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All Journal Media Statistika Statistika Jurnal Gaussian MIX : Jurnal Ilmiah Manajemen Indonesian Journal of Applied Statistics (JRAMB) Jurnal Riset Akuntansi Mercu Buana

Di Asih I Maruddani

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

Author-ID : 3131520

Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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VALUE AT RISK (VAR) METODE DELTA-NORMAL BERDASARKAN DURASI UNTUK UKURAN RISIKO OBLIGASI PEMERINTAH Setiani Setiani; Di Asih I Maruddani; Dwi Ispriyanti
Jurnal Gaussian Vol 10, No 3 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i3.32806

A bond is one of invesment instrument that is basically a debt instrument. In investing, beside getting profit there is also the risk of loss. The risk of loss is unavoidable but it can be manageable. The concept of a portfolio in investing is to minimize risk. Value at Risk (VaR) is a method used to measure risk where VaR states the estimated amount of the maximum loss that will be obtained at a certain level of confidence during a certain period in normal market conditions. In this article the risk of bonds FR0053, FR0056, FR0059, FR0061 and portfolio combinations calculated with VaR value of the Delta-Normal method are calculated based on the duration of the bonds. Normality test of the bond market price return is required before calculating VaR. The results obtained if it is assumed that the bonds are purchased at a price of 100 and with a confidence level of 95%, then the portfolio that has the smallest risk is the Bond portfolio of FR0059 and FR0061 with a VaR value Rp 21,436 (Trillions).

PEMBENTUKAN PORTOFOLIO SAHAM DENGAN METODE MARKOWITZ DAN PENGUKURAN VALUE AT RISK BERDASARKAN GENERALIZED EXTREME VALUE (Studi Kasus: Saham Perusahaan The IDX Top Ten Blue 2017) Ria Epelina Situmorang; Di Asih I Maruddani; Rukun Santoso
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In financial investment, investors will try to minimize risk and increase returns for portfolio formation. One method of forming an optimal portfolio is the Markowitz method. This method can reduce the risk and increase returns. The performance portfolio is measured using the Sharpe index. Value at Risk (VaR) is an estimate of the maximum loss that will be experienced in a certain time period and level of trust. The characteristics of financial data are the extreme values that are alleged to have heavy tail and cause financial risk to be very large. The existence of extreme values can be modeled with Generalized Extreme Value (GEV). This study uses company stock data of The IDX Top Ten Blue 2017 which forms an optimal portfolio consisting of two stocks, namely a combination of TLKM and BMRI stocks for the best weight of 20%: 80% with the expected return rate of 0.00111 and standard deviation of 0.01057. Portfolio performance as measured by the Sharpe index is 1,06190 indicating the return obtained from investing in the portfolio above the average risk-free investment return rate of -0,01010. Risk calculation is obtained based on Generalized Extreme Value (GEV) if you invest both of these stocks with a 95% confidence level is 0,0206 or 2,06% of the current assets. Keywords: Portfolio, Risk, Heavy Tail, Value at Risk (VaR), Markowitz, Sharpe Index, Generalized Extreme Value (GEV).

ANALISIS TEKNIKAL SAHAM DENGAN INDIKATOR GABUNGAN WEIGHTED MOVING AVERAGE DAN STOCHASTIC OSCILLATOR Yustian Dwi Saputra; Di Asih I Maruddani; Abdul hoyyi
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The Stochastic Oscillator which is one of the leading indicators has the disadvantage of opening the gap for false signals. To minimize false signals, the smoothing process is carried out using the Moving Average. Stochastic Oscillator is usually combined with SMA (Simple Moving Average). But SMA has the disadvantage of giving the same weight to all data, even though in reality the data that best reflects the next value is the last data. This makes the basis of weighting the WMA (Weighted Moving Average) method.This study aims to test the combination of Stochastic Oscillator with SMA and WMA and use the best combination to predict the trends that will occur and trading decisions taken from the results of these predictions. The research samples were ANTM, BBRI, and GIAA stocks from November 9 2015 to November 9, 2018.The results show a combination of Stochastic Oscillator and WMA is a better combination of predictions than Stochastic Oscillator and SMA because it has a smaller MSE value. Based on the comparison of signal accuracy based on Overbought and Oversold, the best period of combination of Stochastic Oscillator and WMA is period 25. From the predicted trend that will occur with a combination of Stochastic Oscillator and WMA period 25 a decision is made to buy shares for ANTM shares, sell shares for BBRI shares, and waiting for a buy signal for GIAA shares.Keywords: Stochastic Oscillator, SMA, WMA, Predictions, Trends

DIAGRAM KONTROL MULTIVARIAT np DAN DIAGRAM KONTROL JARAK CHI-SQUARE DALAM PENGENDALIAN KUALITAS PRODUK KAIN DENIM (Studi Kasus di PT Apac Inti Corpora) Dwi Harti Pujiana; Mustafid Mustafid; Di Asih I Maruddani
Jurnal Gaussian Vol 7, No 4 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v7i4.28866

Denim fabric sort number 78032 is one type of fabric in the last 4 years almost every month produced by PT Apac Inti Corpora. In the continuity of denim fabric production process, there are data defects (non-conformity) that causes the quality of denim fabric decreases. To maintain the consistency of the quality of products produced in accordance with the specified specifications, it is necessary to control the quality of the production process that has been running for this. Multivariate control charts attributes used are multivariate control charts np using the number of samples and the proportion of disability data with correlation between variables while the chi-square distance control charts use squared distances with uncorrelated data between variables. The results showed that in the multivariate control chart np there were 2 out-of-control observations in the phase II data using control limits from phase I data already controlled by the value of BKA of 636321.4. While in the chi-square distance control chart showed all observations are in in-control condition with BKA value of 0.06536. Controlled production process obtained multivariate process capability value for multivariate control np diagram of 0.625142 <1 which means the process is not capable, while the value of process capability in the chi-square distance control chart is 1.1329> 1 which means the process is capable. Keywords: denim fabric, multivariate np control chart, chi-square distance control chart, multivariate process capability

KLASIFIKASI STATUS KEMISKINAN RUMAH TANGGA DENGAN ALGORITMA C5.0 DI KABUPATEN PEMALANG Fatiya Nur Umma; Budi Warsito; Di Asih I Maruddani
Jurnal Gaussian Vol 10, No 2 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i2.29934

Pemalang regency is a district which has amount of poverty around 16.04%. One of the effort that must be improved in tackling poverty is increasing the accuracy of the government program’s target. The improvement of target accuracy is expected to give the better impact on the welfare of the population. This study classified the poverty status of households in Pemalang regency using C5.0 Algorithm. The poverty status of households is divided into two classes, namely poor and non-poor. There was an imbalance of data in both classes. Data imbalances were handled by using Synthetic Minority Oversampling Technique (SMOTE). From the research that has been done, SMOTE application in classification of household poverty status affected the evaluation value of the model. Previously the model could not classify the minority class and after using SMOTE the model produced an average value of sensitivity 25.80%. SMOTE application increased the average value of specificity from 91.16% to 94.91%. However, SMOTE application decreased the average value of accuracy which originally 91.16% down to 82.2%.Keywords : C5.0, Household poverty, Classification, SMOTE

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

PREDIKSI HARGA SAHAM MENGGUNAKAN GEOMETRIC BROWNIAN MOTION WITH JUMP DIFFUSION DAN ANALISIS RISIKO DENGAN EXPECTED SHORTFALL (Studi Kasus: Harga Penutupan Saham PT. Waskita Karya Persero Tbk.) Nidaul Khoir; Di Asih I Maruddani; Dwi Ispriyanti
Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v11i1.33989

Investment is an activity that is quite popular among investors in recent years. One of the forms of investment in the financial sector is investing in the capital market by buying stocks in a company. The level of profit from stock investment activities can be seen from the value of stock returns. Factors that can affect the value of stock returns are stock prices. However, stock prices often experience unpredictable changes so that they experience fluctuating movements with increasing time and developing situations, therefore a stock price model is needed to predict stock prices in the future period. The Geometric Brownian Motion with Jump Diffusion’s method is more appropriate to be used in predicting stock prices if there is a jump in stock price data. Predicted stock prices can be used as a basis for measuring the value of investment risk. The results of data processing indicate that the stock return data of PT. Waskita Karya Persero Tbk has a kurtosis value > 3 which means there is a jump in stock return data so that it is more accurately modeled using the Geometric Brownian Motion with Jump Diffusion’s method. The prediction results have a good level of accuracy based on the MAPE value of 18,733%. Furthermore, in order to measure the investment risk of the predicted stock price of PT. Waskita Karya Persero Tbk used the Expected Shortfall Historical Simulation’s method with a significance level of α = 5%, the results were 0,10939, and for the significance level α = 10%, the results were 0,07596. The calculation results show that the greater the trust level used, the greater the risk borne by investors.Keywords: Jump Diffusion Process, Expected Shortfall, Risk, Extreme Value

GENERALIZED PARETO DISTRIBUTION UNTUK PENGUKURAN VALUE AT RISK PADA PORTOFOLIO SAHAM SYARIAH DAN APLIKASINYA MENGGUNAKAN GUI MATLAB Desi Nur Rahma; Di Asih I Maruddani; Tarno Tarno
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The capital market is one of long-term investment alternative. One of the traded products is stock, including sharia stock. The risk measurement is an important thing for investor in other that can decrease investment loss. One of the popular methods now is Value at Risk (VaR). There are many financial data that have heavy tailed, because of extreme values, so Value at Risk Generalized Pareto Distribution is used for this case. This research also result a Matlab GUI programming application that can help users to measure the VaR. The purpose of this research is to analyze VaR with GPD approach with GUI Matlab for helping the computation in sharia stock. The data that is used in this case are PT XL Axiata Tbk, PT Waskita Karya (Persero) Tbk, dan PT Charoen Pokphand Indonesia Tbk on January, 2nd 2017 until May, 31st 2017. The results of VaRGPD are: EXCL single stock VaR 8,76% of investment, WSKT single stock VaR 4% of investment, CPIN single stock VaR 5,86% of investment, 2 assets portfolio (EXCL and WSKT) 4,09% of investment, 2 assets portfolio (EXCL and CPIN) 5,28% of investment, 2 assets portfolio (WSKT and CPIN) 3,68% of investment, and 3 assets portfolio (EXCL, WSKT, and CPIN) 3,75% of investment. It can be concluded that the portfolios more and more, the risk is smaller. It is because the possibility of all stocks of the company dropped together is small. Keywords: Generalized Pareto Distribution, Value at Risk, Graphical User Interface, sharia stock

PENERAPAN k-MODES CLUSTERING DENGAN VALIDASI DUNN INDEX PADA PENGELOMPOKAN KARAKTERISTIK CALON TKI MENGGUNAKAN R-GUI Hanik Malikhatin; Agus Rusgiyono; Di Asih I Maruddani
Jurnal Gaussian Vol 10, No 3 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i3.32790

Prospective TKI workers who apply for passports at the Immigration Office Class I Non TPI Pati have countries destinations and choose different PPTKIS agencies. Therefore, the grouping of characteristics prospective TKI needed so that can be used as a reference for the government in an effort to improve the protection of TKI in destination countries and carry out stricter supervision of PPTKIS who manage TKI. The purpose of this research is to classify the characteristics of prospective TKI workers with the optimal number of clusters. The method used is k-Modes Clustering with values of k = 2, 3, 4, and 5. This method can agglomerate categorical data. The optimal number of clusters can be determined using the Dunn Index. For grouping data easily, then compiled a Graphical User Interface (GUI) based application with RStudio. Based on the analysis, the optimal number of clusters is two clusters with a Dunn Index value of 0,4. Cluster 1 consists of mostly male TKI workers (51,04%), aged ≥ 20 years old (91,93%), with the destination Malaysia country (47%), and choosing PPTKIS Surya Jaya Utama Abadi (37,51%), while cluster 2, mostly of male TKI workers (94,10%), aged ≥ 20 years old (82,31%), with the destination Korea Selatan country (77,95%), and choosing PPTKIS BNP2TKI (99,78%).

COPULA FRANK PADA VALUE at RISK (VaR) PEMBENTUKAN PORTOFOLIO BIVARIAT (Studi Kasus : Saham-Saham Perusahaan yang Meraih Predikat The IDX Top Ten Blue Tahun 2017 dengan Periode Saham 20 Oktober 2014 – 28 Februari 2018) Juria Ayu Handini; Di Asih I Maruddani; Diah Safitri
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The capital market has an important role in society to invest in financial instruments. Investors can invest in the form of a portfolio that is by combining several shares to reduce the risk that will occur. Value at Risk (VaR) is a method for estimating the worst risk of an investment. GARCH (Generalized Autoregressive Conditional Heteroscedasticity) is used to model high-volatile stock data that causes residual variance is not constant. Copula theory is a powerful tool for modeling joint distributions because it does not require normality assumptions that are difficult to fulfill in financial data. Copula Frank has a feature that can identify positive and negative dependencies. This study aims to measure the value of VaR using the Frank-GARCH copula method using stock returns data of PT Bank Rakyat Indonesia, Tbk (BBRI), PT Telekomunikasi Indonesia, Tbk (TLKM), and PT. Unilever Indonesia, Tbk (UNVR) for the period 20 October 2014 - 28 February. Bivariate portfolio pairs obtained namely TLKM and UNVR shares because they have the highest Rho Spearman residual correlation value of ρ = 0.3204. Based on the generation of data using Monte Carlo simulations, the results of the calculation of Value at Risk (VaR) of 1.40% at the 90% confidence level, 1.89% at the 95% confidence level, and 2.79% at the 99% confidence level. Keywords: Value at Risk, Frank copula, GARCH, Monte Carlo

Co-Authors Abdul Hoyyi Abdurakhman Abdurakhman Agus Rusgiyono Ahmad Badruttamam Alifia Hana Linda Rachmawati Atikah Mufidah Budi Warsito Dede Andrianto Dedi Baleo Pasaribu Delsy Nurutsaniyah Desi Nur Rahma Diah Safitri Dina Rahma Prihatiningsih Dwi Harti Pujiana Dwi Ispriyanti Eka Anisha Eny Sulistyowati Fatiya Nur Umma Fredy Yoseph Marianto Hanik Malikhatin Hasbi Yasin Hasbi Yasin Henny Setyowati Juria Ayu Handini Khoirul Anam Lina Tanasya Maria Odelia Mas'ad Mas'ad Meiliawati Aniska Mia Anastasia Sinulingga Mustafid Mustafid Mutik Dian Prabaning Tyas Nidaul Khoir Nur Fitriyah Nur Khofifah Purwati Ayuningtyas Puspita Kartikasari Puspita Kartikasari Putri Devitasari Reno Penggalih Surya Wardhani Reyuli Andespa Ria Epelina Situmorang Rita Rahmawati Rukun Santoso Salsabila Syifa Binsanno Setiani Setiani Sudarno Sudarno Sugito Sugito Suparti Suparti Tarno Tarno Tatik Widiharih Trimono Trimono Trimono Trimono Tutut Dewi Astuti Tutut Dewi Astuti Yustian Dwi Saputra

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