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All Journal Jurnal Matematika International Journal of Science and Engineering (IJSE) Media Statistika SAINS DAN MATEMATIKA JURNAL SISTEM INFORMASI BISNIS Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Science and Technology E-Dimas: Jurnal Pengabdian kepada Masyarakat Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jurnal Riset Teknologi Pencegahan Pencemaran Industri STATISTIKA

Hasbi Yasin

Department Of Statistics, Faculty Of Sciences And Mathematics, Diponegoro University

Author-ID : 422426

Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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APLIKASI METODE GOLDEN SECTION UNTUK OPTIMASI PARAMETER PADA METODE EXPONENTIAL SMOOTHING Mahkya, Dani Al; Yasin, Hasbi; Mukid, Moch. Abdul
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Forecasting is predicting the activities values that have been previously known. One of the methods that can be used to predict is Exponential Smoothing. In this study, exponential smoothing method used is Single Exponential Smoothing (SES), Holt Double Exponential Smoothing (DES) and Triple Exponential Smoothing Holt-Winter (TES) Additive and Multiplicative models. Data used is value of Central Java Export from the period January 2006 until December 2013. There is some weighting parameters were evaluated in this method in order to produce a minimum error. Trial error method is used to obtain the weighting parameters. For SES method parameters evaluated were the parameters α, in DES method there are α and γ. And TES method there are α, γ and β. The value that will be minimize is Persentage Mean Absolute Error (MAPE). This study used the Golden Section method to find the parameter values that minimize the weighting function of MAPE. And built a Graphical User Interface (GUI) MATLAB in order to facilitate the analysis process. The Golden Section analysis found the best model is the TES Holt Winters Additive because it has a minimum value of MAPE. With Use the TES Holt Winters Additive will continue to predict the value of exports of Central Java 12 periods ahead with weighting parameters that minimize MAPE. Keywords : Exponential Smoothing, Graphical User Interface (GUI), Export, Golden Section, Predict

PEMODELAN MARKOV SWITCHING AUTOREGRESSIVE Ariyani, Fiqria Devi; Warsito, Budi; Yasin, Hasbi
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Transition from depreciation to appreciation of exchange rate is one of regime switching that ignored by classic time series model, such as ARIMA, ARCH, or GARCH. Therefore, economic variables are modeled by Markov Switching Autoregressive (MSAR) which consider the regime switching. MLE is not applicable to parameters estimation because regime is an unobservable variable. So that filtering and smoothing process are applied to see the regime probabilities of observation. Using this model, transition probabilities and duration of the regime can be informed. In this case conducted exchange rate of Rupiah to US Dollar modeling with MSAR. The best model is MS(2)-AR(1) with transition probabilities from depreciation to appreciation is 0,052494 and appreciation to depreciation is 0,746716. Duration of the depreciation state is 19,04986 days and appreciation state is 1,339198 days.

PEMODELAN PERSENTASE PENDUDUK MISKIN DI KABUPATEN DAN KOTA DI JAWA TENGAH DENGAN PENDEKATAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION Hakim, Arief Rachman; Yasin, Hasbi; Suparti, Suparti
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Regression analysis is a statistical analysis that models the relationship between the response variable and the predictor variable. Geographically Weighted Regression (GWR) is the development of linear regression with the added factor of the geographical location where the response variable is taken, so that the resulting parameters will be local. Mixed Geographically Weighted Regression (MGWR) has a basic concept that is a combination of a linear regression model and GWR, by modeling variables that are local and which are global variables. Methods for estimating the model parameters MGWR no different from the GWR using Weighted Least Square (WLS). Selection of the optimum bandwidth using the Cross Validation (CV). Application models MGWR the percentage of poor people in the district and town in Central Java showed MGWR models that different significantly from the global regression model. As well as models generated for each area will be different from each other. Based on the Akaike Information Criterion (AIC) between the global regression model, the GWR and MGWR models, it is known that MGWR models with Gaussian kernel weighting function is the best model is used to analyze the percentage of poor in the counties and cities in Central Java because it has the smallest AIC value.Keywords: Akaike Information Criterion, Cross Validation, Kernel Gaussian function, Mixed Geographically Weighted Regression, Weighted Least Square.

PENENTUAN FAKTOR PRIORITAS MAHASISWA DALAM MEMILIH TELEPON SELULER MERK BLACKBERRY DENGAN FUZZY AHP Shega, Hanien Nia H; Rahmawati, Rita; Yasin, Hasbi
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

This study aims to determine the priority factor Diponegoro University students in choosing a BlackBerry mobile phone brands. Consumer or buyer is often confused in making the decision to buy a product because of the many factors that affect the choices available. From the method of Analytic Hierarchy Process (AHP) was found to be too subjective assessment of uncertainty for qualitative data. The problems above can be solved by the method of Fuzzy Analytic Hierarchy Process (FAHP), which uses the interval so that the assessment of qualitative data can provide a more objective assessment. The criteria used to be in this research are quality, price, design, and service. The data were taken by spreading questionnaires. From the answer of respondent, calculation of ratio was performed with a consistency ratio (CR). If CR<0.10 it means the answer of respondent is consisten and can be used for Fuzzy AHP. Based on the result of research, it could be concluded that quality was the top priority with 0.278 priority weights, then the service with 0.254 priority weights, design with 0.240 priority weight, and price with 0.228 priority weights.

PERAMALAN VOLATILITAS MENGGUNAKAN MODEL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY IN MEAN (GARCH-M) (Studi Kasus pada Return Harga Saham PT. Wijaya Karya) Ratnasari, Dwi Hasti; Tarno, Tarno; Yasin, Hasbi
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Stock return volatility in the markets of developing countries (emerging markets) is generally much higher than the markets of developed countries. High volatility illustrates the level of high risk faced by investors due to reflect fluctuations in stock price movement. Therefore, it is probable, stock investments that are carried in Indonesia have a high risk opportunity. Important properties are often owned by time series data in the financial sector in particular to return data that the probability distribution of returns is fat tails and volatility clustering or often referred to as a case of heteroscedasticity.Time series models that can be used to model this condition are ARCH and GARCH. One form of ARCH/GARCH is Generalized Autoregressive Conditional Heteroscedasticity In Mean (GARCH-M). The purpose of this study is to predict volatility by using GARCH-M model in the return data analysis of daily stock price closing of Wijaya Karya (Persero) Tbk from October 18, 2012 until March 14, 2014 by using the active days (Monday to Friday). The best model is used for forecasting the volatility case in the stock price return of PT. Wijaya Karya is ARIMA (0,0, [35]) GARCH (1,1)-M. Keywords: Stocks, Volatility, Generalized Autoregressive Conditional Heteroscedasticity in Mean (GARCH-M)

SIMULASI PENGUKURAN KETEPATAN MODEL VARIOGRAM PADA METODE ORDINARY KRIGING DENGAN TEKNIK JACKKNIFE Kusumawardani, Dewi Setya; Sudarno, Sudarno; Yasin, Hasbi
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Kriging adalah metode yang digunakan untuk mengestimasi besarnya nilai yang mewakili suatu titik yang tidak tersampel berdasarkan titik-titik tersampel yang berada disekitarnya. Pada Ordinary Kriging pendugaan suatu nilai variabel pada titik tertentu dilakukan dengan cara mengamati data yang sejenis pada daerah lain, pada setiap titik yang tidak diketahui nilainya, maka akan diestimasi dengan menggunakan kombinasi linier terboboti (weighted linier combination). Data yang dibangkitkan adalah data kandungan besi (%). Data tersebut merupakan data random hasil simulasi berdasarkan model variogram Spherical dan Eksponensial. Nilai dugaan diperoleh melalui sistem Ordinary Kriging dengan menggunakan teknik Jackknife. Ketepatan model variogram spherical dan eksponensial dihitung berdasarkan nilai tengah kesalahan persentase absolut (Mean Absolut Percentage Error). Berdasarkan hasil perhitungan untuk variogram spherical persentase kesalahan yang diperoleh yaitu 0,0417%, sedangkan persentase kesalahan untuk model variogram eksponensial yaitu 0,0776%. Kedua nilai MAPE tersebut berada dibawah 10%, dengan demikian dapat disimpulkan bahwa teknik jackknife dapat digunakan untuk menentukan nilai dugaan dari sistem ordinary kriging dari model variogram spherical dan eksponensial.

OPTIMASI VALUE AT RISK RETURN ASET TUNGGAL DAN PORTOFOLIO MENGGUNAKAN SIMULASI MONTE CARLO DILENGKAPI GUI MATLAB Astuti, Nur Indah Yuli; Tarno, Tarno; Yasin, Hasbi
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Value at Risk (VaR) is a scale that can measure the maximum loss that may happen for a specified period of time in the normal market conditions at a certain level of confidence. The most important thing in the VaR is to determine the type of methodology and assuming appropriate with the distribution of the return. One of the methods in calculating the VaR is Monte Carlo simulation. VaR with Monte Carlo simulation method assumes that the return value is normal distribution simulated using the appropriate parameters and portfolio return is linier towards its single asset return. From the results and analysis research conducted use GUI Matlab, VaR single asset of value risk on the stock of United Tractors Tbk (UNTR) is greater than Bank Rakyat Indonesia (Persero) Tbk (BBRI), Astra International Tbk (ASII), and Bank Negara Indonesia Tbk ( BBNI), VaR value of portfolio consisting of two assets, the three assets, and four assets have lower value than the sum of its single asset of the value of VaR. Matlab (Matrix Laboratory) is an interactive programming system with the basic elements of array database which dimensions do not need to be stated in particular, while the GUI is the submenu of Matlab. In this research, determining the level of trust and specified time period is very important to count of VaR value because it can describe how much investors bear the risk. Keywords: Value at Risk, time period, confidence level, Monte Carlo simulation

ANALISIS RISIKO INVESTASI SAHAM TUNGGAL SYARIAH DENGAN VALUE AT RISK (VAR) DAN EXPECTED SHORTFALL (ES) Saepudin, Yunus; Yasin, Hasbi; Santoso, Rukun
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One measure that can be used to estimate risk is Value at Risk (VaR). Although VaR is very popular, it has several weakness that VaR not coherent causes the lack of sub-additive. To overcome the weakness in VaR, an alternative risk measure called Expected Shortfall (ES) can be used. The porpose of this research objective are to estimate risk by ES and by using VaR with Monte Carlo simulation. The data we used are the closing price of Unilever Indonesia stocks that consistently get into Jakarta Islamic Index (JII). To make VaR become easier for people to understand, an application is made using GUI in Matlab. The Expected Shortfall results from the calculation using 99% confidence level that may be experienced is at 0.039415 show that the risk exceed the VaR it is at 0.034245. For 95% confidence level that may be experienced is at 0.030608 show that the risk exceed the VaR it is at 0.024471. For 90% confidence level that may be experienced is at 0.026110 show that the risk exceed the VaR it is at 0.019172. Show that the greater the level of confidence that is used the greater the risk will be borne by the investor.Keywords: Risk, Value at Risk (VaR), JII, Expected Shortfall (ES).

PENERAPAN METODE EXPONENTIALLY WEIGHTED MOVING AVERAGE (EWMA) DALAM PENGUKURAN RISIKO INEVSTASI SAHAM PORTOFOLIO UNTUK VOLATILITAS HETEROGEN Wulandari, Heni Dwi; Mustafid, Mustafid; Yasin, Hasbi
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Risk measurement is important in making an investment. One tool used in the measurement of investment risk is Value at Risk (VaR). VaR represents the greatest possible loss of investment with a given period and level of confidence. In the calculation of Value at Risk requires the assumption of normality and homogeneity. However, financial data rarely satisfies that assumption. Exponentially Weighted Moving Average is one method that can be used to overcome the existence of a heterogeneous variant. Daily volatility is calculated using the EWMA method by taking a decay factor of 0.94. VaR portfolio of ASII, BBNI and PTBA stocks is calculated using historical simulation method from the revised portfolio return with Hull and White volatility updating procedure. VaR values obtained are valid at a 99% confidence level based on the validity test of Kupiec PF and Basel rules. Keywords: Value at Risk (VaR), Portfolio, EWMA, Historical Simulation, Volatility Updating

ANALISIS KONJOIN FULL PROFILE DALAM PEMILIHAN BEDAK UNTUK MAHASISWI DEPARTEMEN STATISTIKA UNIVERSITAS DIPONEGORO Julianisa, Rose Debora; Safitri, Diah; Yasin, Hasbi
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Powder is the one of cosmetic product that serves to cover the shortfall on the face. Powder consumption continues to increase from year to year to follow trend of cosmetic and lifestyle that happened to people. It makes producer to be more creative and innovative to produce or developing their product to keep consumers interested. To help producer to know and understand the consumer preference on combinations of attributes in the powder, it can be used conjoint analysis. Beside that, conjoint analysis is used to get the concept of products that comply with the consumers want and can be developed as a combination of new products. In this thesis conjoint analysis is used by using presentation method of full-profile. There are four attributes used in this analysis, they are powder types, form of packaging, aroma, and glass facility. From the results of the analysis that obtained by the respondents, the most importance attribute in selecting a face powder is the package attribute (34,338 %), the second is a kind of powder (33,667 %), the third is glass facility in the powder (16,397 %), and the last is the scent of powder (15,598 %). The combination of desired respondents in choosing or use a powder is a powder that have the type of compact powder, circular packaging forms, has no aroma, and there is no glass. Keywords : powder, consumer’s preference, conjoint analysis, full-profile

Co-Authors Abdul Hoyyi Achmad Choiruddin Adi Waridi Basyiruddin Adi Waridi Basyirudin Arifin Agus Rusgiyono Ajeng Arum Sari Alan Prahutama Alvita Rachma Devi Amanda Lucky Berlian Andreanto Andreanto Anggun Perdana Aji Pangesti Arief Rachman Hakim Arief Rachman Hakim Arumningtyas, Felinda Baluk, Andreas Pedo Bens Pardamean Budi Warsito Budi Warsito Danang Chandra Pradana, Danang Chandra Dani Al Mahkya Darwanto Darwanto Devi Wijayanti Dewi Setya Kusumawardani Dharmawan, Bagus Dwiky Dhea Kurnia Mubyarjati Di Asih I Maruddani Di Asih I Maruddani Di Asih I Maruddani Diah Safitri Dwi Hasti Ratnasari Dwi Ispriyanti Eko Siswanto Fadhilla Atansa Tamardina Fiqria Devi Ariyani Gera Rozalia Hanien Nia H Shega Hari Susanta Nugraha Hendrian, Jody Hidayatul Musyarofah Hindun Habibatul Mubaroroh Ika Chandra Nurhayati Inas Hasimah Inayati, Syarifah Indah Suryani Innosensia Adella Intan Monica Hanmastiana Isna Wulandari Ispriyansti, Dwi Johanes Roisa Prabowo Kadi Mey Ismail Kurniawan, Isma Dwi Lutfia Septiningrum Maghfiroh Hadadiah Mukrom Maria Odelia Mas'ad, Mas'ad Maulana Taufan Permana Mega Fitria Andriyani Meilia Kusumawardani, Meilia Moch. Abdul Mukid Mochammad Iffan Zulfiandri MUHAMMAD HARIS Muhammad Mujahid Muhammad Tahmid Muryanto Muryanto Muryanto, Muryanto Mustafid Mustafid Mutiara, Dinar Nova Delvia Nur Azizah Nur Indah Yuli Astuti, Nur Indah Yuli Pandu Anggara Purhadi Purhadi Puspita Kartikasari Ragil Saputra Rahmasari Nur Azizah Reza Dwi Fitriani Rezzy Eko Caraka Riama Oktaviani Samosir, Riama Oktaviani Rifki Adi Pamungkas, Rifki Adi Rita Rahmawati Rita Rahmawati Riza Fahlevi Rizki Brendita Br Tarigan Rose Debora Julianisa, Rose Debora Rukun Santoso Rung Ching Chen Saepudin, Yunus Sakhinah Abu Bakar Salma Farah Aliyah Sari, Ajeng Arum Sari, Indri Puspita Satriyo Adhy Setiawan Setiawan Setyoko Prismanu Ramadhan Siahaan, Rina Br Siska Alvitiani Siti Maulina Meutuah Sri Endah Moelya Artha Sudarno Sudarno Sudarno Sudarno Sugito Sugito - Sugito Sugito Suhartono Suhartono Suparti Suparti Tarno Tarno Tarno Tarno Tatik Widiharih Tiani Wahyu Utami Tsania Faizia Ubudia Hiliaily Chairunnnisa Via Risqiyanti Wahyu Sabtika Wawan Sugiarto, Wawan Wulandari, Heni Dwi Wulandari, Isna Youngjo Lee Yuciana Wilandari Yudha Subakti, Yudha Zulfa Wahyu Mardika, Zulfa Wahyu

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