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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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Search results for , issue "Vol 6, No 2 (2017): Jurnal Gaussian" : 11 Documents clear

KLASIFIKASI CALON PENDONOR DARAH MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER (Studi Kasus : Calon Pendonor Darah di Kota Semarang) Dhimas Bayususetyo; Rukun Santoso; Tarno Tarno
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Classification is the process of finding a model or function that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. There are some methods that are included in the classification methods, one of them is Naïve Bayes. Naïve Bayes is a prediction technique that based simple probabilistic are based on the application of Bayes theorem with strong independence assumption. On this study carried out correction to the Naïve Bayes method in calculating the conditional probability of each feature using two approaches, normal density function and cumulative distribution function approaches. These two approaches are used to classify prospective blood donors in Semarang City. The predictor variables used are hemoglobin level, upper blood pressure, lower blood pressure, and weight. The result of this study shows that both approaches have the same Matthews Correlation Coefficient (MCC) values, 0.8985841 or close to +1. It means that both approaches equally well doing classification.Keywords: Classification, Naïve Bayes, Normal Density Function, Cumulative Distribution Function, Blood Donors, Matthews Correlation Coefficient (MCC).

PEMODELAN FIXED EFFECT GEOGRAPHICALLY WEIGHTED PANEL REGRESSION UNTUK INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH Siti Maulina Meutuah; Hasbi Yasin; Di Asih I Maruddani
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Human development index is an indicator for assessing the quality of human resources and measure the results of human development. The achievements of the human development index is not enough if conducting observations in each cities in just one particular time, but the observations need to be made in some period of time. The distribution in each cities is also a concern, because the conditions are so diverse that led to their spatial effects. Therefore, it is necessary to study these variables in some time periods that affect human development index taking into account the spatial effects. Statistical methods used to overcome their spatial effects, especially in the problem of spatial heterogeneity in the data type of panel is Geographically Weighted Panel Regression (GWPR). This study focused on the establishment of GWPR model with fixed effects using fixed exponential kernel on the human development index data cities in Central Java in 2010-2015. The results of this study indicate that the fixed effect model GWPR differ significantly on panel data regression model, and the model generated for each location will be different from one another. In addition, cities in Central Java has five groups based on variables that are significant. In the fixed effect model GWPR generates R2 value of 92.27%.Keywords: Human Development Index, Panel Data, Spatial Effects, Fixed Effect, Fixed Exponential Kernel, Geographically Weighted Panel Regression, R2.

IMPLEMENTASI METODE RESPONSE SURFACE SEBAGAI UPAYA OPTIMALISASI JUMLAH BINTIL AKAR PADA TANAMAN KEDELAI Muchammad Aziz Chusen; Rukun Santoso; Rita Rahmawati
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v6i2.16949

Response surface method is a set of statistics and mathematical techniques, useful to analyze the issue of multiple independent variables that affect to the dependent variable of response, and aim to optimize the response. The existence of response surface method is able to assist researchers in conducting improvised to get optimum results accurately and efficiently. In this experiment using the data factorial CRD (completely randomized design) with two factors and three levels. Two factors were tested consists of the elements cobalt and ferrum, with the level in the form of element concentrations with each ie cobalt (0 mg/L, 0.1 mg/L and 0.2 mg/L), and ferrum (0 mg/L, 1mg/L and 2 mg / L). Variable response is the number of nodules roots of soybean crops. After testing the response surface method produced a linear model of the first order Jumlah Bintil Akar Kedelai = 10.3 + 10.2 Ferum + 238.3 Kobalt – 1340.1 Kobalt 2 – 93 Ferum*(Kobalt 2). with the value of concentration at ferum = 2 mg/L and cobalt = 0.1 mg/L is able to generate growth in the number of nodules optimum soybean crop in these experiments. Keywords: Factorial design, Response surface

ANALISIS PEMBENTUKAN PORTOFOLIO PADA PERUSAHAAN YANG TERDAFTAR DI LQ45 DENGAN PENDEKATAN METODE MARKOWITZ MENGGUNAKAN GUI MATLAB Titin Afriana; Tarno Tarno; Sugito Sugito
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Portfolio is one of ways in investment activity that undertaken by more than one asset with intent to determining the amount of proportion of investment that to be made in a certain period of time. To determine optimal portofolio, one of analysis model which can be played is Markowitz. Markowitz exressed through diversification concept (with making of the optimal stock of portfolio), investor can maximize the expected income from investments with specific risk level or seeking to minimize risk to target certain profit level. To simplify the calculation of the portfolio for public, there is an application that made by using GUI in Matlab. Matlab (Matrix Laboratory) is an interactive programming system with basic elements of array database which dimensions do not need to be stated in a particular way, while the GUI is the submenu of Matlab. Generally, Matlab GUI is more easily learned and used because it worked without need to know the commandments and how the command works. The data used in this study consists of five types of assets in the LQ45 group, there are BBNI, PWON, PTBA, INCO, dan KLBF. In determining the portfolio proportion used trial and error method and Lagrange method. Based on the portfolio proportion of both methods obtained the optimal portfolio is almost the same. Keywords: GUI Matlab, LQ45, Portfolio, Markowitz, Trial and Error, Lagrange

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN PENGGUNAAN TRANSPORTASI PRIBADI PADA MAHASISWA MENGGUNAKAN PENDEKATAN PARTIAL LEAST SQUARE (Studi Kasus pada Universitas Diponegoro Semarang) Martyanto Tedjo; Sugito Sugito; Suparti Suparti
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The process of structural development in developing countries is a must. Each sector that developed is related to one another. These sectors associated with the supporting factor named transport, means transport has a vital and strategic functions in the development of other sectors. Education is one of the construction sector that growing rapidly, especially in big cities, and transportation is one of the factors supporting it: since schools and universities is one of the important generator of domestic transportation network. Each university holds up to tens of thousands of new college students every year. In this point, the transport activity in big cities is becoming increasingly complex, due to the increase in the private transportation is not matched by the increase in roads, causing congestion. Factors that influence the decision of the use of private transport on the student comprehensively analyzed using structural equation based on the variance, Partial Least Square (PLS). PLS is a powerful analytical method, though it’s not based on many assumptions (soft model), for example, the multivariate normal assumptions, it can use nominal scale up to ratios, as well as the sample size shouldn’t be large. PLS estimates the model od relationship between latent variables and also latent variables with the indicator. Based on the analysis we concluded that the decision on the use in private transportations of Diponegoro University students affected by a combination of latent variables such time management, cost, physical, social interaction, and the intervening variable perception of 68.28%.Keywords: transportation, using of private transportation, Partial Least Square (PLS)

PEMODELAN HARGA SAHAM DENGAN GEOMETRIC BROWNIAN MOTION DAN VALUE AT RISK PT CIPUTRA DEVELOPMENT Tbk Trimono Trimono; Di Asih I Maruddani; Dwi Ispriyanti
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Financial sector investment is an activity that attracts a lot of public interest. One of them is investing funds in purchasing company’s shares. Profit received from stock investment activity can be seen from the value of stock returns. While, if the previous stock returns Normal distribution, the future stock price can be predicted by Geometric Brownian Motion Method. Based on the stock price prediction, can also be measured an estimated value of the investment risk. The result of data processing shows that the stock price prediction of PT. Ciputra Development Tbk period December 1, 2016 untuk January 31, 2017, has very good accuracy, based on the value of MAPE 1.98191%. Further, Value at Risk Method of Monte Carlo Simulation with α = 5% significance level was used to measure the share investment risk of PT.Ciputra Development Tbk. Thus, this method is only useful if it can be used to predict accurately. Therefore, backtesting is needed. Based on the processing obtained data, backtesting generates the value of violation ratio at 0, it means that at significance level α = 5%, Value at Risk Method of Monte Carlo Simulation can be used at all levels of probability violation.. Keywords : Geometric Brownian Motion, Risk, Value at Risk, Backtesting

KLASIFIKASI DIAGNOSA PENYAKIT DEMAM BERDARAH DENGUE (DBD) MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM) BERBASIS GUI MATLAB Chainur Arrasyid Hasibuan; Moch. Abdul Mukid; Alan Prahutama
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Dengue Hemorrhagic Fever (DHF) is a disease caused by the bite of infected Aedes mosquito by one of the four types of dengue virus with clinical manifestations of fever, muscle aches or joint pain which followed by leukopenia, rash, thrombocytopenia and hemorrhagic diathesis. There are six criteria for determining and catagorizing a positive or negative dengue patients, the variable gender of the patient, the patient's age, the increase in hemoglobin (Hb), increased hematocrit (Hct), the level of platelet and leukocyte levels.Based on these criteria, data of positive and negative catagorized patient will be classified by Support Vector Machine (SVM) using Matlab software. The concept of classification with SVM define as a search for the best hyperplane which serves as a divider of two classes of data in the input space. Kernel function is used to convert the data into a higher dimensional space to allow separation. In order to determine the best parameters of kernel function, hold-out method is used. In the classification by SVM method, 96.4286% obtained as the best accuracy value by using polynomial kernel function. Keywords: Dengue Hemorrhagic Fever (DHF), Classification, Support Vector Machine (SVM), hold-out, Kernel Function.

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 RISIKO INVESTASI SAHAM TUNGGAL SYARIAH DENGAN VALUE AT RISK (VAR) DAN EXPECTED SHORTFALL (ES) Saepudin, Yunus; Yasin, Hasbi; Santoso, Rukun
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One measure that can be used to estimate risk is Value at Risk (VaR). Although VaR is very popular, it has several weakness that VaR not coherent causes the lack of sub-additive. To overcome the weakness in VaR, an alternative risk measure called Expected Shortfall (ES) can be used. The porpose of this research objective are to estimate risk by ES and by using VaR with Monte Carlo simulation. The data we used are the closing price of Unilever Indonesia stocks that consistently get into Jakarta Islamic Index (JII). To make VaR become easier for people to understand, an application is made using GUI in Matlab. The Expected Shortfall results from the calculation using 99% confidence level that may be experienced is at 0.039415 show that the risk exceed the VaR it is at 0.034245. For 95% confidence level that may be experienced is at 0.030608 show that the risk exceed the VaR it is at 0.024471. For 90% confidence level that may be experienced is at 0.026110 show that the risk exceed the VaR it is at 0.019172. Show that the greater the level of confidence that is used the greater the risk will be borne by the investor.Keywords: Risk, Value at Risk (VaR), JII, Expected Shortfall (ES).

KLASIFIKASI KINERJA PERUSAHAAN DI INDONESIA DENGAN MENGGUNAKAN METODE WEIGHTED K NEAREST NEIGHBOR (Studi Kasus: 436 Perusahaan Yang Terdaftar Di Bursa Efek Indonesia Tahun 2015) Cyntia Surya Utami; Moch. Abdul Mukid; Sugito Sugito
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

A company's performance can be seen from the analysis of the company's financial statements. Financial statement analysis is used to determine the development of the company's financial condition. In analyzing the financial statements required financial ratios depicting the weight of the company's performance. This thesis aims to classify the performance of the company into two classifications, namely the company healthy and unhealthy companies as well as determine the level of accuracy. This final project using financial ratio data 436 companies listed in the Indonesia Stock Exchange in 2015 which has been audited and is divided into two parts of 349 training data and 87 test data. The method used is the weighted k nearest neighbor with a dependent variable is the performance of the company and six independent variables are financial ratios WCTA, ROA, TATO, DAR, LDAR and ROI. The results of this thesis show that the method of calculation of weighted k k nearest neighbor optimal done by trial and error. Provided that the optimal k at k = 3 for kernel inversion, epanechnikov and triangles while for optimal kernel k gauss at k = 4. The accuracy of classification and classification performance of the company gave almost the same results by using kernel inversion, Gauss, epanechnikov and triangles. Keywords: financial ratios, weighted k nearest neighbor and kernel inversion, Gauss, epanechnikov and triangles.

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Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275

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Kota semarang,

Jawa tengah

INDONESIA