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All Journal PROSIDING SEMINAR NASIONAL Media Statistika SAINS DAN MATEMATIKA Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang E-Dimas: Jurnal Pengabdian kepada Masyarakat Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) EKO-REGIONAL JURNAL PENGEMBANGAN EKONOMI WILAYAH

Abdul Hoyyi

Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

Author-ID : 3131506

Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Social Sciences Other

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PENGUKURAN VALUE AT RISK MENGGUNAKAN PROSEDUR VOLATILITY UPDATING HULL AND WHITE BERDASARKAN EXPONENTIALLY WEIGHTED MOVING AVERAGE (EWMA) (Studi Kasus pada Portofolio Dua Saham) Putri, Nurissalma Alivia; Hoyyi, Abdul; Safitri, Diah
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Investment is an effort to get profits for individual or institution. But the investment policy is always faced with market risk as the effect of financial instruments movement such as stock price movements. Market risk measurement tool commonly used is Value at Risk (VaR), which measures the amount of loss at a certain confidence level. VaR measurement by Hull and White volatility updating procedure is a modification of the historical simulation involving information of volatility change calculated by Exponentially Weighted Moving Average (EWMA). This procedure is fit to financial data such as stock returns that are generally not normally distributed and are heteroskedastic. VaR calculation applied to the portfolio between Kalbe Farma Tbk (KLBF) stock and Lippo Karawaci Tbk (LPKR) stock from 3 January 2011 to 19 April 2013 were selected based on the largest trading volume at the end of the observation for LQ45 stocks listed in the Indonesia Stock Exchange (IDX) . The data used is the return calculated from the closing price of stocks. The validity of VaR was tested through a back test by Kupiec test, and concluded that the 95% VaR and 99% VaR are valid.

PERBANDINGAN MODEL ARCH/GARCH MODEL ARIMA DAN MODEL FUNGSI TRANSFER (Studi Kasus Indeks Harga Saham Gabngan dan Harga Minyak Mentah Dunia Tahun 2013 sampai 2015) Fakhriyana, Deby; Hoyyi, Abdul; Widiharih, Tatik
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Indonesian Composite Index is a value that used to measure the combined performance of shares listed in stock market. Price of crude oil is one of the factors that affect Indonesian Composite Index. If the prices of crude oil is increasing, it will be responsed by Indonesian goverment directly with also increasing the fuel prices, that will have an impact on Indonesian Composite Index. ARIMA and transfer function are methods of modeling time series data and it have assumption that the residual models have to be homogen. To overcome violations of those assumption, this study continue to modelling ARCH/GARCH with ARIMA and transfer function approach. The data used in this study are daily of Indonesian Composite Index and West Texas Intermediate (WTI) crude oil prices data from 2013 to 2015. This study gained two models, the first is ARIMA (1,1,[3]) which variance model of ARCH(1), it’s AIC value is equal to 7707,4287. The second is transfer fuction model (1,0,0) which noise model ARMA(0,[1,3) as well as variance model ARCH(1), it’s AIC value equal to 7689,18984. The best model is the one that has smallest AIC value. From this study can be concluded that the best of ARCH/GARCH model is ARCH/GARCH model with transfer function approach. Keywords : Indonesian Composite Index, crude oil prices, ARIMA, transfer function, ARCH/GARCH

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.

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

PEMBENTUKAN MODEL LOG LINIER EMPAT DIMENSI (Studi Kasus : Rata-rata Pengguna Jenis Bahan Bakar Minyak berdasarkan Jenis Kendaraan, Rasio Kompresi dan Kapasitas Mesin) Sari, Juli Sekar; Wilandari, Yuciana; Hoyyi, Abdul
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Based on the data from the Central Bureau of statistics, Indonesia's population is 237 million, an increase of 15.2% of the total population in 2000. With the increasing of the population from year to year, automatically the growth of vehicles will also experience increased. The impact of the increase in the number of motor vehicles is surely in the form of fuel consumption. Moreover, many factors will consider by the people to choose the type of fuel for their vehicle. Those factors included in the internal and external factors of the vehicle itself. At first, the internal factors in question are the type of vehicle, the compression ratio of the engine, and engine capacity. This research was conducted to find out the relationship between the internal factors with the log-linear Models. Log-linear Model was used to analyze the relationship between the variable responsesthat arewhich formed the contingency table. In this case, the researcher used log-linear Model of four dimensions with the step of analysis, as follows: outlining the possible model with diagram’s association, looking for the grade of frequency estimation of hope of any possible model, examining the Goodness of Fit of each model to find out the significant one, and determining the best model, in this case by looking at the smallest value of AIC. From the log-linear Model four dimensions is obtained the best model is the Model (WX, XY, XZ, YZ YZ) which means in case of this research there is a relationship between the type of fuel (W)*type of vehicle (X), the type of vehicle (X)*the compression ratio of the engine (Y), the type of vehicle (X)*engine capacity (Z), and the compression ratio of the engine (Y)*Engine Capacity(Z), with the value of AIC = -184. Keywords:, Log linear models four dimention, AIC

PEMODELAN KURS MATA UANG RUPIAH TERHADAP DOLLAR AMERIKA MENGGUNAKAN METODE GARCH ASIMETRIS Sulistyowati, Ulfah; Tarno, Tarno; Hoyyi, Abdul
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One factor causing to slowing economic growth in Indonesia is the currency exchange rate. In Indonesia,the exchange rate of the rupiah against the dollar is always become an attention of society. To monitor the movement needed a mathematical model that can be used to forecast the rupiah exchange rate to the dollar. Data rupiah exchange rate against the dollar is a financial time series data has a non-constant volatility. One model that is often used for the prediction of these data is ARIMA-GARCH. In this study discussed about modeling the data rate of the rupiah against the dollar using asymmetric GARCH, such as exponential GARCH (EGARCH), Threshold GARCH (TGARCH) and Autoregressive Power ARCH (APARCH). Modeling the exchange rate against the dollar using all three types of the Asymmetric GARCH models produce the best models, the ARIMA ([4.5], 1, [4,5]) - APARCH (2,1). With the results obtained using the model for volatility forecasting that volatility decreased from the previous forecast but still be at its high volatility.Keywords : Exchange rate, ARIMA, GARCH, Asymmetric GARCH, volatilty

PREDIKSI JUMLAH PENUMPANG KERETA API MENGGUNAKAN MODEL VARIASI KALENDER DENGAN DETEKSI OUTLIER (Studi Kasus : PT. Kereta Api Indonesia DAOP IV Semarang) Saputri, Ani Funtika; Hoyyi, Abdul; Sugito, Sugito
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Transportation is an inseparable and indispensable part of society in everyday life. Trains became one of the most popular public transportation, especially during the Eid. The shifting of the lunar month of Eid forms a pattern called calendar variation. The calendar variation model is a model that combines the dummy regression model with the ARIMA model. In time series models sometimes there are outliers that can affect the suitability of the model. So that modeling and forecasting method is done using model of calendar variation with outlier detection. Based on the analysis that has been done on the data of the number of passengers of Argo Bromo Anggrek railway, we get the ARIMA model ([11], 0, 1), Dt, Dt-2,t with the addition of 4 outliers as the best model and the resulted forecasting shows increase Railway passengers increase in the months leading up to Eid. Keywords: Train, Calendar Variations, Outlier Detection

METODE GENERALIZED MEAN DISTANCE-BASED K-NEAREST NEIGHBOR CLASSIFIER (GMDKNN) UNTUK ANALISIS CREDIT SCORING CALON DEBITUR KREDIT TANPA AGUNAN (KTA) Saraswati, Mei Sita; Mukid, Moch. Abdul; Hoyyi, Abdul
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Unsecured Credit is one of the credit facilities provided by banks, where the prospective debtor can borrow some amount of fund from the bank without having to provide collateral. Credit scoring is a process that aims to assess the worthiness of credit applications and classify the credit applicants into prospective debtors whose the credit application is worthy to be accepted and prospective debtors whose the credit application should be rejected. One of the statistical methods that can be applied in examining the analysis of credit scoring is the Generalized Mean Distance-Based k-Nearest Neighbor (GMDKNN) classifier. Empirical study on this method uses 23,337 data of prospective debtor of unsecured credit in 2018, with the dependent variable being the credit scoring final decision and seven independent variables, i.e. age, child dependent, length of employment, age of the company, income, loan proposed, and duration of credit. Based on the feature selection test, all independent variables are significantly taking effect on the credit scoring final decision. The best classification model is obtained in the parameters k = 137 and p = -1 with the classification performance metrics represented by the values of APER = 0,2580, accuracy = 74,20%, sensitivity = 0,6083, specificity = 0,8393, AUC = 0,7238, and G-Mean = 0,7146.Keywords: Unsecured Credit, credit scoring, classification, Generalized Mean Distance-Based k-Nearest Neighbor (GMDKNN).

ANALISIS FAKTOR – FAKTOR YANG MEMPENGARUHI JUMLAH KEJAHATAN PENCURIAN KENDARAAN BERMOTOR (CURANMOR) MENGGUNAKAN MODEL GEOGRAPHICALLY WEIGHTED POISSON REGRESSION (GWPR) Haris, Muhammad; Yasin, Hasbi; Hoyyi, Abdul
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Theft is an act taking someone else’s property, partially or entierely, with intention to have it illegally. Motor vehicle theft is one of the most highlighted crime type and disturbing the communities. Regression analysis is a statistical analysis for modeling the relationships between response variable and predictor variable. If the response variable follows a Poisson distribution or categorized as a count data, so the regression model used is Poisson regression. Geographically Weighted Poisson Regression (GWPR) is a local form of Poisson regression where data sampling location is prioritized. GWPR model is used for identifying the factors that influence the numbers of motor vehicles theft, either using a weighted gauss kernel function or bisquare kernel function. Based on the value of Akaike Information Criterion (AIC) of Poisson regression and GWPR model, it is analyzed that GWPR model using a weighted fixed bisquare kernel function is the best model for analyzing the number of motor vehicles theft at every Sub-Districts in the Semarang city in 2012, because it has the smallest AIC value. This model has a precision of 88,81%.Keywords: Motor Vehicle Theft, Geographically Weighted Poisson Regression, Kernel Gauss Function, Kernel Bisquare Function, Akaike Information Criterion

REGRESI KOMPONEN UTAMA ROBUST S-ESTIMATOR UNTUK ANALISIS PENGARUH JUMLAH PENGANGGURAN DI JAWA TENGAH Jeffri Nelwin J. O. Siburian; Rita Rahmawati; Abdul Hoyyi
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Robust principal component regression s-estimator is principal component regression that applies robust approach method at principal component analysis and s-estimator at principal component regression analysis. The aim of robust principal component regression s-estimator is to overcome multicollinearity problems in multiple linier regression Ordinary Least Square (OLS) and to overcome outlier problems in principal component regression so get the most effective model. Minimum Volume Ellipsoid (MVE) is one of the robust approach methods that applied when doing principal component analysis and S-Estimator is one of the estimation methods that applied when doing principal component regression analysis. The case in this study is the factors that influence the Number of Unemployment in Central Java in 2017. The model that provides the most effective result to handling multicolliniearity and ouliers in the case study Number of Unemployment in Central Java in 2017 is using robust principal component regression MVE-(S-Estimator) with Adjusted R2 value of 0.9615 and RSE value of 0.4073. Keywords: Robust Principal Component Regression S-Estimator, Multicollinearity, Outliers, Minimum Volume Ellipsoid (MVE), Number of Unemployment.

Co-Authors Abdurakhman Abdurakhman Afifah Alrizqi Agus Rusgiyono Agus Somantri Ahmat Dhani Riau Bahtiyar Alan Prahutama Alan Prahutama Alifah Zahlevi Allima Stefiana Insani Alvi Waldira Alwi Assegaf Amelia Crystine Anggit Ratnakusuma Anggita, Esta Dewi Anik Nurul Aini Annisa Intan Mayasari ANNISA RAHMAWATI Ari Fakhrus Sanny Arief Rachman Hakim Arya Huda Arrasyid Aulia Desy Deria Avia Enggar Tyasti Bella Cynthia Devi Besya Salsabilla Azani Arif Bisri Merluarini Bitoria Rosa Niashinta Budi Warsito Budi Warsito Candra Silvia Chyntia Arum Widyastusti Cindy Wahyu Elvitra Darwanto Darwanto Dea Manuella Widodo Deby Fakhriyana, Deby Dede Zumrohtuliyosi Deden Aditya Nanda, Deden Aditya Dedi Rosadi Dermawanti Dermawanti Desriwendi Desriwendi Dewi Erliana Dewi Setya Kusumawardani Dhea Kurnia Mubyarjati Di Asih I Maruddani Di Asih I Maruddani Di Asih I Maruddani Diah Safitri Diah Safitri Diah Wulandari Dilla Retno Deswita Dwi Ispriyanti DWI RAHMAWATI Emyria Natalia br Sembiring Endah Cahyaningrum Erna Musri Arlita Esti Pratiwi Faiqotul Himmah Fiki Farkhati Firda Dinny Islami Fitra Ramdhani Gayuh Kresnawati Hasbi Yasin Hasbi Yasin Henny Setyowati Herwindhito Dwi Putranto Ikha Rizky Ramadani Indri Puspitasari Irfan Afifi Isowedha Widya Dewi Issabella Marsasella Christy Jeffri Nelwin J. O. Siburian Juli Sekar Sari, Juli Sekar Kartikaningtiyas Hanunggraheni Saputri Khotimatus Sholihah Khusnul Umi Fatimah Kiki Febri Azriati Koko Arie Bowo Kristika Safitri Kumo Ratih Leni Pamularsih Maidiah Dwi Naruri Saida Malik Hakam Mega Fitria Andriyani Mega Fitria Andriyani Mia Anastasia Sinulingga Moch. Abdul Hoyyi Moch. Abdul Mukid Moch. Abdul Mukid MUHAMMAD HARIS Mustafid Mustafid Mustafid Mustafid Mutiara Ardin Rifkiani Nadya Kiki Aulia Nandang Fahmi Jalaludin Malik Novika Pratnyaningrum Nurissalma Alivia Putri Nurul Fauziah Ovie Auliya’atul Faizah Priska Rialita Hardani Purina Pakurnia Artiguna Rita Rachmawati Rita Rahmawati Rita Rahmawati Rizki Pradipto Widyantomo Rizky Oky Ari Satrio Rukun Santoso Saputri, Ani Funtika Saraswati, Mei Sita Shaumal Luqman Silvia Nur Rinjani SITI NURLATIFAH Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Sugito Sugito Suparti Suparti Suparti Suparti Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Tatik Widiharih Titis Nur Utami Tresno Sayekti Nuryanto Triastuti Wuryandari Triastuti Wuryandari Trisnawati Gusnawita Berutu Ubudia Hiliaily Chairunnnisa Ulfah Sulistyowati Yosi Dhyas Monica Yuciana Wilandari Yuciana Wilandari Yudia Yustine Yunisa Ratna Resti Yustian Dwi Saputra

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