Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

0.961

P-Index

This Author published in this journals

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

Published : 108 Documents Claim Missing Document

Claim Missing Document

Articles

Title

5 6 7 8 9

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.

ANALISIS LAPANGAN PEKERJAAN UTAMA DI JAWA TENGAH BERDASARKAN GRAFIK BIPLOT SQRT (SQUARE ROOT BIPLOT) Anik Nurul Aini; Diah Safitri; Abdul Hoyyi
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Biplot analysis is one of the methods of descriptive statistical analysis that can present data of the n objects which p variables into a two-dimensional graph. Biplot has several types according to the scale of α used. There are three scales α which is often used in the biplot analysis, that are α = 0, α = 0,5 and α = 1. Biplot with α = 1 is called the RMP biplot (Row Matric Preserving). Biplot with α = 0 is called CMP biplot (Column Matric Preserving). While biplot with α = 0,5 called SQRT biplot (Square Root Biplot). Biplot with a scale of α = 0,5 is the best biplot to describe a data, because it make a graph between variable and object spread evenly. This study aims to create a SQRT biplot amount of population aged 15 years and over who worked according to district/city and major employment opportunities in Central Java. Biplot chart shows areas that have similar characteristics with the closest Euclidean distance. The diversity of characteristics is indicated by the length of the vector, the longest vector contained in the agricultural sector. Based on the biplot analysis in this study, it was obtained that the goodness size biplot is equal to 64,19958%. Keywords: Biplot, Singular Value Decomposition, Jobs, SQRT, Square Root Biplot

ANALISIS TECHNOLOGY ACCEPTANCE MODEL PADA APLIKASI PLATFORM SHOPEE DENGAN PENDEKATAN PARTIAL LEAST SQUARE (STUDI KASUS PADA MAHASISWA UNIVERSITAS DIPONEGORO) Ovie Auliya’atul Faizah; Suparti Suparti; Abdul Hoyyi
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.v10i3.32802

E-commerce refers to business transactions using digital networks such as the internet. Based on the rank on the Appstore and Playstore, Shopee places the first rank. In 2019, Shopee had 56 million visitors. Meanwhile, in the same year, it had 3,225 workers. The imbalance between the number of Shopee visitors and Shopee employees allows users to be disappointed with Shopee's services, but on the other hand, there are also many users who are happy with its services. With both positive and negative responses to the services provided by Shopee, this study analyzes the factors affecting the acceptance of Shopee Apps on students of Universitas Diponegoro Semarang. The analysis was based on the Technology Acceptance Model (TAM). It used the Structural Equation Modeling with the Partial Least Square (SEM-PLS) approach. The study used primary data obtained by distributing questionnaires to students of Universitas Diponegoro. The result showed 28 valid indicators, 5 deal inner models, and 8 significant pathways. All the causality between latent variables contained in the Technology Acceptance Model (TAM) have a positive and significant effect, it's just that the results of integrating trust variables on TAM, namely the latent variable between trust and interest in usage behavior, have no significant effect.

KUALITAS PELAYANAN PADA BANK JAWA TENGAH (Studi Kasus : Bank Jateng Cabang Tembalang) Yosi Dhyas Monica; Abdul Hoyyi; Moch. Abdul Mukid
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Service attendance quality is superiority level which is be expected and control above its superiority level for satisfying consumen's desire. In this case, there are 5 service quality dimensions. Those are tangible, reliability, responsiveness, assurance, and emphaty. This research study was doing at Bank Jateng, where the respondents are the customer of Bank Jateng. Importance Performance Analysis consist of two components, there are quadrant analysis and discrepancy analysis (gap). Quadran analysis can find out the respond of cusumens against variable which has plotted based on interest and performance level from those variables. While gap analysis is being used for perceiving discrepancy between performance of a variable with the expectation from consumen against its variable. Customer Satisfaction Index (CSI) is used for discovering overall satisfaction level of customers. The T2 hotelling control chart is to know the qualiy controlof two or more related quality characteristics. Result of the research is showing that for quadran analysis, those variables which representing 5 service quality dimensions be located spread in different quadran. For gap analysis, the service perormance of a bank represented by 20 variables who representing 5 service quality dimensions, all of which is still under customers expectation. CSI value aa big as 72,22% which is mean customers satisfaction index is on the satisfaction criteria. On T Hotelling chart is said that the process is not restrained statistically yet because there are 4 points is on the top of control chart

HISTORICAL SIMULATION UNTUK MENGHITUNG VALUE AT RISK PADA PORTOFOLIO OPTIMAL BERDASARKAN SINGLE INDEX MODEL MENGGUNAKAN GUI MATLAB (Studi Kasus: Kelompok Saham JII Periode Juni - November 2017) Tresno Sayekti Nuryanto; Alan Prahutama; Abdul Hoyyi
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.v7i4.28869

The essence of investment is a placement of a number of funds at one time in hope of gaining profits in the future. One of the most traded forms of investment is stocks. When investing in stocks, investors often run the risk of loss. This loss risk can be overcome by forming a portfolio consisting of several shares. To form an optimal portfolio, investors must first determine an efficient portfolio that produces a certain level of profit with the lowest risk, or a certain level of risk with the highest level of profit. One method for determining the optimal portfolio is to use the Single Index Model method. Whereas to calculate Value at Risk (VaR) using the Historical Simulation method. In this study, researcher used data from the daily closing price of shares incorporated in the Jakarta Islamic Index (JII) stock group in the period of June - November 2017. The shares which will be used were 9 shares in the JII stock group. According to the research result, there are three stocks that go into an optimal portfolio that is SMGR, UNTR, and KLBF with the value of each of its shares respectively by 48,54%, 46,18%, and 5,28%. While the value of the Value at Risk with initial capital of Rp100.000.000, 1 day holding period and a trust level of 95% for optimal portfolio and each stock that goes into optimal portfolio amounted Rp2.090.283, Rp2.258.600, Rp3.403.000, and Rp2.564.200. Keywords: Share, Portofolio, Single Index Model, Value at Risk, Historical Simulation, JII.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUASAN MAHASISWA DALAM PEMILIHAN JURUSAN MENGGUNAKAN STRUCTURAL EQUATION MODELING (SEM) (Studi Kasus di Jurusan Statistika Universitas Diponegoro Semarang) Allima Stefiana Insani; Abdul Hoyyi; Rita Rahmawati
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

University is an institution that provide educational service which has a wide variety of majors. Image of the university would affect the interest of new students in decision making process, as this will affect student satisfaction through the course. Many factors influence students decision in determining their aim majors, such as service quality, curriculum, environment and academic ability. These factors are latent variables then Structural Equation Modeling (SEM) used to determine factors effect that affect student satisfaction in selection of majors. The research conducted at Diponegoro University in Statistics Department. Overall model fit test obtain Goodness Of Fit on model with the value of GFI = 0,875 and RMSEA = 0,084 are indicative of a good fit. In concluding the analysis, the factors that affect student satisfaction in decision to choose Statistics Department can be measured by academic ability, curriculum, and service quality. Students decision in choosing Statistics Department can be explained by the academic ability of students, the curriculum which is owned by Statistics Department and quality of service that is owned by the department of statistics at 96,9%. Statistics students satisfaction can be explained by academic ability of students and student decision after choosing Statistics Department of 68,8%. Key words: Decision in choosing major, students satisfaction, Structural Equation Modeling

VERIFIKASI MODEL ARIMA MUSIMAN MENGGUNAKAN PETA KENDALI MOVING RANGE (Studi Kasus : Kecepatan Rata-rata Angin di Badan Meteorologi Klimatologi dan Geofisika Stasiun Meteorologi Maritim Semarang) Kiki Febri Azriati; Abdul Hoyyi; Moch. Abdul Mukid
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Forecasting method Box-Jenkins ARIMA (Autoregressive Integrated Moving Average) is a forecasting method that can provide a more accurate forecasting results. To verify the model obtained using the one Moving Range Chart. The control charts are used to determine the change in the pattern of file seen from the residual value (the difference between the actual file and the file forecasting). File used in this study the average wind speed in the Tanjung Emas harbor during January 2008 to December 2013. The best of Seasonal ARIMA model is ARIMA (0,0,1) (0,0,1) 12. The results of the verification using the Moving Range Control Chart on the model showed that all residual values are within control limits to the length of the shortest interval, means of verification results show that the model is a good model used for forecasting future periods. Forecasting is generated during the period of the next 15 shows the seasonal pattern. This is shown in the figure forecast 2014 average wind speeds are highest in January, as well as forecasting the 2015 figures the average speed of the highest winds also occurred in January. Forecasting results reflect past file, because the actual file used also showed a seasonal pattern with the same seasonal period is annual, where the numbers mean wind speeds are highest in January. Keywords : Seasonal ARIMA, Moving Range Control Chart, Mean wind speeds.

PENERAPAN SEASONAL GENERALIZED SPACE TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (SGSTAR SUR) PADA PERAMALAN HASIL PRODUKSI PADI Leni Pamularsih; Mustafid Mustafid; Abdul Hoyyi
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.v10i2.29435

Ordinary Least Square (OLS) is general method to estimate Generalized Space Time Autoregressive (GSTAR) parameters. Parameter estimation by using OLS for GSTAR model with correlated residuals between equations will produce inefficient estimators. The method that appropriate to estimate the parameter model with correlated residuals between equations is Generalized Least Square (GLS), which is usually used in Seemingly Unrelated Regression (SUR). This research aims to build the seasonal GSTAR SUR model as model of rice yield forecasting in three locations by using the best weighting. Weights used are binary weights, inverse distance and normalization of cross correlation. Data which used in this research are the data of rice yield per quarter in three districts in Central Java, namely Banyumas, Cilacap and Kebumen. The data from the period of January 1981 to December 2014 as training data and the period of January 2015 to December 2018 as validation data. The resulting is a model that has a seasonal effect with the autoregressive order and the spasial order limited to 1 so the model formed is SGSTAR (41)-I(1)(1)3. The best model produced is the SGSTAR SUR (41)-I(1)(1)3 model with inverse distance weighting because it fulfills both assumptions, residuals white noise and residuals normally multivariate distribution. Additionally, it has the smallest MAPE value when compared the other weighting, that is 20%. This MAPE value indicates that the accuracy rate of forecast is accurate.Keywords: Rice yield, Seasonal, GSTAR, SUR.

REGRESI ROBUST ESTIMASI-M DENGAN PEMBOBOT ANDREW, PEMBOBOT RAMSAY DAN PEMBOBOT WELSCH MENGGUNAKAN SOFTWARE R Aulia Desy Deria; Abdul Hoyyi; Mustafid Mustafid
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Robust regression is one of the regression methods that robust from effect of outliers. For the regression with the parameter estimation used Ordinary Least Squares (OLS), outliers can caused assumption violation, so the estimator obtained became bias and inefficient. As a solution, robust regression M-estimation with Andrew, Ramsay and Welsch weight function can be used to overcome the presence of outliers. The aim of this study was to develop a model for case study of poverty in Central Java 2017 influenced by the number of unemployment, population, school participation rate, Human Development Index (HDI), and inflation. The result of estimation using OLS show that there is violation of heteroskedasticity caused by the presence outliers. Applied robust regression to case study proves robust regression can solve outliers and improve parameter estimation. The best robust regression model is robust regression M-estimation with Andrew weight function. The influence value of predictor variables to poverty is 92,7714% and MSE value is 370,8817. Keywords: Outliers, Robust Regression, M-Estimator, Andrew, Ramsay, Welsch

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

Title Search

Found 89 Documents Search Journal : Jurnal Gaussian

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 89 Documents
Search
Journal : Jurnal Gaussian