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All Journal Media Statistika Jurnal Ilmu Dasar 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 Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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RISIKO INVESTASI SAHAM SECOND LINER DENGAN TAIL VALUE AT RISK Di Asih I Maruddani; Tutut Dewi Astuti
MIX: JURNAL ILMIAH MANAJEMEN Vol 11, No 2 (2021): MIX: Jurnal Ilmiah Manajemen
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/mix.2021.v11i2.009

This pandemic which has been going on for almost a year, is very influential in all fields. Economic growth and investment in all countries have been declined dramatically. Indonesian Composite Stock Price Index (CSPI) as an indicator of stock performance in Indonesia is weakening. Big caps stocks, which are usually the main driver for domestic bourses, fell sharply uncontrollably. However, second liner stocks managed to hold down the CSPI throughout 2020. This condition made many investors switch to stocks in this group. Low prices and high profit potential are attractive considerations. But high price fluctuations make investing riskier. Tail Value at Risk (T-VaR) as a measure of risk in a bad situation is the right measure so that investors can estimate the worst possibility and a larger reserve fund. The purpose of this study is to measure the investment risk in second liner stocks based on T-VaR. The six stocks of Pefindo 25 Index indicate that at a 95% significance level, the investment risk in second liner stocks will be in the range 6,14% - 8,22% of the investment.

PENGUKURAN RISIKO GLUE-VALUE-AT-RISK PADA DATA DISTRIBUSI ELLIPTICAL (Studi Kasus: Data Saham PT Indocement Tunggal Prakarsa Tbk, PT Unilever Indonesia Tbk, PT United Tractors Tbk, Periode 1 Juni 2018 – 29 November 2019) Dede Andrianto; Di Asih I Maruddani; Tarno Tarno
Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Risk measurement is carried out to determine the risk. Popular methods that can be used to measure risk at a confidence level are Value-at-Risk (VaR) and Tail-Value-at-Risk (TVaR). A Risk measurement should satisfy: translation invariance, positive homogenicity, monocity and subadditivity. VaR does not satisfy one of coherent axioms, namely subadditivity. TVaR is considered capable of overcoming VaR problems, but it’s too large for a risk measure. Glue-Value-at-Risk (GlueVaR) is a method that can overcome these problems because it can be valued between VaR and TVaR and fulfills four coherent axioms. In this paper GlueVaR used in the elliptical distribution for normal distribution to measure the risk of the stock of PT Indocement Tunggal Prakarsa Tbk (INTP), PT Unilever Indonesia Tbk (UNVR), and PT United Tractors Tbk (UNTR) for the period June 1st 2018 – 29th November 2019. After knowing the stock return is normally distributed and used confidence levels of α = 95% and β = 98%, a high selection of distortion ℎ1=0,3≤1−????1−???? and ℎ2=0,4≥ℎ1. The high distortion selected makes GlueVaR worth between VaR and TVaR. GlueVaR for INTP, UNVR, and UNTR respectively are 4.886%; 2.999%; and 4.083%. Thus the lowest risk level is PT Unilever Indonesia Tbk.Keywords : Value-at-Risk, Tail-Value-at-Risk, Glue-Value-at-Risk

VALUE at RISK (VaR) DAN CONDITIONAL VALUE at RISK (CVaR) DALAM PEMBENTUKAN PORTOFOLIO BIVARIAT MENGGUNAKAN COPULA GUMBEL Dina Rahma Prihatiningsih; Di Asih I Maruddani; Rita Rahmawati
Jurnal Gaussian Vol 9, No 3 (2020): 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.v9i3.28913

One way to minimize risk in investing is to form of portfolio by combining several stocks.Value at Risk (VaR) is a method for estimating risk but has a weakness that is VaR is incoherent because it does not have the subadditivity. To overcome the weakness of VaR, Conditional Value at Risk (CVaR) can use. Stock data is generally volatile, so ARIMA-GARCH is used to model it. The selection of ARIMA models on R software can be automatically using the auto.arima() function. Then Copula Gumbel is a method for modeling joint distribution and flexible because it does not require the assumption of normality and has the best sensitivity to high risk so that it is suitable for use in stock data.The first step in this research is to modeling Copula Gumbel-GARCH with the aim to calculate VaR and CVaR on the portfolio of PT Bank Mandiri Tbk (BMRI) and PT Indo Tambangraya Megah Tbk (ITMG). At the confidence level 99%, 95%, and 90% obtained the VaR results sequentially amounted to 3.977073%; 2.546167%; and 1.837288% and the CVaR results sequentially amounted to 4.761437%; 3.457014%; and 2.779182%. The worst condition is a loss with VaR and it is still possible if a worse condition occurs is a loss with CVaR so that investors can be more aware of the biggest loss that will be suffered.Keywords: Value at Risk, Conditional Value at Risk, Auto ARIMA, Copula Gumbel.

PERBANDINGAN METODE NAÏVE BAYES DAN BAYESIAN REGULARIZATION NEURAL NETWORK (BRNN) UNTUK KLASIFIKASI SINYAL PALSU PADA INDIKATOR STOCHASTIC OSCILLATOR (Studi Kasus: Saham PT Bank Rakyat Indonesia (Persero) Tbk Periode Januari 2017 – Agustus 2019) Fredy Yoseph Marianto; Tarno Tarno; Di Asih I Maruddani
Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Keputusan untuk membeli atau menjual saham merupakan kunci utama untuk memperoleh keuntungan dalam trading dan investasi. Salah satu indikator yang dapat digunakan dalam menentukan momentum untuk membeli atau menjual saham adalah Stochastic Oscillator. Sebagai indikator yang sensitif terhadap pergerakan harga saham, Stochastic Oscillator sering mengeluarkan sinyal palsu yang mengakibatkan kerugian dalam transaksi. Terdapat 9 atribut yang diduga dapat mengidentifikasi apakah suatu sinyal yang keluar dari indikator Stochastic Oscillator merupakan sinyal palsu atau tidak. Tujuan dari penelitian ini adalah melakukan klasifikasi atau deteksi sinyal dengan metode Naïve Bayes dan Bayesian Regularization Neural Network (BRNN), dan kemudian membandingkan tingkat akurasi hasil klasifikasi antara kedua metode. Hasil dari penelitian ini menunjukkan bahwa hanya terdapat 6 atribut yang dapat digunakan untuk mengidentifikasi apakah suatu sinyal yang keluar merupakan sinyal palsu atau tidak, yaitu kondisi IHSG, kondisi high price, kondisi low price, kondisi close price, posisi %K, dan posisi %D, serta tingkat akurasi dari metode Naïve Bayes adalah sebesar 76,92%, sedangkan akurasi dari metode BRNN adalah sebesar 80,77%. Dapat disimpulkan bahwa dalam penelitian ini, metode BRNN lebih baik dibandingkan dengan metode Naïve Bayes untuk mendeteksi sinyal palsu yang keluar dari indikator Stochastic Oscillator.Kata kunci: Stochastic Oscillator, Sinyal Palsu, Klasifikasi, Naïve Bayes, BRNN, Akurasi

VECTOR AUTOREGRESSIVE STABILITY CONDITION CHECK UNTUK PEMODELAN DAN PREDIKSI SUMBER PENERIMAAN PABEAN BELAWAN Mia Anastasia Sinulingga; Di Asih I Maruddani; Abdul Hoyyi
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Customs Intermediate are an institution that is responsible for regulating the flow of export and import trade activities in the Customs Area with the revenue coming from import duties and export duties. The time series data from the customs acceptance component import dan export which have a relationship between variables. Vector Autoregressive is a statistical method used in predicting and evaluating interrelationships between variables. The purpose of this study is to obtain a model for predicting import and export by using the VAR model and detecting the stability of the model. Model requirements are said to be stable if all modulus values from roots characteristic of coefficient matrices ≤ 1 that the predicted results can be verified. The data is divided into in sample data starting from January 2010 to June 2018 and out sample data starts from July 2018 until December 2018. The results of data analysis in this study, the model obtained for prediction is the VAR model (4) and there is a direct relationship between both variables. The VAR (4) residual model fulfills the assumption of white noise, while the assumption of multivariate normality is not fulfilled. Based on out sample the value of MAPE for import variables 18.42%, export 12.94% shows the VAR model (4) has good predictive capabilities that can be used for predicting future periods. Predicted results on import show fluctuations during the period of January to December 2019 while in the export shows increase during the period of January to December 2019.

OPTIMASI REGRESI LOGISTIK MENGGUNAKAN ALGORITMA GENETIKA UNTUK PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI PENGGOLONGAN KREDIT BANK (Studi Kasus: Debitur di PT BPR Gunung Lawu Klaten Periode Tahun 2017) Reno Penggalih Surya Wardhani; Sudarno Sudarno; Di Asih I Maruddani
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit is the greatest asset managed by banks and also the most dominant contributor to the bank’s income. But in its implementation, the provision of credit to the public is at risk for non-performing loans. For this reason, creditors try to minimize the occurrence of non-performing loans by predicting credit risk appropriately. In this study, modeling the factors that influence credit classification at PT BPR Gunung Lawu is useful for predicting the credit risk of prospective debtors. Modeling are done using logistic regression and genetic algorithms. Factors suspected of influencing credit classification include age, gender, marital status, education, home ownership, employment, net income, tenor, type of business, type of loan, type of loan interest, and loan size. Estimated model parameters obtained from logistic regression were optimized using genetic algorithms. The fitness function used is pseudo or and MSE. The best model is generated by modeling with genetic algorithms based on MSE fitness. The model produces the highest value of 0.1958 and the lowest MSE value of 0.1648 with classification accuracy of 75.33%. Keywords: credit classification, logistic regression, genetic algorithms

PERAMALAN HARGA SAHAM DENGAN METODE LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR) (Studi Kasus pada Harga Saham Mingguan PT. Bank Mandiri Tbk Periode 03 Januari 2011 sampai 24 Desember 2018) Maria Odelia; Di Asih I Maruddani; Hasbi Yasin
Jurnal Gaussian Vol 9, No 4 (2020): 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.v9i4.29403

Series such as financial and economic data do not always form a linear model, so a nonlinear model is needed. One of the popular nonlinear models is the Smooth Transition Autoregressive (STAR). STAR has two possible suitable transition function such as logistic and exponential that need to be test to find the appropriate transition function. The purpose of writing this thesis is to determine the LSTAR model, then use the model to predict the stock price of PT Bank Mandiri. This study uses the data of the weekly stock price of PT Bank Mandiri from the period of January 3, 2011 to December 24, 2018 as insample data and the period of January 1, 2019 to December 30, 2019 as outsample data. The research procedure begins with modeling the data with the Autoregressive (AR) process, testing the linearity of the data, modeling with LSTAR, forecasting, and finally evaluating the results of forecasting. Evaluating the results of the forecasting of the weekly share price of PT Bank Mandiri with the STAR model results in the best nonlinear model LSTAR (1,1). This model produces an highly accurate forecasting result with a value of symmetric Mean Square Error (sMAPE) to be 5.12%.Keywords: Nonlinear, Time Series, STAR, LSTAR.

EXPECTED SHORTFALL DENGAN PENDEKATAN GLOSTEN-JAGANNATHAN-RUNKLE GARCH DAN GENERALIZED PARETO DISTRIBUTION Lina Tanasya; Di Asih I Maruddani; Tarno Tarno
Jurnal Gaussian Vol 9, No 4 (2020): 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.v9i4.29447

Stock is a type of investment in financial assets that are many interested by investors. When investing, investors must calculate the expected return on stocks and notice risks that will occur. There are several methods can be used to measure the level of risk one of which is Value at Risk (VaR), but these method often doesn’t fulfill coherence as a risk measure because it doesn’t fulfill the nature of subadditivity. Therefore, the Expected Shortfall (ES) method is used to accommodate these weakness. Stock return data is time series data which has heteroscedasticity and heavy tailed, so time series models used to overcome the problem of heteroscedasticity is GARCH, while the theory for analyzing heavy tailed is Extreme Value Theory (EVT). In this study, there is also a leverage effect so used the asymmetric GARCH model with Glosten-Jagannathan-Runkle GARCH (GJR-GARCH) model and the EVT theory with Generalized Pareto Distribution (GPD) to calculate ES of the stock return from PT. Bank Central Asia Tbk for the period May 1, 2012-January 31, 2020. The best model chosen was ARIMA(1,0,1) GJR-GARCH(1,2). At the 95% confidence level, the risk obtained by investors using a combination of GJR-GARCH and GPD calculations for the next day is 0.7147% exceeding the VaR value of 0.6925%.

COPULA FRANK UNTUK PERHITUNGAN VALUE AT RISK PORTOFOLIO BIVARIAT PADA MODEL EXPONENTIAL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY Eka Anisha; Di Asih I Maruddani; Suparti Suparti
Jurnal Gaussian Vol 10, No 4 (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.v10i4.29932

Stocks are one type of investment that promises return for investors but often carries a high risk. Value at Risk (VaR) is a measuring tool that can calculate the amount of the worst loss that occurs in a stock portfolio with a certain level of confidence and within a certain time period. In general, financial data have a high volatility value, which causes the residuals are not normally distributed. ARCH/GARCH modoel is used to solve the heteroscedasticity problem. If the data also have an asymmetric effect, it is modelled with Exponential GARCH model. Copula-Frank is part of the Archimedian copula which is used to solve empirical cases. The data on this study were BBCA and KLBF stock price return data in the observation period 30 December 2011 – 6 December 2019. Furthermore, to test the validity of the VaR model, a backtesting test will be carried out using the Kupiec Test. The results showed that the best model used for BBCA stocks was ARIMA (1,0,1) EGARCH (1,1) and for KLBF stocks was ARIMA (1,0,1) EGARCH (1,2). The amount of risk with a 95% confidence level used a combination of the EGARCH and Copula-Frank models was 2.233% of today's investment. Based on the backtesting test used the Kupiec Test, the VaR model of the portfolio obtained was declared valid.

PENERAPAN ANALISIS KLASTER K-MODES DENGAN VALIDASI DAVIES BOULDIN INDEX DALAM MENENTUKAN KARAKTERISTIK KANAL YOUTUBE DI INDONESIA (Studi Kasus: 250 Kanal YouTube Indonesia Teratas Menurut Socialblade) Ahmad Badruttamam; Sudarno Sudarno; Di Asih I Maruddani
Jurnal Gaussian Vol 9, No 3 (2020): 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.v9i3.28907

YouTube is one of the most popular online platforms today. The popularity of YouTube has makes it an effective advertising medium. In April 2019, Socialblade released the top 250 YouTube channels in Indonesia based on their gradations with various characteristics. YouTube channel data will be grouped into several clusters to make it easier for advertisers to choose channels with characteristics as needed. The purpose of this study is to determine the best number of clusters and determine their characteristics. The method used is the k-Modes cluster analysis with values k = 3, 4, 5, ..., 8. The k-Modes method can group objects that have categorical type variables into relatively homogeneous groups. The best number of clusters (k) can be checked using the Davies Bouldin Index (DBI). Based on the analysis carried out, obtained the best number of six clusters with a Davies-Bouldin Index value of 1.080509. The most recommended cluster for advertising is cluster 6, which has grade A characteristics, gold title, and has an estimated annual income of 5 million USD < income ≤ 10 million USD. Keywords: Youtube, Cluster Analysis, k-Modes, Categorical Data, Davies-Bouldin Index

Co-Authors Abdul Hoyyi Agus Rusgiyono Ahmad Badruttamam Alifia Hana Linda Rachmawati Arief Rachman Hakim 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 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 Nadiyah Hafidah Sinambela Nidaul Khoir Nur Fitriyah Nur Khofifah Purwati Ayuningtyas 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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