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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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ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI GIZI BURUK BALITA DI JAWA TENGAH DENGAN METODE SPATIAL DURBIN MODEL Ikha Rizky Ramadani; Rita Rahmawati; Abdul Hoyyi
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Severe malnutrition is a state of nutritional deficiencies at a severe level, where the nutritional status is far below the standard. Anyone can suffer from severe malnutrition, especially infants and children who are in the growth period. Central Java Province is one of many provinces in Indonesia where the cases of severe malnourished children under five years are high enough. It is noted that Central Java Province is one of 10 provinces in Indonesia with the highest rate of severe malnutrition cases for 6 years (2005-2010). Using data from year 2011, the result of the Moran’s I test states that there are spatial dependencies on severe malnutrition’s rate of children under five years and some of its influential factors on Central Java Province. Therefore, Spatial Durbin Model (SDM) method is used in this experiment. Variables which significantly affect severe malnutrition on Central Java Province through SDM method are : the numbers of infants with low birth weight ( ), the numbers of houses with good health status ( ), and the numbers of households with access to source of clean water ( ). SDM model obtains value of as much as 70.3% with AIC and MSE respectively 476.32 and 35280.11, results better than Ordinary Least Square (OLS) which produce as much as 41.5% with AIC 490.52 and MSE 60653.693

PEMBENTUKAN PORTOFOLIO OPTIMAL DENGAN METODE RESAMPLED EFFICIENT FRONTIER UNTUK PERHITUNGAN VALUE AT RISK DILENGKAPI APLIKASI GUI MATLAB Henny Setyowati; Abdul Hoyyi; Di Asih I Maruddani
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The purpose of investors in investing is to get a return, but investors also have to bear the risks that might exist. There are 3 types of investors in investment based on their preference for risk, namely risk aversion (risk averter), moderate risk takers (risk moderate), and high risk takers (risk takers). To obtain an optimal portfolio for each type of investor, the Resampled Efficient Frontier Method is used with Monte Carlo Simulation as much as 700 times, to obtain more parameter estimates. The results of the Resampled Efficient Frontier from Efficient Frontier will take 51 efficient points to determine the optimal portfolio for each type of investor. The efficient point taken is the 1st, 26th and 51st efficient points for the investor risk averter type, risk moderate, and risk taker. To determine the estimated loss in investment, the VaR value is calculated based on the monthly return data of BBNI, UNTR, INKP, and KLBF shares for the period February 2013 to March 2017, with a capital allocation of Rp 100,000,000.00, a holding period of 20 days, and a level of trust of 95%. The Matlab GUI is used to facilitate users in processing data.Keywords: Efficient Frontier, Monte-Carlo Simulation, Normal Distribution, VaR, Matlab GUI

OPTIMALISASI PORTOFOLIO SAHAM MENGGUNAKAN METODE MEAN ABSOLUTE DEVIATION DAN SINGLE INDEX MODEL PADA SAHAM INDEKS LQ-45 Diah Wulandari; Dwi Ispriyanti; Abdul Hoyyi
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Stock investment is the planting of money in a securities that indicates the ownership of a company in order to provide benefits in the future. In obtaining optimal results from stock investments, investors are expected to create a series of portfolios. The portfolio will help investors in allocating some funds in different types of investments in order to achieve optimal profitability. For selection of optimal stocks representing LQ-45 Index, used 2 methods of Mean Absolute Deviation (MAD) method and Single Index Model (SIM) method. In MAD method, 5 best stocks are BBCA with weight 23%, INDF 8%, KLBF 23%, TLKM 23%, and UNVR 23%. While the SIM method of candidate portfolio obtained is AKRA with weight 15,459%, BBCA 48,193%, BBNI 5,028%,KLBF 0,258% and TLKM 31,062%. Portfolio performance meter is used by sharpe ratio. The value of sharpe ratio is 0,36754 for optimal portfolio using MAD method and 0,40782 for optimal portfolio using SIM method, this means that optimal portfolio using SIM method has better performance than MAD. Keywords: Investment, Portfolio, Index LQ-45, Mean Absolute Deviation, Single Index Model, Sharpe Ratio

PEMODELAN RETURN INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN THRESHOLD GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (TGARCH) Maidiah Dwi Naruri Saida; Sudarno Sudarno; Abdul Hoyyi
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

ARIMA model is one of modeling method that can be applied on time series data. It assumes that the variance of residual is constant. Time series data, particularly the return of composite stock price index, tend to change rapidly from time to time and also fluctuating, which cause heteroscedasticity where the variance of residual is not constant. Autoregressive Conditional Heteroscedasticity (ARCH) or Generalized Autoregressive Conditional Heteroscedasticity (GARCH) can be used to construct model of financial data with heteroscedasticity. Besides of having inconsistent variance, financial data usually shows phenomenon where the difference of the effect between positive error value and negative error value towards data volatility, called asymmetric effect. Therefore, one of the GARCH asymmetric models, Threshold Generalized Autoregressive Conditional Heteroscedasticity (TGARCH) is used in this research to solve heteroscedasticity and asymmetric effect in stock price index return. The data in this research is stock price index return from January 2nd, 2013 until October 30th, 2015. From the analysis, TGARCH models are obtained. ARIMA([3],0,[26])-TGARCH(1,1) is the best model because it has the smallest AIC value compared to other models. It produces the forecast value of stock price index return nearly the same with actual return value on the same day. Keywords: Return, Heteroscedasticity, Asimmetry effect, ARCH/GARCH, TGARCH.

PENENTUAN BOBOT PORTOFOLIO OPTIMAL DENGAN METODE RESAMPLED EFFICIENT FRONTIER UNTUK PERHITUNGAN VALUE AT RISK PADA DATA BERDISTRIBUSI NORMAL Esti Pratiwi; Abdul Hoyyi; Sugito Sugito
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The investors have a goal of getting return when they invest their wealth, but on the other hand they should bear the risk that might arise from their investment. There are three categories of investors based on their preferences toward risk that is risk averter, moderate risk and risk taker. To establish a portfolio that is able to incorporate investor preferences is used Resampled Efficient Frontier Method. Resampled Efficient Frontier Method is a development of the Mean Variance Efficient Portfolios Method, which used Monte Carlo simulation to obtain more estimated of parameter inputs. Based on the efficient portfolios of Resampled Efficient Frontier along the efficient frontier with 51 efficient points, taken optimal portfolio for each investor type. Optimal portfolio for risk averter, moderate risk and risk taker respectively is an efficient portfolio on the first point, 26th point, and 51st point. To describe the loss of the optimal portfolio is used Value at Risk. VaR is calculated based on monthly return from BBCA, LPKR, PGAS and SMGR during January 2008 until December 2013. Estimated VaR on 95% confidence level during 20 days holding period and the amount of investment allocation Rp 100,000,000.00 from the optimal portfolio for risk averter, moderate risk and risk taker respectively is Rp 50,706,000.00, Rp 54,618,000.00 and Rp 64,522,000.00

ALGORITMA ITERATIVE DICHOTOMISER 3 (ID3) UNTUK MENGIDENTIFIKASI DATA REKAM MEDIS (Studi Kasus Penyakit Diabetes Mellitus Di Balai Kesehatan Kementerian Perindustrian, Jakarta) Avia Enggar Tyasti; Dwi Ispriyanti; Abdul Hoyyi
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Iterative Dichotomiser 3 (ID3) Algorithm is a basic decision tree learning algorithm. These algorithms perform a thorough search (greedy) in all possible decision tree. ID3 algorithm can be implemented using a recursive function, (function that calls itself). One of the problems that can be solved using the ID3 algorithm is a classification of diabetic patients. Diabetic is a disease because of the body is not able to control the amount of sugar or glucose in the bloodstream. Classification using ID3 in the case of diabetics produce trees with many vertices to 32 knot where 21 of them is a leaf node and attribute two-hour postprandial glucose fasting elected as the root node in the decision-making tree. Based on the classification performance measurements show that the classification accuracy or measurement accuracy reaches 89,75%. While the measurement accuracy of the classification algorithm ID3 using test samples totaling 84 samples showed an accuracy of 72,619%. Keywords: ID3 Algortihm, Decision Tree, DiabetesALGORITMA ITERATIVE DICHOTOMISER 3 (ID3) UNTUK MENGIDENTIFIKASI DATA REKAM MEDIS(Studi Kasus Penyakit Diabetes Mellitus Di Balai Kesehatan Kementerian Perindustrian, Jakarta)

PERAMALAN INDEKS HARGA SAHAM GABUNGAN DENGAN METODE LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR) Gayuh Kresnawati; Budi Warsito; Abdul Hoyyi
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Smooth Transition Autoregressive (STAR) Model is one of time series model used in case of data that has nonlinear tendency. STAR is an expansion of Autoregressive (AR) Model and can be used if the nonlinear test is accepted. If the transition function G(st,γ,c) is logistic, the method used is Logistic Smooth Transition Autoregressive (LSTAR). Weekly IHSG data in period of 3 January 2010 until 24 December 2017 has nonlinier tend and logistic transition function so it can be modeled with LSTAR . The result of this research with significance level of 5% is the LSTAR(1,1) model. The forecast of IHSG data for the next 15 period has Mean Absolute Percentage Error (MAPE) 2,932612%. Keywords : autoregressive, LSTAR, nonlinier, time series

PEMODELAN KECEPATAN ANGIN DI KOTA SEMARANG MENGGUNAKAN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) Alifah Zahlevi; Alan Prahutama; Abdul Hoyyi
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Semarang city is the one of the strategic areas located in the middle of the north coast of Java that has a tropical climate with the high humidity and temperature, so it often causes a high rainfall and strong wind. So that is way Semarang city is ever sustained the extreme weather like a Tropical Storm. Since January 2016 until 2017 there are 34 cases of Tornado and 24 incidents of fallen trees because of the gale. For helping the people to be allert the effect of the strong winds can be done by predicting the average of wind velocity by using Adaptive Neuro-Fuzzy Inference System (ANFIS) method which can predict the climate change that do not require the assumption of white noise and normal residual distribution. In addition ANFIS is a group of neural network with input that has been fuzzied on the first or second layer, but the weight of the artificial neural is not fuzzied. The identification result of stationaries obtained the plot of PACF on the first and second lag, with the result that these lag which will be a input variable on ANFIS model. The result of ANFIS by using cluster FCM, the third total membership show the smallest percentage of RMSE in-sample is 0,0048 on the first lag, and the smallest percentage of RMSE out-sample is 0,008 on the ANFIS model with the input lag 1 and three cluster. Keywords : the average of wind velocity, ANFIS, RMSE

IDENTIFIKASI VARIABEL YANG MEMPENGARUHI BESAR PINJAMAN DENGAN METODE POHON REGRESI (Studi Kasus di Unit Pengelola Kegiatan PNPM Mandiri) Shaumal Luqman; Moch. Abdul Mukid; Abdul Hoyyi
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Most people need a loan to fullfil their daily needs, such as a loan of goods or money. Loan can be obtained from financial institutions or individuals. In order to the loan granted by a financial institutions is not wrong target, financial institutions usually apply precaution principle. In making decisions related to how much a decent loan granted to a customer, the financial institutions often use the help of statistical methods. One methods often used is the Classification and Regression Trees (CART). Classification and Regression Trees (CART) is a nonparametric method that can be used to identify the variable that affect the amount of the loan at a financial institutions and estimate how much worth of loans granted. Because of the loan is a continous variable so the form of the tree is a Regression Tree. In this thesis, the financial institutions is UPK PNPM Mandiri Mekar Sejati in Kecamatan Bawang Kabupaten Batang. Variables that may be affected for large loans are age, occupation, type of warranty, the number family members, and the average income per month. The analysis showed that the variables that most influence on the income of the loans. Mean Absolute Percentage Error (MAPE) value from this method is 36%.Keyword : Regression tree, CART, Large loans.

PENENTUAN VALUE AT RISK SAHAM KIMIA FARMA PUSAT MELALUI PENDEKATAN DISTRIBUSI PARETO TERAMPAT Dede Zumrohtuliyosi; Abdul Hoyyi; Agus Rusgiyono
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Each investment object being traded in the stock market will get return that it has risk potential. Return and risk has mutual correlation that equilibrium. If the risk is high, then it obtains high return and vice versa. Risk management is the desain and implementation procedure for controlling risk. Value at Risk (VaR) is instrument to analyze risk management. Financial time series data for return data is assumed that it has heavy tail distribution and heteroscedasticity case (volatility clustering). Time series model that used to modelling this condition are Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregresive Conditional Heteroscedasticity (GARCH), while Value at Risk calculation is used Generalized Pareto Distribution (GPD) approach. This research uses return data from stock closing prices of Kimia Farma Pusat period October 2009 until September 2014. The best ARCH-GARCH model is ARIMA(0,1,1) GARCH(1,2) model because the parameters are significant and it has the smallest AIC value. Risk calculation that is gotten with GPD approach if invest in Kimia Farma Pusat with interval confidence 95% is 13.6928% rupiah from current asset. Keywords: Stock, Risk, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Generalized Pareto Distribution (GPD), Value at Risk (VaR)

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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