Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

0.659

P-Index

This Author published in this journals

All Journal Media Statistika MATEMATIKA Jurnal Gaussian Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) (JRAMB) Jurnal Riset Akuntansi Mercu Buana International Journal of Data Science, Engineering, and Analytics (IJDASEA) Prosiding Seminar Nasional Unimus

Di Asih I Maruddani

Unknown Affiliation

Author-ID : 284298

Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Mathematics Nursing Public Health Other

Published : 24 Documents Claim Missing Document

Claim Missing Document

Articles

Title

1 2

PENERAPAN MODEL INDEKS TUNGGAL UNTUK OPTIMALISASI PORTOFOLIO DAN PENGUKURAN VALUE AT RISK DENGAN VARIANCE COVARIANCE (Studi Kasus: Saham yang Stabil dalam LQ 45 Selama Periode Februari 2011 – Juli 2016) Hanifa Eka Oktafiani; Di Asih I Maruddani; Suparti Suparti
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One of popular investments among investors is investing in a form of stock in go public companies. Investing stocks must not be separated from a wide variety of risks. One way to minimize risk is by taking a portfolio of several stocks. This research uses single index model to form portfolio of several stocks because it has simple computation than other method. This model based on the observation that price of securities have linier fluctuation with market indeks. Estimate of Value at Risk (VaR) can be calculated using variance covariance method which requires that return of a stock and return portfolio of several stocks have a normal distribution. This research aplicated to stable several stocks, in the meaning that always recorded in LQ 45 during February 2011 until July 2016. Based on 21 stable stocks in LQ 45, there are six stocks included in the optimal portfolio. That is stock of GGRM (Gudang Garam Ltd.), BBCA (Bank Central Asia Ltd.), JSMR (Jasa Marga Persero Ltd.), LPKR (Lippo Karawaci Ltd.), BBRI (Bank Rakyat Indonesia Persero Ltd.), and INDF (Indofood Sukses Makmur Ltd.), which estimated of VaR in a month after investing on optimal portfolio at 95% confidence level is Rp 7.846.572,00 from initial capital of Rp 100.000.000,00. Keywords: Portfolio, Stock, Single Index Model, Variance Covariance, LQ 45

ANALISIS DATA RUNTUN WAKTU MENGGUNAKAN METODE WAVELET THRESHOLDING DENGAN MAXIMAL OVERLAP DISCRETE TRANSFORM Dyah Ayu Kusumaningrum; Suparti Suparti; Di Asih I Maruddani
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Wavelet is a mathematical tool for analyzing time series data. Wavelet has certain properties one of which is localized in the time domain and frequency and form an orthogonal basis in the space L2(R). There are two types of wavelet estimators are linear and nonlinear wavelet estimators. Linear wavelet estimators can be analyzed using the approach of Multiresolution Analysis (MRA), while nonlinear wavelet estimator called Wavelet Thresholding. Wavelet thresholding are emphasizing the reconstruction of wavelet using a number of the largest coefficient or can be said that only coefficient greater than a value taken, while other coefficients are ignored. There’re several factors that affect the smooth running of the estimation are the type of wavelet function, types of functions of thresholding, thresholding parameters, and the level of resolution. Therefore, in this thesis will have optimal threshold value in analyzing the data. Wavelet Thresholding method provides value of Mean Square Error (MSE) that smaller compare to wavelet method with the approach Multiresolution Analysis (MRA). In this case study Wavelet Thresholding are considered better in the analysis of time series data. Keywords: Multiresolution Analysis, Wavelet Thresholding Estimator.

VALUASI COMPOUND OPTION PUT ON CALL TIPE EROPA PADA DATA SAHAM FACEBOOK Muhammad Sunu Widianugraha; Di Asih I Maruddani; Diah Safitri
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Option is a contract that gives the right to individuals to buy (call options) or sell (put options) the underlying asset by a certain price for a certain date. One type of options that are traded is compound options. Compound option model is introduced by Robert Geske in 1979. Compound option is option on option. Compound option put on a call is put option where the underlying asset are call option. An empirical study using compound option put on a call stocks of Facebook. It has strike price compound option US$ 77.5 and strike price call option US$ 80, with the first exercise date on September 26, 2014 and the second exercise date on October 31, 2014. The theoritical price of compound option put on call on stocks of Facebook is US$ 75.65048. Keywords: Compound option, put on a call, option stocks of Facebook, Black-Scholes model, theoritical price.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI DIVIDEND PAYOUT RATIO (DPR) MENGGUNAKAN ANALISIS REGRESI LINIER DENGAN BOOTSTRAP (Studi Kasus: PT. Unilever Indonesia, Tbk Tahun 1999-2015) Lia Safitri; Di Asih I Maruddani; Rukun Santoso
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The amount of dividend paid by the company to shareholders or dividend payout ratio is the main factor that investors pay attention to invest their capital into the company. Investors want a relative dividend, even increasing over time. Factors influencing the level of dividend payout ratio are Return on Equity (ROE), stock price, liquidity ratio, and leverage level. Based on this, multiple linear regression analysis with bootstrap is used. The purpose of this study is to analyze the factors that significantly affect the dividend payout ratio based on the best model used to predict the value of dividend payout ratio for the next period. The bootstrap method is used to overcome the occurrence of multicollinearity among independent variables due to the small sample size. Based on the simulation done with software R using PT data. Unilever Indonesia, Tbk from 1999-2015 obtained best model is bootstrap residual with 2 significant independent variable are ROE and level of leverage. Based on the best model, the predicted value of dividend payout ratio of 2016 is 41.60196 with percentage error of 7.0812%. Keywords : Regression analysis, Bootstrap, Dividend Payout Ratio, ROE, leverage

PEMODELAN HARGA SAHAM DENGAN GEOMETRIC BROWNIAN MOTION DAN VALUE AT RISK PT CIPUTRA DEVELOPMENT Tbk Trimono Trimono; Di Asih I Maruddani; Dwi Ispriyanti
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Financial sector investment is an activity that attracts a lot of public interest. One of them is investing funds in purchasing company’s shares. Profit received from stock investment activity can be seen from the value of stock returns. While, if the previous stock returns Normal distribution, the future stock price can be predicted by Geometric Brownian Motion Method. Based on the stock price prediction, can also be measured an estimated value of the investment risk. The result of data processing shows that the stock price prediction of PT. Ciputra Development Tbk period December 1, 2016 untuk January 31, 2017, has very good accuracy, based on the value of MAPE 1.98191%. Further, Value at Risk Method of Monte Carlo Simulation with α = 5% significance level was used to measure the share investment risk of PT.Ciputra Development Tbk. Thus, this method is only useful if it can be used to predict accurately. Therefore, backtesting is needed. Based on the processing obtained data, backtesting generates the value of violation ratio at 0, it means that at significance level α = 5%, Value at Risk Method of Monte Carlo Simulation can be used at all levels of probability violation.. Keywords : Geometric Brownian Motion, Risk, Value at Risk, Backtesting

PENGUKURAN RISIKO KREDIT OBLIGASI KORPORASI DENGAN CREDIT VALUE AT RISK (CVAR) DAN OPTIMALISASI PORTOFOLIO MENGGUNAKAN METODE MEAN VARIANCE EFFICIENT PORTFOLIO (MVEP) Agus Somantri; Di Asih I Maruddani; Abdul Hoyyi
Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Getting benefits of many kinds of coupon is not the only advantage of bond investment, but also it gives potential risks such as credit risk. Credit risk originates from the fact that counterparties may be unable to fulfill their contractual obligations. Credit Value at Risk (CVaR) is introduced as a method to calculate bond credit risk if default occurs. CVaR is defined as the most significant credit loss which occurs unexpectedly at the selected level of confidence, measured as the deviation of Expected Credit Loss (ECL). To construct optimal bond portfolio requires Mean variance Efficient Portfolio (MVEP) method. MVEP is defined as the portfolio with minimum variance among all possible portfolios that can be formed. This study case has been constructed through two bonds, bond VI of Jabar Banten Bank (BJB) year 2009 serial B and bond of BTPN Bank I year 2009 serial B. Based on the R programming output, the obtained results for bonds with a rating idAA BJB, has a positive CVaR value of Rp 22.728.338,00. While bonds with a rating idAA BTPN and portfolio for both bonds, each of which has a negative CVaR value amounted Rp 28.759.098,00 and Rp 32.187.425,00. CVaR is positive (+) expressed as the loss addition of ECL while is negative () expressed as a decrease in loss of ECL. For optimal bond portfolio, gained weight for each bond is equal to 16,85202% for BJB and 83,14798% for BTPN bonds.

CLUSTERING KARAKTERISTIK INDUSTRI KECIL DAN MENENGAH DI KOTA KENDARI MENGGUNAKAN ALGORITMA k-PROTOTYPES Reihanah, Khalifah Nadya; I Maruddani, Di Asih; Widiharih, Tatik
Jurnal Gaussian Vol 12, No 3 (2023): 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.12.3.340-351

Industri Kecil Menengah (IKM) have important roles in economic development. The large number of IKM cannot be separated from various problems. The basic problems faced by IKM in Kendari are limited capital, inadequate human resources, difficulty in obtaining raw materials, and the Indonesian economy which has slumped due to the impact of the COVID-19 pandemic. This research was conducted with the aim of classifying the characteristics of the IKM with the optimal number of clusters. The method used is k-Prototypes Clustering with values of k = 2, 3, 4, ..., and 10. The k-Prototypes method is a clustering method that maintains the efficiency of the k-Means algorithm in handling large data when compared to the hierarchical clustering method. This method can group mixed type data (consisting of numeric type data and categorical type data). Based on the analysis, the optimal number of clusters is five clusters, with a Silhouette Index value of 0.461. Cluster 5 is the best IKM cluster with the highest average number of workers and the highest average investment value, while cluster 2 has the lowest average investment value and IKM in this cluster is relatively new compared to IKM in other clusters.

Co-Authors Abdul Hoyyi Abdurakhman Abdurakhman Agus Somantri ANNISA RAHMAWATI Arief Seno Nugroho Azzarah, Dian Fatimah Budi Warsito Dedi Rosadi Desy Rahmawati Ningrat Diah Safitri Dwi Ispriyanti Dyah Ayu Kusumaningrum Endang Purbowati Hanifa Eka Oktafiani Hasbi Yasin I Gede Susrama Mas Diyasa Indradewa, Rhian Irfan Afifi Khotimatus Sholihah Kumo Ratih Lia Safitri Masithoh Yessi Rochayani Moch. Abdul Mukid Muhammad Sunu Widianugraha Nurul Fauziah Oktafiani Widya Ningrum Prisma Hardi Aji Riyantoko Reihanah, Khalifah Nadya Rukun Santoso Siti Maulina Meutuah Sri Sumiyati Sugito Sugito Suparti Suparti Tarno Tarno Tatik Widiharih Triastuti Wuryandari Trimono Trimono Trimono, Trimono Yudia Yustine

Title Search

Found 17 Documents Search Journal : Jurnal Gaussian

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 17 Documents
Search
Journal : Jurnal Gaussian