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All Journal PROSIDING SEMINAR NASIONAL Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang

Alan Prahutama

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Author-ID : 452056

Decision Sciences, Operations Research & Management Education Other

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PEMBENTUKAN POHON KLASIFIKASI BINER DENGAN ALGORITMA QUEST (QUICK, UNBIASED, AND EFFICIENT STATISTICAL TREE) PADA DATA PASIEN LIVER Muhammad Rosyid Abdurrahman; Dwi Ispriyanti; Alan Prahutama
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In this modern era of fast food commonly found that sometimes have chemical substances and the increasing number of motor vehicles that cause the uncontrolled circulation of air pollution that can affect the health of the human liver. To assist in analyzing the presence of liver disorders in humans can be used QUEST (Quick, Unbiased, and Efficient Statistical Tree) algorithm to classify the characteristics of the patient's liver by liver function tests performed in clinical laboratories. QUEST construct rules to predict the class of an object from the values of predictor variables. The tree is constructed by partitioning the data by recuresively, where class and the values of the predictor variables of each observation in the data sample is known. Each partition is represented by a node in the tree. QUEST is one of the binary classification tree method. The results of the classification tree is formed, an important variable in classifying a person affected by liver disease or not, that is the variable Direct Bilirubin, Alkaline Phosphatase, Serum Glutamic Oxaloacetic Transaminase (SGOT), and age of the patient. Accuracy of the QUEST algorithm classifying liver patient data by 73,4 %. Keywords: binary classification trees, QUEST algorithm, liver patient data.

GEOGRAPHICALLY WEIGHTED REGRESSION PRINCIPAL COMPONENT ANALYSIS (GWRPCA) PADA PEMODELAN PENDAPATAN ASLI DAERAH DI JAWA TENGAH Nurmalita Sari; Hasbi Yasin; Alan Prahutama
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Linear Regression Analysis is a method for modeling the relation between a response variable with two or more independent variables. Geographically Weighted Regression (GWR) is a development of the regression model where each observation location has different regression parameter values because of the effects of spatial heterogenity. Regression Principal Component Analysis (PCA) is a combination of PCA and are used to remove the effect of multicolinearity in regression. Geographically Weighted Regression Principal Component Analysis (GWRPCA) is a combination of PCA and GWR if spatial heterogenity and local multicolinearity occured. Estimation parameters for the GWR and GWRPCA using Weighted Least Square (WLS). Weighting use fixed gaussian kernel function through selection of the optimum bandwidth is 0,08321242 with minimum Cross Validation (CV) is 3,009035. There are some variables in PCA that affect locally-generated revenue in Central Java on 2012 and 2013, which can be represented by PC1 that explained the total variance data about 71,4%. GWRPCA is a better model for modeling locally-generated revenue for the districts and cities in Central Java than RPCA because it has the the smallest Akaike Information Criterion (AIC) and the largest R2. Keywords : Spatial Heterogenity, Local Multicolinearity, Principal Component Analysis, Geographically Weighted Regression Principal Component Analysis.

PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA JUMLAH WISATAWAN MANCANEGARA EMPAT KABUPATEN/KOTA DI JAWA TENGAH Ronny Gusnadi; Rita Rahmawati; Alan Prahutama
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling number of international tourist four locations in Magelang Regency, Surakarta City, Wonosobo Regency, and Karanganyar Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR(11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on International tourist arrivals Surakarta City influenced by the International tourist in other regencies. Keywords: GSTAR, RMSE, International Tourist

PERAMALAN BEBAN PEMAKAIAN LISTRIK JAWA TENGAH DAN DAERAH ISTIMEWA YOGYAKARTA DENGAN MENGGUNAKAN HYBRID AUTOREGRESIVE INTEGRATED MOVING AVERAGE – NEURAL NETWORK Berta Elvionita Fitriani; Dwi Ispriyanti; Alan Prahutama
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Excessive use of electronic devices in household and industry has made the demand of nation’s electrical power increase significantly these days. As a corporation that aim to provide national electrical power, Perusahaan Listrik Negara (PLN) that distributes electrical power to Central Java and Yogyakarta has to be able to provide an economical and reliable system of electrical power provider. This study aimed to forecast data of electrical power usage in Central Java and Yogyakarta for the next 30 days. There were three forecasting methods used in this study; Neural Networks and Hybrid ARIMA-NN. The data used in this study was electrical power usage data in January 2014 - November 2014 in Central Java and Yogyakarta. The accuracy of the study was measured based on MSE criteria where the best model chosen was the model that has lowest MSE value. According to the result of the analysis, using Neural Networks model to forecast electrical power usage for the next 30 days has better forecasting result than Hybrid ARIMA-NN model.Key Word : electrical power usage, forecasting of electrical power usage, ARIMA, NN, hybrid ARIMA-NN

PEMODELAN GENERAL REGRESSION NEURAL NETWORK (GRNN) PADA DATA RETURN INDEKS HARGA SAHAM EURO 50 Rezzy Eko Caraka; Hasbi Yasin; Alan Prahutama
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

General Regression Neural Network (GRNN) merupakan salah satu model jaringan radial basis yang digunakan untuk pendekatan suatu fungsi. Model GRNN termasuk model jaringan syaraf tiruan dengan solusi yang cepat, karena tidak diperlukan iterasi yang besar pada estimasi bobot-bobotnya. Model ini memiliki arsitektur jaringan yang baku, dimana jumlah unit pada pattern layer sesuai dengan jumlah data input. Salah satu aplikasi GRNN adalah untuk memprediksi nilai return saham dari indeks Euro 50 CFD (Contract For Difference). Indeks Euro 50 CFD (Contract For Difference) digunakan sebagai patokan harga saham dari 50 perusahaan terbesar di zona Eropa. Para investor melakukan investasi di saham indeks Euro 50 CFD (Contract For Difference) dengan harapan mendapatkan kembali keuntungan yang sesuai dengan apa yang telah di investasikannya. Dengan menggunakan model GRNN diperoleh bahwa nilai RMSE dan R2 untuk data training sebesar 0,00095 dan 99,19%. Untuk data testing diperoleh nilai RMSE dan R2 sebesar 0,00725 dan 98,46%. Berdasarkan nilai prediksi return saham dua belas hari ke depan diperoleh kerugian tertinggi atau capital loss pada 15 Desember 2014 sebesar 5,583188% dan profit tertinggi atau capital gain pada tanggal 10 Desember 2014 sebesar 2,267641% Kata Kunci: GRNN, Jaringan Syaraf Tiruan, Return Saham, Indeks Euro 50, Kerugian Tertinggi, Profit Tertinggi, Prediksi

ANALISIS SUPPORT VECTOR REGRESSION (SVR) DALAM MEMPREDIKSI KURS RUPIAH TERHADAP DOLLAR AMERIKA SERIKAT Rizky Amanda; Hasbi Yasin; Alan Prahutama
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In economy, the global markets have an important role as a forum for international transactions between countries in selling or purchasing goods or services on an international scale. Money as legal tender in the trading activities, but the problem is the difference between the state of the currency, the exchange rate will be established. Exchange rate is the value of a country's currency is expressed in another country's currency value. Fluctuations in foreign exchange rates greatly affect the Indonesian economy, so the determination of the exchange rate should be beneficial to a country can run the economy well. To predict the exchange rate of the Rupiah against the United States dollar in this study used methods of Support Vector Regression (SVR) is a technique to predict the output in the form of continuous data. SVR aims to find a hyperplane (line separator) in the form of the best regression function is used to predict the exchange rate against the United States dollar with linear kernel and polynomial functions. Criteria used in measuring the goodness of the model is the MAPE (Mean Absolute Percentage Error) and R2 (coefficient of determination). The results of this study indicate that both the kernel function gives very good accuracy in the prediction results of the exchange rate with R2 of 99.99% with MAPE 0.6131% in the kernel linear and R2 result of 99.99% with MAPE 0.6135% in the kernel polynomial. Keyword : Exchange rate, Support Vector Regression (SVR), Hyperplane, Linear Kernel, Polynomial Kernel, ε-insensitive, Accuracy

PERBANDINGAN DISKRIMINAN KUADRATIK KLASIK DAN DISKRIMINAN KUADRATIK ROBUST PADA KASUS PENGKLASIFIKASIAN PEMINATAN PESERTA DIDIK (Studi Kasus di SMA Negeri 1 Kendal Tahun Ajaran 2014/2015) Laili Isna Nur Khiqmah; Moch. Abdul Mukid; Alan Prahutama
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Discriminant is a multivariate statistical technique that can be used to perform the classification new observation into a particular group. Quadratic discriminant analysis tied to an assumption of normal multivariate distributed observations and variance covariance matrix inequality. Robust quadratic discriminant analysis can be used if the observations contain outliers. Classification using robust quadratic discriminant analysis with the Minimum Covariance Determinant (MCD) estimator in the data specialization students of SMA Negeri 1 Kendal that containing outliers gives the results of the classification accuracy of 95,06% with a percentage of 4,94% classification error while generating the classical quadratic discriminant analysis classification accuracy of 92,59% with a percentage of 7,41% classification error. Thus a robust quadratic discriminant analysis with the MCD estimator is more appropriate in the case of the data which contains outliers. Keywords : discriminant, outliers, robust, MCD estimators, classification

KLASIFIKASI PASIEN DIABETES MELLITUS MENGGUNAKAN METODE SMOOTH SUPPORT VECTOR MACHINE (SSVM) Rizky Adhi Nugroho; Tarno Tarno; Alan Prahutama
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Diabetes Mellitus (DM) is a high-risk metabolic diseases. Laboratory tests are needed to determine if the patients suffer from a Diabetes Mellitus. Therefore, it needs a classification methods that can precisely classify data according to the classes criteria. SVM is one of commonly used methods of classification. The basic concept of SVM is to find the bes separator function (hyperplane) that separates the data according its class. SVM uses a kernel trick for nonlinear problems, which transforms data into high-dimensional space using kernel functions, so it can be classified linearly. This research will use a developed methods of SVM called SSVM, that adds smoothing function using Newton-Armijo method. The smoothing methods is needed to correct the effectiveness of SVM in big data classifying. The result is indicating tha SSVM classification prediction using Gaussian RBF kernel function, can classify 98 out of 110 patient data of Diabetes Mellitus correctly according the original class.Keywords : Diabetes Mellitus, Classification, Support Vector Machine (SVM), Smooth Support Vector Machine (SSVM), Kernel Gaussian RBF.

METODE REGRESI DATA PANEL UNTUK PERAMALAN KONSUMSI ENERGI DI INDONESIA Mariska Srihardianti; Mustafid Mustafid; Alan Prahutama
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Panel data regression is a method that aims to model the effect of one or more predictor variables on the response variable, observed in some sectors of an object of research for a specific time period. To estimate the panel data regression model, there are three approaches, namely Common Effect Model (CEM), Fixed Effects Model (FEM) and Random Effects Model (REM). In estimating the parameters for each model, there are several methods that can be used based on the assumption of the structure residual variance-covariance matrix, that is Ordinary Least Square/Least Square Dummy Variable (OLS/LSDV), Weighted Least Square (WLS) dan Seemingly Unrelated Regression (SUR). This research aims to implement the panel data regression to analyze the effect of GDP on energy consumption in Indonesia for each sector. Panel data regression model that has been obtained then is used to predict the amount of energy consumption in Indonesia for each sector in 2015 and 2016 using trend analysis. The analysis showed that the panel data regression model corresponding to the data of energy consumption in Indonesia in 1990-2014 is Fixed Effect Model (FEM) with Cross-section SUR, with R2 value is 0.975943. Forecasting results show energy consumption in Indonesia in 2015 and 2016 will increase to the household sector and transport. Whereas for industrial, commercial and others sectors will decline in 2015 and then increase in 2016. Keywords : Panel Data, Fixed Effect Model, SUR, Trend Analysis, Energy Consumption

KLASIFIKASI PENERIMA PROGRAM BERAS MISKIN (RASKIN) DI KABUPATEN WONOSOBO DENGAN METODE SUPPORT VECTOR MACHINE MENGGUNAKAN LibSVM Yogi Setiyo Pamuji; Diah Safitri; Alan Prahutama
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Beras Miskin (Raskin) Program is a program of social protection, as supporters of other programs such as nutrition improvement, healthy increase, education and productivity improvement of Poor Households. According to Badan Pusat Statistika, there were 14 criteria to determine a household is classified as poor households. Based on these criteria it will be classified of recipient households and non-recipient households of Beras Miskin (Raskin) Program by Support Vector Machine (SVM) method using LibSVM. The concept of classification by SVM is search for the best hyperplane which serves as a separator of two classes of data in the input space. Kernel function is used to convert the data into a higher dimensional space to allow a separation. LibSVM is a package program created by Chih-Chung Chang and Chih-Jen Lin from Department of Computer Science at National Taiwan University. The method used by LibSVM to obtain global solution of duality lagrange problem is decomposition method. To determine the best parameters of kernel function, used k-vold cross validation method and grid search algorithm. In this classification by SVM method using LibSVM, obtain the best accuracy value as 83,1933%, which is the kernel function Radial Basis Function (RBF). Keywords : Beras Miskin (Raskin) Program, Classification, Support Vector Machine (SVM), LibSVM, Kernel Function

Co-Authors - Sugito Abdul Hoyyi Agus Rusgiyono Arief Rachman Hakim Aselina Pratidina Wrediningsih Azizah Mulia Mawarni Berta Elvionita Fitriani Budi Warsito Chainur Arrasyid Hasibuan Diah Safitri Dian Andhika Prameswara Dwi Ispriyanti Dwi Ispriyanti Dwi Ispriyanti Erna Fransisca Angela Sihotang Fathimatuzzahra Syahrozad Nuroqi Fizry Listiyani Maulida Hasbi Yasin Irawati Tamara Jenesia Kusuma Wardhani Laili Isna Nur Khiqmah Luthfi Nashukha Dewi Mariska Srihardianti Mega Susilowati Moch Abdul Mukid Moch. Abdul Mukid Muhammad Rosyid Abdurrahman Mustafid Mustafid Nia Puspita Sari Nunung Hanurowati Nurmalita Sari Omy Wahyudi Rengganis Purwakinanti Rezzy Eko Caraka Rita Rahmawati Rizky Adhi Nugroho Rizky Amanda Rizky Dwi Rahmawati Ronny Gusnadi Rukun Santoso Siti Azizah Sudarno Sudarno Sugito - Sugito Sugito Suparti - Suparti Suparti Tarno Tarno Tatik Widiharih Tiani Wahyu Utami Viliyan Indaka Ardhi Yogi Setiyo Pamuji

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