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All Journal Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang JURNAL PENDIDIKAN ROKANIA Jurnal Pengabdian Masyarakat Indonesia Sejahtera

Tarno Tarno

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Author-ID : 2977088

Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Other

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ANALISIS KLASTER KECAMATAN DI KABUPATEN SEMARANG BERDASARKAN POTENSI DESA MENGGUNAKAN METODE WARD DAN SINGLE LINKAGE Annisa Nur Fathia; Rita Rahmawati; Tarno Tarno
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Physical and non-physical aspects are the ways to explain a diversity among regions, including a diversity among districts. Village potential providing data about the existence, availability and development potential of each administrative area. To know the district that has the same characteristics, do the grouping using cluster analysis. Cluster analysis is a grouping of objects or cases into groups smaller where each group contains objects that are similar to one another. Clustering process is done for 19 districts in Semarang Regency by ward’s method and single linkage. Four cluster are chosen for the process of potential developing more specific in each district. From the analysis using ward’s method, 1st cluster obtained with minimal educational facilities. 2nd cluster with minimal health facilities. 3rd cluster with the districts which caracteristics itself have a good condition. 4th cluster with minimal power line facilities. From the analysis using single linkage method, 1st cluster obtained with a good condition of power line facilities. 2nd cluster with a good condition of educational facilities. 3rd cluster with a minimal educational facilities. 4th cluster with minimal power line facilities. R-Squared value from single linkage method is higher than ward’s method, this shows the single linkage clustering method produces cluster features with each other more heterogeneous compared to the clustering method ward. Keywords: Cluster Analysis, Ward’s Method, Single Linkage, Distict, Village Potential.

OPTIMASI VALUE AT RISK REKSA DANA MENGGUNAKAN METODE ROBUST EXPONENTIALLY WEIGHTED MOVING AVERAGE (ROBUST EWMA) DENGAN PROSEDUR VOLATILITY UPDATING HULL AND WHITE Khalida Hanum; Tarno Tarno; Sudarno Sudarno
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Risk measurement is important in making investments. One tool to measure risk is Value at Risk (VaR), which is the worst possible loss on a given time horizon under normal market conditions with a certain confidence level. The successful implementation of VaR depends on conditional volatility estimates of portfolio returns. Robust Exponentially Weighted Moving Average (robust EWMA) is one approach in forecasting the conditional volatility of asset returns. Robust EWMA is suitable for financial data analysis which is heteroscedastic and not normally distributed. The final VaR is calculated using historical simulation method with updated data return through volatility updating Hull and White procedure. In this research, robust EWMA is used for portfolio VaR calculation with case study of mutual funds shares BNI AM Dana Berkembang (BNI), Manulife Dana Saham Utama (MDSU) and Mega Asset Greater Infrastructure (MAGI). Validity testing of VaR was conducted based on Basel rule and Kupiec's proportion of failures (PF) test. The result of backtesting test shows that the obtained VaR are valid to predict the loss of the equity fund portfolio at both 95% and 99% confidence level.Keywords : mutual fund, Value at Risk, robust EWMA, volatility updating

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

PENENTUAN TREN ARAH PERGERAKAN HARGA SAHAM DENGAN MENGGUNAKAN MOVING AVERAGE CONVERGENCE DIVERGENCE (Studi Kasus Harga Saham pada 6 Anggota LQ 45) Tri Murda Agus Raditya; Tarno Tarno; Triastuti Wuryandari
Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One of many examples of technical indicator that frequently used for stock price analysis is Moving Average Convergence Divergence (MACD). MACD generates two signal called goldencross and deathcross are used to find the reversal momentum of stock price trend movement. Goldencross as a oversold point marker serves to give a buying signal. While, deathcross as a overbought point marker serves to give a selling signal. Research on six stocks member of LQ45 (ANTM, BWPT, MNCN, TINS, BJBR, and LPKR) during the period January 1 until October 31, 2012 managed to prove the accuracy of the signal formed by MACD signal. By applying the MACD Indicator consistently, investors can get a percentage of profit above the actual inflation rate in 2012 by Indonesian Bank. On these results, the goldencross and deathcross signal give a good performance as tool of technical analysis for determining the trend of the direction of stock price movements

PERAMALAN OUTFLOW UANG KARTAL DI BANK INDONESIA WILAYAH JAWA TENGAH DENGAN METODE GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) Aukhal Maula Fina; Tarno Tarno; Rukun Santoso
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Generalized Space Time Autoregressive (GSTAR) model is a method that has interrelation between time and location or called with space time data. This model is generalization of Space Time Autoregressive (STAR) model where GSTAR more flexible for data with heterogeneous location characteristics. The purposes of this research are to get the best GSTAR model that will be used to forecast the outflow in the Bank Indonesia Office (BIO) Semarang, Solo, Purwokerto and Tegal. The best model obtained in this study is GSTAR (11) I(1) using the inverse distance weighting locations. This model has an average value of MAPE 35.732% and RMSE 440.52. The best model obtained explains that the outflow in BIO Semarang, Solo and Purwokerto are affected by two time lag before while for outflow in BIO Tegal is affected by two time lag befor and outflows in three other BIO. Keywords: GSTAR, Space Time, Outflow, Currency

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.

ANALISIS INTEGRASI PASAR BAWANG MERAH MENGGUNAKAN METODE VECTOR ERROR CORRECTION MODEL (VECM) (Studi Kasus: Harga Bawang Merah di Provinsi Jawa Tengah) Rizky Aditya Akbar; Agus Rusgiyono; Tarno Tarno
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Spatial market integration is the degree of closeness of relationship between the regional market with other regional market. Vertical market integration is the level of the relationship between a marketing agency with other marketing agencies in the marketing. Spatial market integration in onion prices at the wholesale level for the area of Brebes, Tegal, Pemalang, Semarang, Salatiga, Surakarta and can be analyzed using the Vector Error Correction Model (VECM) to see where the long-term relationship. Vertical market integration in onion prices in the wholesale and consumer levels for Tegal, Semarang and Surakarta can be analyzed using the Granger Causality. The data used are the monthly time series data from January 2010 until February 2016, where data must be stationary at the first difference. Based on Johansen cointegration test obtained their long-term relationship at all six of the region and can be used to analyze VECM method. Granger Causality used as a test of causality. From this study, it can be concluded the onion market in Central Java is not fully integrated spatial whereby if shocks in Brebes then be transmitted to the market in Pemalang, Semarang, Salatiga and Surakarta whereas if shocks occur in Tegal will be transmitted to Semarang, Salatiga and Surakarta. This occurs because Brebes as the central region producing onion and Tegal as many areas in need of onion. The existence of vertical integration only occurs in Semarang, although only one-way causality. Keywords: Market Integration, Johansen Cointegration Test, VECM, Granger Causality Test.

OPTIMASI VALUE AT RISK PADA REKSA DANA DENGAN METODE HISTORICAL SIMULATION DAN APLIKASINYA MENGGUNAKAN GUI MATLAB Christa Monica; Tarno Tarno; Hasbi Yasin
Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Value at Risk (VaR) is a method used to measure financial risk within a firm or investment portfolio over a specific time period at certain confidence interval level. Historical Simulation is used in this research to compute VaR of stock mutual fund at 5% confidence interval level, with one day time period and Rp 100.000.000,00 startup investment fund. Historical Simulation ia a non parametric method where the formula doesn’t require any asumption. Portfolio optimization is done by calculating the weight of allocation fund for each asset in the portfolio using Mean Variance Efficient Portfolio (MVEP) method. The data in this research are divided into four mutual fund asset. To make VaR become easier for people to understand, an application is made using GUI in Matlab. The smallest risk value for single investment asset is obtained by Valbury Equity I stock mutual fund and the smallest risk value for two-asset portfolio is obtained by the combination assets of Pacific Equity Fund and Valbury Equity I. Meanwhile for three-asset portfolio, the combination assets of Pacific Equity Fund, Valbury Equity I, and Millenium Equity Prima Plus have the smallest risk value. The test result of VaR with Basel Rules shows that the usage of VaR is legitimate to measure loses potency in mutual fund investment.Keywords: Value at Risk (VaR), Historical Simulation, Mutual Fund, Risk.

PERBANDINGAN PENDEKATAN GENERALIZED EXTREME VALUE DAN GENERALIZED PARETO DISTRIBUTION UNTUK PERHITUNGAN VALUE AT RISK PADA PORTOFOLIO SAHAM Ayu Ambarsari; Sudarno Sudarno; Tarno Tarno
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Stock is one of investments that used by investor but often have high risk. So we need to calculate risk assessment for single stock and portfolios. Value at Risk (VaR) is a tool often used in measuring risk, especially in stock trading. Return stock usually has a fat tail distribution, there is usually a case of heteroscedasticity. Time series model that used to modeling this condition is Autoregressive Conditional Heteroscedasticity / Generalized Autoregressive Conditional Heteroscedasticity. This study focused on the calculation of VaR using Block Maxima with the approach Generalized Extreme Value/GEV and Peaks Over Threshold approach Generalized Pareto Distribution/GPD. Modeling volatility models of GARCH. Share data used in the case study is a daily closing PT. Astra International and Panin Financial period January 1st, 2010 – January 22nd, 2016. The result is ARIMA(0,1,1) GARCH(1,2) which is the best model with the smallest AIC. The amount of risk with a confidence level of 95% by GEV is 3,1613%, while the GPD is 3,2761% rupiah from current asset, in other words VaR GPD higher better than GEV.Keywords: Portfolio, Return, Value at Risk (VaR), ARCH/GARCH, Block Maxima, Peaks Over Threshold, GEV, GPD.

PEMODELAN RETURN PORTOFOLIO SAHAM MENGGUNAKAN METODE GARCH ASIMETRIS Muhammad Arifin; Tarno Tarno; Budi Warsito
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Investment in stocks is an alternative for investors and companies to obtain external funding sources. In the investment world there is a strong relationship between risk and return (profit), if the risk is high then return will also be high. Risks can be minimized by performing stock portfolio. Stock is the time series data in the financial sector, which usually has a tendency to fluctuate rapidly from time to time so that variance of error is not constant. Time series model in accordance with these condition is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). This research will apply asymmetric GARCH covering Exponential GARCH (EGARCH), Threshold GARCH (TGARCH), and Autoregressive Power ARCH (APARCH) in stock data Indocement Tunggal Tbk (INTP), Astra International Tbk (ASII), and Adaro Energy Tbk (ADRO) commencing from the date of March 1, 2013 until February 29, 2016 during an active day (Monday to Friday). The purpose of this research is to predict the value of the volatility of a portfolio of three assets stocks. The best models used for forecasting volatility in asset stocks which have asymmetric effect is ARIMA ([13],0,[2,3]) EGARCH (1,1) on a single asset data INTP, ARIMA ([2],0,[2,3]) EGARCH (1,1) on the 2 asset portfolio data ASII INTP, and ARIMA ([3],0,[2]) EGARCH (1,1) on the 3 asset portfolio data INTP-ASII-ADRO.Keywords: Stocks, Portfolio, Return, Volatility, Asymmetric GARCH.

Co-Authors Agus Rusgiyono Aisha Shaliha Mansoer Alan Prahutama Annisa Nur Fathia Aukhal Maula Fina Ayu Ambarsari Azizah Mulia Mawarni Budi Warsito Christa Monica Dhimas Bayususetyo Di Asih I Maruddani Dian Febriana Dody Apriliawan Dwi Ispriyanti Hasbi Yasin Khalida Hanum Khoirunnisa Nur Fadhilah Lenny Budiarti Lina Irawati Mufirda Mufirda MUHAMMAD ARIFIN Muhammad Yusnan Oktafiani Widya Ningrum Rita Rahmawati Rizky Adhi Nugroho Rizky Aditya Akbar Rukun Santoso Salsa Welyana Siti Maryam Sudarno Sudarno Sugito Sugito Suparti Suparti Titin Afriana Tri Murda Agus Raditya Triastuti Wuryandari Yuciana Wilandari Yudi Ari Wibowo

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