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Alan Prahutama
Universitas Diponegoro

Published : 76 Documents
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REGRESI SEMIPARAMETRIK SPLINE TRUNCATED DENGAN SOFTWARE R Utami, Tiani Wahyu; Prahutama, Alan
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi

#### Abstract

Metode statisika sangat berperan penting dalam memprediksi maupunmenduga. Salah satu metode yang digunakan adalah dengan analisisregresi semiparametrik spline. Bentuk estimator Spline Truncated sangatdipengaruhi oleh nilai titik knots. Oleh karena itu, pemilihan titik knotoptimal mutlak diperlukan. Metode pemilihan titik knot dengan GCV. Â Dua algoritma dan pemogramanÂ  untuk mendapatkan pemodelan regresisemiparametrik spline truncated dengan menggunakan software R yaitualgoritma dan program untuk menentukan Knot optimal berdasarkan metodeGCV, algoritma dan program untuk mengestimasi model regresisemiparametrik Spline Truncated.Â Keywords: Spline Truncated, GCV, Software R.
KAJIAN PEMODELAN SPLINE UNTUK DATA LONGITUDINAL SEBAGAI PERKEMBANGAN DARI REGRESI NONPARAMETRIK Suparti, Suparti; Prahutama, Alan; Santoso, Rukun
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi

#### Abstract

Regression analysis can be approached by using parametric, semi-parametricand nonparametric regression approaches. One of nonparametric regressionapproach that great developed was Spline truncated, including for modelinglongitudinal data. Longitudinal data is data that consisting of several subjectswhich is each subject is observed repeatedly based on a certain time. Theadvantages of longitudinal data has provided more complexcityofÂ  informationthan cross section and time series data. The spline approach was a segmentedpolynomial regression approach. Spline provides high flexibility due to the useof knot points. To determine the optimal knot points using Generalized CrossValidation (GCV). The principle of determining the optimum point of knot oflongitudinal data using spline truncated is basically the same as with SplinemethodÂ  for cross section data, that is determination of knot point based on eachsubject. However, the estimation is done simultaneously so that each subject hasits own model. Keywords: Spline Truncated, GCV, Knot points.
MANAJEMEN PRODUKSI PENGOLAHAN IKAN BANDENG DI KABUPATEN PATI UNTUK PENGEMBANGAN PRODUK EKSPOR -, Sugito; Prahutama, Alan; Hoyyi, Abdul
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: PROSIDING IMPLEMENTASI PENELITIAN PADA PENGABDIAN MENUJU MASYARAKAT MANDIRI BERKEMAJUAN

#### Abstract

Central Java as one of the regions in Indonesia which has various food preparations. One typical souvenirs Semarang, which is the capital of Central Java province is milkfish presto, milkfish brains, chips and shredded milkfish milkfish. Aquaculture fish of the largest in Central Java is located in Pati regency. One of the famous places that serve as souvenirs of milkfish is Juwana area, Pati. Many SMEs (Small and Medium Enterprises) in Pati engaged in the processing of aquaculture fish, one of which is milkfish presto. In this paper, the researchers took two of SMEs as a form of field studies engaged in the processing of fish. SMEs are SMEs milkfish Presto twin source of fortune and SMEs banding without thorns excellent. Both of us empower SMEs for the development of export products. In the first year field study outcome shows that the manufacture of standard operating procedures (SOP) and product diversification is the first step the product criteria ekspor. Also marketing techniques is an important factor in the expansion of the market.Keywords: Processed milkfish; Diversification of products; marketing Management; SOP; SME in Pati.
PERAMALAN BEBAN PEMAKAIAN LISTRIK JAWA TENGAH DAN DAERAH ISTIMEWA YOGYAKARTA DENGAN MENGGUNAKAN HYBRID AUTOREGRESIVE INTEGRATED MOVING AVERAGE â€“ NEURAL NETWORK Fitriani, Berta Elvionita; Ispriyanti, Dwi; Prahutama, Alan
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

#### Abstract

Excessive use of electronic devices in household and industry has made the demand of nationâ€™s electrical power increase significantly these days. As a corporation that aim to provide national electrical power,Â  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.Â  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 VECTOR AUTOREGRESSIVE X (VARX) UNTUK MERAMALKAN JUMLAH UANG BEREDAR DI INDONESIA Rosyidah, Haniatur; Rahmawati, Rita; Prahutama, Alan
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

#### Abstract

The economic stability of a country can be seen from the value of inflation. The money supply in a country will affect the value of inflation, so it is necessary to control the money supply. The money supply in Indonesia consists of currency, quasi money, and securities other than shares. One of the factors affecting the amount of currency, quasi money, and securities other than shares is the SBI interest rate. Time series data from the money supply components are correlated. To explain multiple time series data variables that are correlated we can use the VAR approach. VAR model with the addition of an exogenous variable is called VARX. The purpose of this study is to obtain models to predict the amount of currency, quasi money, securities other than shares using the VARX approach with the SBI interest rate as an exogenous variable. The results of data analysis in this study, the model obtained is VARX (1,1). Based on t test with 5% significance level, SBI interest rate variable has no significant effect to variable of currency amount, amount of quasi money, or amount of securities other than shares. Residual model VARX (1,1) satisfies the white noise assumption, while the normal multivariate assumption is not satisfied. The value of MAPE for currency variables (7,53969%), quasi money (0,49036%), and securities other than shares (9,64245%) indicates that the VARX (1,1) model has excellent forecasting ability that can be used for forecasting future periods. Forecasting results indicate an increase in the amount of currency, quasi money, or securities other than shares in each period..Keywords : Amount of currency, amount of quasi money, amount of securities other than shares, SBI interest rate, VARX, MAPE
PEMILIHAN INPUT MODEL ANFIS UNTUK DATA RUNTUN WAKTU MENGGUNAKAN METODE FORWARD SELECTION DILENGKAPI GUI MATLAB (Studi Kasus: Jumlah Penumpang Kereta Api di Wilayah Jawa Non Jabodetabek) Valentina, Tiara Sukma; Tarno, Tarno; Prahutama, Alan
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

#### Abstract

One of the methods that is commonly used to identify a time series model and input ANFIS (Adaptive Neuro Fuzzy Inference System) model is PACF plot. The PACF plot shows the correlation between current observations and previous observations visually. Formally there are several methods that are known to effectively identify ANFIS inputs, one of which is the Forward Selection regression method. With the same concept as PACF, the process of selecting ANFIS inputs using the Forward Selection method is based on the order of the correlatiom between the predictors of the response which is indicated by the magnitude of the correlation coefficient. This study discusses the Forward Selection method in simulation data that has stationary characteristics, stationary with outliers, non stationary, non stationary with outliers and implements data on the number of train passengers in the Non Jabodetabek Java region. ANFIS modeling on data of the number of train passengers in the Non Jabodetabek Java region produces AIC of 15,5617, MAPE of 8,5093% and RMSE of 571,33691. The result of this study is equipped with a GUI which is useful as a tool to facilitate users in processing data.Keywords : PACF Plot, Forward Selection, ANFIS, non stasionary, outlier
PERAMALAN JUMLAH PENUMPANG KERETA API MENGGUNAKAN METODE ARIMA, INTERVENSI DAN ARFIMA (Studi Kasus : Penumpang Kereta Api Kelas Lokal EkonomiDAOP IV Semarang) Panjaitan, Helmi; Prahutama, Alan; Sudarno, Sudarno
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

#### Abstract

Autoregressive Integrated Moving Average (ARIMA) is stationary time series model after differentiation. Differentiation value of ARIMA method is an integer so it is only able to model in the short term. The best model using ARIMA method is ARIMA([13]; 1; 0) with an MSE value of 1,870844. The Intervention method is a model for time series data which in practice has extreme fluctuations both up and down. In the data plot the number of train passengers was found to be extreme fluctuation. The data used was from January 2009 to June 2017 where fluctuation up significantly in January 2016 (T=85 to T=102) so the intervention model that was suspected was a step function. The best model uses the Intervention step function is ARIMA ([13]; 1; 1) (b=0; s=18; r=0) with MSE of 1124. Autoregressive Fractionally Integrated Moving Average (ARFIMA) method is a development of the ARIMA method. The advantage of the ARFIMA method is the non-integer differentiation value so that it can overcome long memory effect that can not be solve with the ARIMA method. ARFIMA model is capable of modeling high changes in the long term (long term persistence) and explain long-term and short-term correlation structures at the same time. The number of local economy class train passengers in DAOP IV Semarang contains long memory effects, so the ARFIMA method is used to obtain the best model. The best model obtained is the ARMA(0; [1,13]) model with the differential value is 0,367546, then the model can be written into ARFIMA (0; d; [1,13]) with an MSE value of 0,00964. Based on the analysis of the three methods, the best method of analyzing the number of local economy class train passengers in DAOP IV Semarang is the ARFIMA method with the model is ARFIMA (0; 0,367546; [1,13]).Â Keywords: Train Passengers, ARIMA, Intervention, ARFIMA, Forecasting
PENGUKURAN KINERJA PORTOFOLIO SAHAM MENGGUNAKAN MODEL BLACK-LITTERMAN BERDASARKAN INDEKS TREYNOR, INDEKS SHARPE, DAN INDEKS JENSEN (Studi Kasus Saham-Saham yang Termasuk dalam Jakarta Islamic Index Periode 2009-2013) Azizah, Siti; Sugito, Sugito; Prahutama, Alan
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

#### Abstract

The composing of portfolio is one of the way to minimize the risk of investment. Through portfolio, it is expected that some stocks still give return when other stocks are loss. From this composed portfolio, every investor expect appropriate return. The higher the return is better. Black-Litterman Model is the method which optimize the investorâ€™s return by giving difference financial capital proportion for every stocks of portfolio. This method combines both the aspect historical data and the investor view to make new prediction about return of portfolio as the basic to compose the weight model of assets. Investor often compose some portfolio to plan their investment, to compare the performance (capability to produce return and also risk) from any number of portfolio, before evaluating whether the performance of chosen portfolio has been appropriate with the expectation. The measurement of the performance of portfolio is done by using Sharpe, Treynor, and Jensen Indeks. The result of the case study of eleven Jakarta Islamic Indexstocks in the period of 2009-2013 recommend the portfolio with the best perform, whichis optimized which Black-Litterman Model. Based on Sharpe Indeks, the best portfolio consists of SMGR 60,79% and INTP 39,21% of capital allocation. Based on Treynor and Jensen Indeks, the best portfolio consists of SMGR 22,59%, INTP 37,67%, PTBA 1,62%, ANTM 2,69%, ITMG 16,17%, and KLBF 19,26%.Â Keywords :Â Â Â Â  JII, Portfolio, Black-Litterman Model, Treynor Index, Sharpe Index, Jensen Index.Â
ANALISIS KINERJA PORTOFOLIO OPTIMAL DENGAN METODE MEAN-GINI Susilowati, Mega; Rahmawati, Rita; Prahutama, Alan
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

#### Abstract

Investments in financial assets has a special attraction that investors can form a portfolio. Portfolio is investment which comprised of various stocks from different companies. A common issue is the uncertainty when investors are faced with the need to choose stocks to be formed into a portfolio of his choice. A rational investor, would choose the optimal portfolio. Determination of the optimal portfolio can be performed by various methods, one of which is a method of Mean-Gini. Mean-Gini is the expected value of the portfolio return is the weight density function while the random variable is the average of the shares. Mean-Gini methods used to generate the greatest value of portfolio return expectations with the smallest risk. Mean-Gini does not require the assumption of normality on stock returns. Mean-Gini was first introduced by Shalit and Yitzhaki in 1984. This research uses data of closing prices stocks from January 2008 to December 2015. Measurement of portfolio performance with Mean-Gini performed using the Sharpe index. Based on Sharpe index, the optimal portfolio is second portfolio with three stocks portfolio and the proportion investments are 25.043% for SMGR, 68.148% for UNVR and 6.809% for BMRI.Â Keywords:Â Â  Stock, Portfolio, Mean-Gini, Sharpe index.
PEMODELAN DEFORESTASI HUTAN LINDUNG DI INDONESIA MENGGUNAKAN MODEL GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION (GTWR) Adiningrumh, Thea Zulfa; Prahutama, Alan; Santoso, Rukun
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

#### Abstract

Regression analysis is a statistical analysis method that is used to modeling the relationship between dependent variables and independent variables. In the linear regression model only produced parameter estimators are globally, so itâ€™s often called global regression. While to analyze spatial data can be used Geographically Weighted Regression (GWR) method. Geographically and Temporally Weighted Regression (GTWR) is the development of Â GWR model to handle the instability of a data both from the spatial and temporal sides simultaneously. In this GWR modeling the weight function used is a GaussianÂ  Kernel, which requires the bandwidth value as a distance parameter. Optimum bandwidth can be obtained by minimizing the CV (cross validation) coefficient value. By comparing the R-square, Mean Square Error (MSE) and Akaike Information Criterion (AIC) values in both methods, it is known that modeling the level of deforestation in protected forest areas in Indonesia in 2013 through 2016 uses the GTWR method better than global regression. With the R-square value the GTWR model is 25.1%, the MSE value is 0.7833 and AIC value is 349,6917. While the global regression model has R-square value of 15.8%, MSE value of 0.861 and AIC value of 361,3328.Â Keywords : GWR, GTWR, Bandwidth, Kernel Gaussian