existence of a clear price list, inventory as well as an interesting ad.

PERBANDINGAN REGRESI KOMPONEN UTAMA DENGAN REGRESI RIDGE PADA ANALISIS FAKTOR-FAKTOR PENDAPATAN ASLI DAERAH (PAD) PROVINSI JAWA TENGAH Tazliqoh, Agustifa Zea; Rahmawati, Rita; Safitri, Diah
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Assuming violation multicollinearity in classical regression analysis can cause estimator resulting from classical model regression inefficient. Principal components regression and ridge regression are the methods that can be used to overcome the problem of multicollinearity. This research aimed to compare between the principal components regression with ridge regression to tackle the problem of multicollinearity in the analysis of the factors that affect revenue (PAD) of the Central Java province. The data used in this research are data revenue (PAD), and factors that affect the region, such as local tax, retribution, Gross Regional Domestic Products (GRDP) at current prices, Gross Regional Domestic Products (GRDP) at constant prices, population, regional spending. Based on the coefficient of determination value and test on individual regression coefficients, the value of variance inflation factor and correlations sufficiently high among some independent variables so we can conclude the existence of a violation of multicollinearity on analysis factors PAD. Based on standard error resulting from principal components regression and ridge regression show that principal components regression results in a standard smaller error. This shows that principal component regression is better than ridge regression in solving the problem multicollinearity on analysis of factors that affects pad province of central java. Keywords: Multicolinearity, revenue (PAD), Principal Component Regression, Ridge Regression, standard error

PENENTUAN KOEFISIEN KORELASI KANONIK DAN INTERPRETASI FUNGSI KANONIK MULTIVARIAT Asbah, Muhamad Faliqul; Sudarno, Sudarno; Safitri, Diah
Jurnal Gaussian Vol 2, No 2 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Canonical correlation analysis is a useful technique to identify and quantify the linier relationships, involving multiple independent and multiple dependent variable. It focuses on the correlation between a linier combination of the variables in one set independent and a linier combination of the variables in another set dependent. The pairs of linier combinations are called canonical function, and their correlation are called canonical correlation coefficient. The statistical assumptions should be fulfilled are: linearity, multivariate normality, homoscedasticity, and nonmulticollinearity. The use of variable consists of three dependent variable: y1 =Maximum daily relative humidity, y2 = Minimum daily relative humidity, and y3 = Integrated area under daily humidity curve and three independent variable: x1 = Maximum daily air temperature, x2 = Minimum daily air temperature, and x3 = Integrated area under daily air temperature curve. For The result of canonical correlation analysis indicate that there are two significant canonical correlation between the daily air temperature level with the daily humidity level. The reduncancy index showed that the daily humidity level can explained a total of 69 % of the variance in the daily air temperature level, otherwise the daily air temperature level can explained a total 60 % of the variance in the daily humidity level. Interpretations involves examining the canonical function to determine the relative contibution of each of the original variables in the canonical relationships: canonical weights, canonical loadings, and canonical cross loadings showed that the sequence variables which contribute on the independent variate are x1,x3, and x2. Then, the sequence variables which contribute on the dependent variate are y1, y2, and y3.

PENGUKURAN VALUE AT RISK MENGGUNAKAN PROSEDUR VOLATILITY UPDATING HULL AND WHITE BERDASARKAN EXPONENTIALLY WEIGHTED MOVING AVERAGE (EWMA) (Studi Kasus pada Portofolio Dua Saham) Putri, Nurissalma Alivia; Hoyyi, Abdul; Safitri, Diah
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Investment is an effort to get profits for individual or institution. But the investment policy is always faced with market risk as the effect of financial instruments movement such as stock price movements. Market risk measurement tool commonly used is Value at Risk (VaR), which measures the amount of loss at a certain confidence level. VaR measurement by Hull and White volatility updating procedure is a modification of the historical simulation involving information of volatility change calculated by Exponentially Weighted Moving Average (EWMA). This procedure is fit to financial data such as stock returns that are generally not normally distributed and are heteroskedastic. VaR calculation applied to the portfolio between Kalbe Farma Tbk (KLBF) stock and Lippo Karawaci Tbk (LPKR) stock from 3 January 2011 to 19 April 2013 were selected based on the largest trading volume at the end of the observation for LQ45 stocks listed in the Indonesia Stock Exchange (IDX) . The data used is the return calculated from the closing price of stocks. The validity of VaR was tested through a back test by Kupiec test, and concluded that the 95% VaR and 99% VaR are valid.

PEMILIHAN INPUT MODEL REGRESSION ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (RANFIS) UNTUK KAJIAN DATA IHSG Sari, Sasmita Kartika; Tarno, Tarno; Safitri, Diah
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The Jakarta Composite Index (JCI) is one of indexes issued by the Indonesia Stock Exchange (IDX) with its calculation component using all the registered emiten. Several factors affecting the JCI are Dow Jones Index, inflation, and USD/IDR exchange rate. The study used Regression Adaptive Neuro Fuzzy Inference System (RANFIS) to analyze the affect of predictor variables on the JCI. The role of regression in RANFIS is a preprocessing in the determination of input in ANFIS. The optimum ANFIS model in RANFIS is strongly influenced by three things, they are input determination, membership functions, and rule. The technique of defining rules followed the rule of genfis1 and genfis3. The model accuracy was measured using the smallest RMSE and MAPE. Based on the empirical studies which implemented Dow Jones Index, inflation, and USD/IDR exchange rate as the predictors and JCI as the response, it was obtained that optimum RANFIS model with gauss membership function, the number of cluster 2 with 2 rules generated by genfis3 produced RMSE in-sample 233.0 and out-sample 301.9, as well as MAPE in-sample 6.5% and out-sample 4.8%. While in regression analysis, it obtained RMSE in-sample 351.27 and out-sample 590.99, as well as MAPE in-sample 9.6% and out-sample 10.2% with violation of assumption. This shows that the result of RANFIS method is better than regression analysis. Keywords: JCI, regression analysis, neuro fuzzy, RANFIS, genfis

ANALISIS JALUR (PATH ANALYSIS) UNTUK MENGETAHUI HUBUNGAN ANTARA USIA IBU, KADAR HEMOGLOBIN, DAN MASA GESTASI TERHADAP BERAT BAYI LAHIR (Studi Kasus di Rumah Sakit Aisyiyah Kudus) Handaningrum, Evi Yulia; Safitri, Diah; Ispriyanti, Dwi
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Birth weight is the weight of a baby who weighed in 1 (one) hour after birth. Birth weight is important to note because many cases are caused by birth weight that is too high or too low as in the case of LBW (Low Birth Weight). LBW is infants with a birth weight less than 2500 grams. The factors that considered in addressing LBW are factors maternal age, maternal hemoglobin levels, and gestational age. One of the statistical analysis that can be used to analyze the causal relationship of several variables is path analysis.Path analysis is a modified form of regression analysis in which the independent variables studied not only directly affect the dependent variable, but it can also affect these variables indirectly. The independent variables have a direct effect and indirect effect on the dependent variable. Based on analyzing, it is concluded that the variable which has a direct effect to birth weight infant was gestational age, whereas for maternal age and maternal hemoglobin levels effect to birth weight infant, it can be seen by its inderect effect.

PEMODELAN REGRESI SPLINE MENGGUNAKAN METODE PENALIZED SPLINE PADA DATA LONGITUDINAL (Studi Kasus: Harga Penutupan Saham LQ45 Sektor Keuangan dengan Kurs USD terhadap Rupiah Periode Januari 2011-Januari 2016) Zia, Nabila Ghaida; Suparti, Suparti; Safitri, Diah
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Nonparametric regression is one type of regression analysis used when parametric regression assumptions are not fulfilled. Nonparametric regression is used when the curve does not form a specific pattern of connections. One of the approach by using nonparametric regression is spline regression with penalized spline method. Spline regression using penalized spline method was applied to three closing stock prices on the financial sector such as Bank BRI, BCA and Mandiri with the data of USD currency rate in rupiah. Closing price of stock data and the USD currency rate in rupiah were taken from January 2011 up to January 2016 for in sample data and from February 2016 up to December 2016 for out sample data. The data taken is called longitudinal data which is observing some subjects on specific period. Best spline regression model with penalized spline method is derived from the minimum value of GCV, the number of optimal knots and the optimal orde. Best spline regression model with penalized spline method for longitudinal data was obtained on the orde of 1, the 59 knots, the smoothing parameter with λ value of 1 and the GCV value of 889,797. The R2 value of in sample data was 99,292%, best model performance for in sample data. MAPE value of out sample data is 1,057%, the best accurate performance model.Keyword: stock price, USD currency rate, longitudinal data, spline regression, penalized spline

ANALISIS KONJOIN FULL PROFILE DALAM PEMILIHAN BEDAK UNTUK MAHASISWI DEPARTEMEN STATISTIKA UNIVERSITAS DIPONEGORO Julianisa, Rose Debora; Safitri, Diah; Yasin, Hasbi
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Powder is the one of cosmetic product that serves to cover the shortfall on the face. Powder consumption continues to increase from year to year to follow trend of cosmetic and lifestyle that happened to people. It makes producer to be more creative and innovative to produce or developing their product to keep consumers interested. To help producer to know and understand the consumer preference on combinations of attributes in the powder, it can be used conjoint analysis. Beside that, conjoint analysis is used to get the concept of products that comply with the consumers want and can be developed as a combination of new products. In this thesis conjoint analysis is used by using presentation method of full-profile. There are four attributes used in this analysis, they are powder types, form of packaging, aroma, and glass facility. From the results of the analysis that obtained by the respondents, the most importance attribute in selecting a face powder is the package attribute (34,338 %), the second is a kind of powder (33,667 %), the third is glass facility in the powder (16,397 %), and the last is the scent of powder (15,598 %). The combination of desired respondents in choosing or use a powder is a powder that have the type of compact powder, circular packaging forms, has no aroma, and there is no glass. Keywords : powder, consumer’s preference, conjoint analysis, full-profile

MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) UNTUK KLASIFIKASI STATUS KERJA DI KABUPATEN DEMAK Kishartini, Kishatini; Safitri, Diah; Ispriyanti, Dwi
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Unemployment is one of the issues relating to economic activities, public relations and also the problems of humanity. Unemployment also occur in Demak and factors suspected as the cause of unemployment in Demak: gender, area of residence, age, status in the household, marriage status and education. Demak BPS records the number of people looking for work (unemployed) as many as 226.228 people, or 29,55% of the working age population. MARS (Multivariate Adaptive Regression Splines) is one of the methods used for classification. MARS is used for high-dimensional data, which is data that has a number of predictor variables for 3 ≤ v ≤ 20 data used in this study is a secondary data from national labor force survey (SAKERNAS) in 2012. To get the best MARS models performed with by combining Maximum Base Function (BF), Minimal Observation (MO), and Maximum Interaction (MI) by trial and error. MARS model is used to classify employment status in Demak are MARS models (BF =24, MI=3, MO=1). Keywords: Unemployment, Classification, MARS

Co-Authors Aan Rosiatun Abdul Hoyyi Agus Rusgiyono Agustifa Zea Tazliqoh Al Qarani, Muhammad Aqajahs Anik Nurul Aini Arsyil Hendra Saputra Bimbi Ardhana Rizky Desy Tresnowati Hardi Di Asih I Marudani Di Asih I Maruddani Dwi Ispriyanti Esti Pratiwi Evi Yulia Handaningrum Hasbi Yasin Imam Nur Sholihin Juria Ayu Handini Kishatini Kishartini Maruddani, Di Asih Maryam Jamilah An Hasibuan Muhamad Faliqul Asbah Mustafid Mustafid Nariswari Diwangkari, Nariswari Nunik Nurhasanah Nurissalma Alivia Putri Octafinnanda Ummu Fairuzdhiya Paramita Indrasari Puput Ramadhani Rita Rahmawati Rita Rahmawati Rose Debora Julianisa, Rose Debora Rukun Santoso Sari, Sasmita Kartika Setya Adi Rahmawan Sucihatiningsih Dian Wisika Prajanti Sudarno Sudarno Sudarno Sudarno Suparti Suparti Tarno Tarno Tatik Widiharih Tatik Widiharih Tyas Estiningrum Vetranella .T.R.A. Sinaga Winastiti, Lugas Putranti Yuciana Wilandari Zia, Nabila Ghaida

Title Search

Found 20 Documents Search Journal : Jurnal Gaussian

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 20 Documents
Search
Journal : Jurnal Gaussian