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Jurnal Gaussian

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Published by Universitas Diponegoro

ISSN : - EISSN : 23392541 DOI : -

Core Subject : Education,

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Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM UNDIP.

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Articles 733 Documents

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OPTIMASI WAKTU EFEKTIF APLIKASI HERBISIDA PADA TANAMAN KELAPA SAWIT (ELAEIS GUINEENSIS JACQ.) DENGAN FUNGSI ESTIMASI DENSITAS KERNEL (Studi Kasus di Perkebunan Sawit PT SMART Tbk, Libo Estate, Riau) Putri Aulia Wahyuningsih; Tatik Widiharih; Hasbi Yasin
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Palm oil agribusiness is one of potential source to accelerate economic growth in Indonesia. Palm oil is the raw material to produce CPO (Crude Palm Oil) which is source of vegetable oil that is needed by all people. This research used a combination of 16 treatments of type and dose of herbicide on oil palm trees. Purposes of this research are to determine the optimal timing of herbicide applications and determine the treatment that maximizes efficacy of weed. Optimal timing of herbicide applications to the palm trees is determined through the largest mean of bootstrap resample and plot of kernel epanechnikov density estimation. Optimal treatment is determined through the largest mean of bootstrap resample, the smallest variance resample, the smallest range of bootstrap percentile confidency interval, and coverage probability that close to 1-α. Result obtained is the optimal timing of herbicide applications to oil palm trees is 8 weeks after applications. And optimal treatment is Tricalon 318 EC at a dose of 1500 cc.

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

KLASIFIKASI WILAYAH DESA-PERDESAAN DAN DESA-PERKOTAAN WILAYAH KABUPATEN SEMARANG DENGAN SUPPORT VECTOR MACHINE (SVM) Mekar Sekar Sari; Diah Safitri; Sugito Sugito
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

This research will be carry out classification based on the status of the rural and urban regions that reflect the differences in characteristics/ conditions between regions in Indonesia with Support Vector Machine (SVM) method. Classification on this issue is working by build separation functions involving the kernel function to map the input data into a higher dimensional space. Sequential Minimal Optimization (SMO) algorithms is used in the training process of data classification of rural and urban regions to get the optimal separation function (hyperplane). To determine the kernel function and parameters according to the data, grid search method combined with the leave-one-out cross-validation method is used. In the classification using SVM, accuracy is obtained, which the best value is 90% using Radial Basis Function (RBF) kernel functions with parameters C=100 dan γ=2-5. Keywords : classification, support vector machine, sequential minimal optimization, grid search, leave-one-out, cross validation, rural, urban

PENERAPAN RANCANGAN BLOK RANDOM TIDAK LENGKAP SEIMBANG PADA KOMBINASI PUPUK NANOSILIKA DAN PUPUK NPK TERHADAP PERTUMBUHAN TANAMAN JAGUNG Asismarta, Asismarta; Suparti, Suparti; Sudarno, Sudarno
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Balanced Incomplete Block Design (BIBD) when all treatment comparisons are equally important, the treatment combinations used in each block should be selected in a balanced manner so that any pair of treatments occur together the same number of times as any other pair. The data used is the result a simulation of the generation of data using program packages MINITAB 16 that normal distributing with a and varying Based on the study of cases the combined effect fertilizer nanosil and fertilizer NPK on the growth of corn plant, tested on 6 treatment and 10 block with every treatment repeated as many as 5 times and each block unfilled 3 treatment. Assuming model that is residual the normal distribution, independence and variant homogeneous. When third this assumption be accepted then followed the effect treatment (adjusted) against an observed, when having effect and undergone a further Tukey to know treat which that differ significantly. Of treatment to be adjusted obtained with combination 25% fertilizer nanosil + 75% fertilizer NPK who gives the average the biggest contributor to the growth of plants corn.Keywords : BIBD, Tuckey test, normality, independence, equal variance

ANALISIS EKUITAS MEREK SEPEDA MOTOR HONDA TERHADAP KEPUTUSAN PEMBELIAN DAN PERILAKU PASCA BELI MENGGUNAKAN STRUCTURAL EQUATION MODELLING (SEM) Herwindhito Dwi Putranto; Abdul Hoyyi; Moch. Abdul Mukid
Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Research on the implementation of Structural Equation Modelingto analyze the Honda brand equityon purchase decision and post-purchase behavior is based on the strength of the brand equityas a market leader Honda motorcycles in Indonesia for many years. The problem saddressed in this study is how the relationship between brand equity Honda motorcycle on purchase decision and post purchase behavior of consumers. In this study developed six variables consisting of 4 exogenous variables, namely brand awareness, brand response, the impression of quality and product loyalty, to measure brand equityas well as two endogenous variables, ie, purchase decision and post-purchase behavior. The study involved 200 students of the University of Diponegoro as respondents using purposive sampling technique.Structura lequation modeling research is Behavioral Post Buy=Purchasing Decisions + error. From the Goodness of Fittest results, structural equation modelin this study can be used with a value of 70,237 and the Chi-Square probability AGF I1000 and 0951. Brand awareness of 10.1% influence on purchasing decisions and 10% of the post-purchase behavior and is avariable that gives the effect of CR 1477-value ≤2.58. Responses highest brandin fluenceis equal to 32.7% against 32.4% purchase decision and post-purchase behavior. Thusit was concluded that brand awareness does not affect the purchase decision, while there sponse the brand, the impression of quality and product loyalty influence purchasing decisions. Purchasing decisions also provide a positive influence on post-purchase decisions.

ANALISIS STRUCTURAL EQUATION MODELLING PENDEKATAN PARTIAL LEAST SQUARE DAN PENGELOMPOKAN DENGAN FINITE MIXTURE PLS (FIMIX-PLS) (Studi Kasus: Kemiskinan Rumah Tangga di Indonesia 2017) Anggita, Esta Dewi; Hoyyi, Abdul; Rusgiyono, Agus
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Poverty is a complex and multidimensional problem that links several dimensions. Statistical method that can explain the relationship between one latent variable with others is Structural Equation Modelling (SEM). The purpose of this study is to create a structural model of the relationship between education, health, economy and poverty in Indonesia in 2017 by using Structural Equation Modeling with Partial Least Square approach (SEM-PLS) based on predetermined indicators with the results of 11 valid indicators. Based on the model obtained, health has a significant positive effect on education, health and education have a significant positive effect on the economy and the economy has a significant negative effect on poverty. Segmentation based on the relationship of latent variables in structural models can be overcome by Finite Mixture Partial Least Square (FIMIX-PLS) so that it can identify poverty areas in each province in Indonesia with more homogeneous characteristics. The best segmentation result is number of segments (K) = 2 obtained based on the criteria of AIC, BIC, CAIC and Normed Entropy (EN) with an EN value of 0.964 which means the quality of segment separation is very good. Papua and West Papua provinces form one segment in segment 2, while 32 other provinces form one segment in segment 1.Keywords: Poverty, Structural Equation Modelling, Partial Least Square, Finite Mixture, Segmentation.

PEMODELAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION (GWLR) DENGAN FUNGSI PEMBOBOT FIXED GAUSSIAN KERNEL DAN ADAPTIVE GAUSSIAN KERNEL (Studi Kasus : Laju Pertumbuhan Penduduk Provinsi Jawa Tengah) Desriwendi Desriwendi; Abdul Hoyyi; Triastuti Wuryandari
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The Population Growth Rate (PGR) that are not controlled will have a negative impact on the various social-economic problems such as increased poverty, crime, and so forth. Factors contributing to the population growth rate of uncontrolled allegedly various between Regency/City. Geographically Weighted Logistic Regression (GWLR) is a local form of the logistic regression where geographical factors considered. This study will analyze the factors that affect the population growth rate of Central Java Province using logistic regression and GWLR with a weighting function of Fixed Gaussian Kernel and Adaptive Gaussian Kernel. The results showed that GWLR model with a weighting function of Adaptive Gaussian Kernel better than logistic regression model and GWLR model with a weighting function of Fixed Gaussian Kernel because it has the smallest Akaike Information Criterion (AIC) value with the classification accuracy is 82.8 %.Keywords : PGR, Logistic Regression, Fixed Gaussian Kernel, Adaptive Gaussian Kernel, GWLR, AIC.

ANALISIS PENGARUH JUMLAH UANG BEREDAR DAN NILAI TUKAR RUPIAH TERHADAP INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN PEMODELAN REGRESI SEMIPARAMETRIK KERNEL Nanda, Deden Aditya; Suparti, Suparti; Hoyyi, Abdul
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Stocks are one of the many forms of investment chosen by the investor. Investors can use Composite Stock Price Index (CSPI) as one of the indicators that show the movement of stock prices. CSPI fluctuates every day, where one of the causes are macroeconomic factors. Therefore needs to be done a proper analysis to model the CSPI and the factors that influence it. This study is using 1 parametric component variable (money supply) and 1 nonparametric component variable (exchange rate the rupiah against the dollar). So that proper modeling is semiparametric regression. Nonparametric component will be using kernel regression method by selecting the optimal bandwidth using a generalized cross validation method (GCV). This study uses monthly data. Data in sample is used as much as 68 data that is taken from Januari 2010 to August 2015, meanwhile out sample that is used as much as 6 data from September 2015 to February 2016. Based on the results of the analysis that has been done, the best kernel semiparametric regression model is using gaussian kernel function with bandwidth is around 47.94 and GCV=34675.27047. Determination coefficient value is 0.9781. Evaluation result of the model for value of Mean Absolute Percentage Error (MAPE) data out sample is around 4,036%, which indicates that the model is very accurate.Keywords: Composite Stock Price Index (CSPI), Semiparametric regression, Kernel, GCV

METODE PERAMALAN DENGAN MENGGUNAKAN MODEL VOLATILITAS ASYMMETRIC POWER ARCH (APARCH) Cindy Wahyu Elvitra; Budi Warsito; Abdul Hoyyi
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Exchange rate can be defined as a ratio the value of currency. The exchange rate shows a currency price, if it exchanged with another currency. Exchange rates of a currency fluctuate all the time. Rise and fall exchange rates of a currency in the money market shows the magnitude of volatility occurred in a country currency to other's. To estimate the volatility behavior of the data gave rise to volatility clustering or heteroscedasticity problems, can’t be modeled using ARMA model and asymmetric effects that can‘t be modeled by ARCH or GARCH, can be modeled by Asymmetric Power ARCH (APARCH). In determining the estimated parameter values of APARCH model, used the maximum likelihood method, followed by using the iteration method is Berndt, Hall, Hall and Hausman (BHHH). The APARCH model used to the data return of exchange rate against dollar is APARCH(2,1) or in the form as follows : = 0,00000268 + 0,830902 + 0,130516 + 0,074784 + 0,151157

GUI MATLAB UNTUK METODE FUZZY SAW DAN FUZZY TOPSIS DALAM PEMILIHAN PENERIMA BEASISWA PPA DENGAN PEMBOBOTAN ENTROPI (Studi Kasus : Pemilihan Penerima Beasiswa PPA tahun 2017 Mahasiswa FSM UNDIP, Semarang) Rahmaniar, Ratna; Widiharih, Tatik; Ispriyanti, Dwi
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

For students, scholarships are important to ease the burden on parents, namely tuition fees.The large number of scholarship applicants is a challenge for FSM to be able to provide an appropriate, effective and efficient decision to manage data on scholarship recipients who are truly entitled to receive scholarships. Prospective scholarship recipients are selected based on the criteria determined by FSM.The criteria determined by the FSM are GPA (Grade Point Average), parent income, number of certificates, number of dependents of parents, semester, and electricity. The method applied to select 170 PPA scholarship recipients (Academic Achievement Improvement) is FSAW (Fuzzy Simple Additive Weighting) and FTOPSIS (Fuzzy Technique for Order Preference by Similarity to Ideal Solution) with entropy weighting. This entropy weighting does a combination of the initial weight that has been determined by FSM and the calculation weight. This research was conducted with the help of MATLAB (Matrix Laboratory) GUI (Graphical User Interface) as a computing tool. With the MATLAB GUI system built, it can simplify and speed up the selection process. FSAW and FTOPSIS calculation results are 96% the same, while FSAW with FSM is only 39% the same and FTOPSIS with FSM is only 42% the same.The FSAW and FTOPSIS methods are better used than the determination of the FSM, because the results of the FSM are not appropriate.FSM selects manually by looking at files collected by registrants. Keywords:Scholarship, FSAW, FTOPSIS, Entropy, GUI

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